From dbbc384aace44424f99cd69cff1a8b069ab9ed29 Mon Sep 17 00:00:00 2001 From: Evan Date: Mon, 24 Feb 2020 15:46:46 -0700 Subject: [PATCH 01/33] Convert frame to RGB before processing --- Object_detection_webcam.py | 1 + 1 file changed, 1 insertion(+) diff --git a/Object_detection_webcam.py b/Object_detection_webcam.py index 5767987a..1f49850f 100644 --- a/Object_detection_webcam.py +++ b/Object_detection_webcam.py @@ -95,6 +95,7 @@ # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3] # i.e. a single-column array, where each item in the column has the pixel RGB value ret, frame = video.read() + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame_expanded = np.expand_dims(frame, axis=0) # Perform the actual detection by running the model with the image as input From af20035eec4203c7ded2c34b89917cd9796d6484 Mon Sep 17 00:00:00 2001 From: Evan Date: Mon, 24 Feb 2020 15:47:35 -0700 Subject: [PATCH 02/33] Convert frame to RGB before processing --- Object_detection_webcam.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Object_detection_webcam.py b/Object_detection_webcam.py index 1f49850f..ffbe767d 100644 --- a/Object_detection_webcam.py +++ b/Object_detection_webcam.py @@ -96,7 +96,7 @@ # i.e. a single-column array, where each item in the column has the pixel RGB value ret, frame = video.read() frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - frame_expanded = np.expand_dims(frame, axis=0) + frame_expanded = np.expand_dims(frame_rgb, axis=0) # Perform the actual detection by running the model with the image as input (boxes, scores, classes, num) = sess.run( From d60da0051ba417c5110d318e054444782d278b01 Mon Sep 17 00:00:00 2001 From: Evan Date: Mon, 24 Feb 2020 15:48:54 -0700 Subject: [PATCH 03/33] Convert frame to RGB before processing --- Object_detection_video.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Object_detection_video.py b/Object_detection_video.py index 167f659e..a6ca6d5d 100644 --- a/Object_detection_video.py +++ b/Object_detection_video.py @@ -95,7 +95,8 @@ # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3] # i.e. a single-column array, where each item in the column has the pixel RGB value ret, frame = video.read() - frame_expanded = np.expand_dims(frame, axis=0) + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frame_expanded = np.expand_dims(frame_rgb, axis=0) # Perform the actual detection by running the model with the image as input (boxes, scores, classes, num) = sess.run( From 188ffccb509f7bb6e262f5464f1a2384774fe8a6 Mon Sep 17 00:00:00 2001 From: Evan Date: Mon, 24 Feb 2020 15:49:52 -0700 Subject: [PATCH 04/33] Convert image to RGB before processing --- Object_detection_image.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Object_detection_image.py b/Object_detection_image.py index d5669c0c..577b8c6b 100644 --- a/Object_detection_image.py +++ b/Object_detection_image.py @@ -90,7 +90,8 @@ # expand image dimensions to have shape: [1, None, None, 3] # i.e. a single-column array, where each item in the column has the pixel RGB value image = cv2.imread(PATH_TO_IMAGE) -image_expanded = np.expand_dims(image, axis=0) +image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) +image_expanded = np.expand_dims(image_rgb, axis=0) # Perform the actual detection by running the model with the image as input (boxes, scores, classes, num) = sess.run( From e155eb9aefcf8666c316b007ac4c62b614dfdc7f Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Tue, 17 Mar 2020 20:21:41 +0700 Subject: [PATCH 05/33] Create README_Vietnamese.md --- README_Vietnamese.md | 42 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 README_Vietnamese.md diff --git a/README_Vietnamese.md b/README_Vietnamese.md new file mode 100644 index 00000000..a27242d6 --- /dev/null +++ b/README_Vietnamese.md @@ -0,0 +1,42 @@ +# Cách đào tạo một Object Detection Classifier cho Multiple Objects sử dụng TensorFlow (GPU) trên Window 10 + +## Tóm lược +*Lần cập nhật gần nhất: 6/22/2019 với TensorFlow phiên bản 1.13.1* + +*Một phiên bản tiếng Hàn của hướng dẫn này đã có ở thư mục [translate folder](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10/blob/master/translate/README.md) (thanks @cocopambag!). Nếu bạn muốn đóng góp một bản dịch bằng một ngôn ngữ khác, bạn có thể thêm nó như là một pull request và tôi sẽ merge nó khi có thể* + +Repository này là một hướng dẫn về cách sử dụng TensorFlow's Object Detection API để đào tạo một object detection classsifier cho multiple objects trên Window 10, 8, hoặc 7. (Với vài thay đổi nhỏ, nó cũng hoạt động được trên các hệ điều hành khác dựa trên nhân Linux). Bản gốc được viết dựa trên Tensorflow phiên bản 1.5, tuy nhiên nó vẫn hoạt động trên các phiên bản mới nhất của TensorFlow. + +Tôi cũng làm một YouTube video tóm tắt về hướng dẫn này. Bất kỳ sự khác biệt nào giữa video và bản hướng này này do các bản cập nhật bắt buộc lên các phiên bản mới hơn của TensorFlow. + +**If there are differences between this written tutorial and the video, follow the written tutorial!** + +[![Đường dẫn đến YouTube video!](https://raw.githubusercontent.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10/master/doc/YouTube%20video.jpg)](https://www.youtube.com/watch?v=Rgpfk6eYxJA) + +File readme mô tả tất cả các bước cần thiết để bắt đầu đào tạo một Object detection classifier của riêng bạn: +1. [Cài đặt Anaconda, CUDA, và cuDNN](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#1-install-anaconda-cuda-and-cudnn) +2. [Thiết lập cấu trúc thư mục Object Detection và Anaconda Virtual Environment](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#2-set-up-tensorflow-directory-and-anaconda-virtual-environment) +3. [Thu thập và gán nhãn hình ảnh](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#3-gather-and-label-pictures) +4. [Tạo dữ liệu đào tạo](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#4-generate-training-data) +5. [Tạo một label map và thiết lập trước khi đào tạo](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#5-create-label-map-and-configure-training) +6. [Đào tạo](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#6-run-the-training) +7. [Xuất ra file inference graph](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#7-export-inference-graph) +8. [Sử dụng bộ object detection classifier của bạn vừa huấn luyện để kiểm thử](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#8-use-your-newly-trained-object-detection-classifier) + +[Phụ lục: Những lỗi thường gặp](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#appendix-common-errors) + +Repository này chứa tất cả những file cần thiết để đào tạo một trình phát hiện các quân bài chín, mười, Ri, Q, K trong bộ bài Tây "Pinochle Deck". Hướng dẫn này mô tả cách thay thế các file này bằng các file của bạn để đào tạo một detection classifier cho bất kỳ mục đích nào bạn muốn. Nó cũng chứa các Python scripts để kiểm thử bộ phân loại của bạn cho từng ảnh, video hay từ webcam. + +

+ +

+ +## Introduction +Mục đích của hướng dẫn này là giải thích chi tiết cách đào tạo một mạng nơ-ron tích chập (CNN) của riêng bạn để nhận dạng và phân loại cho nhiều vật thể. Vào cuối hướng dẫn này, bạn sẽ có một chương trình có thể nhận dạng và vẽ khoanh vùng các đối tượng cụ thể trong ảnh, videos, hoặc với đầu vào của webcam. + +Đã có một số hướng dẫn chi tiết về cách sử dụng TensorFlow's Object Detection API để huấn luyện một bộ phân loại cho một đối tượng. Tuy nhiên, hầu hết đều sử dụng hệ điều hành Linux. Nếu bạn giống như tôi, chắc bạn cũng đắn đo một chút khi cài đặt Linux trên PC - gaming với card đồ họa mạnh mẽ mà bạn dùng để đào tạo bộ phân loại. Object Detection API dường như đã được phát triển trên hệ điều hành Linux. Để thiết lập TensorFlow đào tạo một model trên Window, có một số cách giải quyết và cần được sử dụng để thay thế các lệnh đã hoạt động tốt trên Linux. Ngoài ra, hướng dẫn này sẽ giúp bạn đào tạo một bộ phân loại mà có thể nhận diện được nhiều vật thể, chứ không chỉ một. + +Hướng dẫn này dành riêng cho Windows 10, và nó cũng hoạt động trên Windows 7 và 8. Quy trình các bước trong hướng dẫn có thể sử dụng trên Linux, tuy nhiên đường dẫn tệp và lệnh cài đặt các gói sẽ phải thay đổi cho phù hợp. Tôi sử dụng TensorFlow-GPU phiên bản 1.5 trong lúc viết phiên bản đầu tiên của hướng dẫn này, nhưng nó vẫn sẽ hoạt động với các bản cập nhật mới nhất của TensorFlow. + +TensorFlow-GPU allows your PC to use the video card to provide extra processing power while training, so it will be used for this tutorial. In my experience, using TensorFlow-GPU instead of regular TensorFlow reduces training time by a factor of about 8 (3 hours to train instead of 24 hours). The CPU-only version of TensorFlow can also be used for this tutorial, but it will take longer. If you use CPU-only TensorFlow, you do not need to install CUDA and cuDNN in Step 1. +TensorFlow-GPU cho phép PC của bạn sử dụng card đồ họa để tăng sức mạnh xử lý trong quá trình đào tạo, vì vậy nó sẽ được sử dụng trong hướng dẫn này. From 0aa1dc85a4341101d091c377490f892cf01e610c Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Wed, 18 Mar 2020 00:17:06 +0700 Subject: [PATCH 06/33] Update README_Vietnamese.md --- README_Vietnamese.md | 157 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 154 insertions(+), 3 deletions(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index a27242d6..7d401ae1 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -7,7 +7,7 @@ Repository này là một hướng dẫn về cách sử dụng TensorFlow's Object Detection API để đào tạo một object detection classsifier cho multiple objects trên Window 10, 8, hoặc 7. (Với vài thay đổi nhỏ, nó cũng hoạt động được trên các hệ điều hành khác dựa trên nhân Linux). Bản gốc được viết dựa trên Tensorflow phiên bản 1.5, tuy nhiên nó vẫn hoạt động trên các phiên bản mới nhất của TensorFlow. -Tôi cũng làm một YouTube video tóm tắt về hướng dẫn này. Bất kỳ sự khác biệt nào giữa video và bản hướng này này do các bản cập nhật bắt buộc lên các phiên bản mới hơn của TensorFlow. +Tôi đã làm một YouTube video tóm tắt về hướng dẫn này. Bất kỳ sự khác biệt nào giữa video và bản hướng này này do các bản cập nhật bắt buộc lên các phiên bản mới hơn của TensorFlow. **If there are differences between this written tutorial and the video, follow the written tutorial!** @@ -38,5 +38,156 @@ Mục đích của hướng dẫn này là giải thích chi tiết cách đào Hướng dẫn này dành riêng cho Windows 10, và nó cũng hoạt động trên Windows 7 và 8. Quy trình các bước trong hướng dẫn có thể sử dụng trên Linux, tuy nhiên đường dẫn tệp và lệnh cài đặt các gói sẽ phải thay đổi cho phù hợp. Tôi sử dụng TensorFlow-GPU phiên bản 1.5 trong lúc viết phiên bản đầu tiên của hướng dẫn này, nhưng nó vẫn sẽ hoạt động với các bản cập nhật mới nhất của TensorFlow. -TensorFlow-GPU allows your PC to use the video card to provide extra processing power while training, so it will be used for this tutorial. In my experience, using TensorFlow-GPU instead of regular TensorFlow reduces training time by a factor of about 8 (3 hours to train instead of 24 hours). The CPU-only version of TensorFlow can also be used for this tutorial, but it will take longer. If you use CPU-only TensorFlow, you do not need to install CUDA and cuDNN in Step 1. -TensorFlow-GPU cho phép PC của bạn sử dụng card đồ họa để tăng sức mạnh xử lý trong quá trình đào tạo, vì vậy nó sẽ được sử dụng trong hướng dẫn này. +TensorFlow-GPU cho phép PC của bạn sử dụng card đồ họa để tăng sức mạnh xử lý trong quá trình đào tạo, vì vậy nó sẽ được sử dụng trong hướng dẫn này. Với kinh nghiệm của tôi, sử dụng TensorFlow-GPU thay vì TensorFlow thông thường sẽ giảm thời gian đào tạo xuống 8 lần (24 giờ xuống còn 3 giờ). Nếu sử dụng TensorFlow thông thường với CPU cũng có thể làm theo các bước trong hướng dẫn này, nhưng thời gian đào tạo sẽ lâu hơn. Nếu bạn sử dụng TensorFlow chỉ dành cho CPU thì bạn không cần phải cài đặt CUDA và cuDNN trong Bước 1. + +## Các bước thực hiện +### 1. Cài đặt Anaconda, CUDA, và cuDNN +Theo dõi [YouTube video này thực hiện bởi Mark Jay](https://www.youtube.com/watch?v=RplXYjxgZbw), video hướng dẫn cách cài đặt Anaconda, CUDA, và cuDNN. Bạn không cần thực sự cần phải cài đặt TensorFlow như trong video hướng dẫn, vì chúng ta sẽ làm nó sau ở Bước 2. Video trên được làm với TensorFlow-GPU v1.4, vì vậy bạn hãy tải và cài đặt CUDA và cuDNN phiên bản phù hợp với TensorFlow bản mới nhất, thay vì CUDA v8.0 và cuDNN v6.0 như video đề cập. Hãy truy cập [TensorFlow website](https://www.tensorflow.org/install/gpu) để kiểm tra phiên bản nào của CUDA và cuDNN là phù hợp với phiên bản TensorFlow mới nhất. + +Nếu bạn sử dụng một phiên bản cũ hơn của TensorFlow, hãy chắc chắn rằng bạn sử dụng phiên bản của CUDA và cuDNN tương thích với bản TensorFlow bạn đang dùng. [Đây](https://www.tensorflow.org/install/source#tested_build_configurations) là bảo chỉ ra phiên bản của TensorFlow thì phù hợp với phiên bản CUDA và cuDNN tương ứng. + +Hãy đảm bảo [Anaconda](https://www.anaconda.com/distribution/#download-section) được cài đặt theo chỉ dẫn của video, bởi vì Anaconda Virtual Environment sẽ được sử dụng trong suốt hướng dẫn này. (Lưu ý: Phiên bản gần nhất của Anaconda sử dụng Python 3.7, phiên bản này không được hỗ trợ chính thức bởi TensorFlow. Tuy nhiên, khi tạo một Anaconda Virtual Environment trong Bước 2d của hướng dẫn này, chúng ta sẽ hướng nó sử dụng Python 3.5). + +Truy cập [TensorFlow's website](https://www.tensorflow.org/install) để biết thêm về chi tiết cài đặt, bao gồm cả cách cài đặt nó trên các hệ điều hành khác (như Linux). Với [object detection repository](https://github.com/tensorflow/models/tree/master/research/object_detection) bản thân nó cũng có [các hướng dẫn cài đặt](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). + +### 2. Thiết lập Thư mục TensorFlow và Anaconda Virtual Environment +TensorFlow Object Detection API yêu cầu sử dụng cấu trúc thư mục riêng được cung cấp tại GitHub repository. Nó cũng yêu cầu một số Python pakages bổ sung, thêm các biến PATH và PYTHONPATH riêng, và một vài lệnh thiết lập để có thể chạy hoặc đào tạo một Object Detection Model. + +Mục này của hướng dẫn toàn bộ thiết lập cần thiết. Nó khá tỉ mỉ, nhưng hãy làm theo các hướng dẫn một cách chặt chẽ và cẩn thận, bởi vì việc thiết lập sai có thể gây ra các lỗi khó khắc phục. + +#### 2a. Tải TensorFlow Object Detection API repository từ GitHub +Đầu tiên, tạo một thư mục trong ổ C: và đặt tên nó là "tensorflow1". Thư mục này sẽ bao gồm tất cả TensorFlow Object Detection Framework, cũng như các hình ảnh đào tạo, dữ liệu đào tạo, bộ phân loại đã huấn luyện, các tệp cấu hình và tất cả những thứ khác cần thiết cho Object Detection Classifier. + +Tải bản đầy đủ nhất của TensorFlow object detection repository tại https://github.com/tensorflow/models bằng cách nhấn vào nút "Clone or Download" để tải về file zip. Mở file zip vừa tải xuống và giải nén với thư mục "models-master" tới địa chỉ thư mục C:\tensorflow1 mà bạn vừa tạo ở trên. Đổi tên “models-master” thành “models”. + +**Note: Code của TensorFlow models repository (nơi chứa object detection API) được các nhà phát triển cập nhật liên tục. Đôi khi họ thực hiện các thay đổi khiến các phiên bản cũ TensorFlow không còn sử dụng được. Do đó tốt nhất nên sử dụng phiên bản mới nhất của TensorFlow và tải về repository mới nhất. Nếu bạn không sử dụng phiên bản mới nhất, Clone or Download repository thích hợp với phiên bản bạn sử dụng đã được liệt kê như bảng dưới đây.** + + Nếu bạn sử dụng một phiên bản cũ hơn của TensorFlow, đây là bảng cho biết Gihub commit của repository bạn nên sử dụng. Tôi đã tạo nó bằng cách đi đến các branches cho cái model repository và lấy commit trước commit cuối cho branch. (Họ xóa các thư mục nghiên cứu như là commit cuối trước khi phát hành phiên bản mới nhất.) + +| Phiên bản TensorFlow | GitHub Models Repository Commit | +|--------------------|---------------------------------| +|TF v1.7 |https://github.com/tensorflow/models/tree/adfd5a3aca41638aa9fb297c5095f33d64446d8f | +|TF v1.8 |https://github.com/tensorflow/models/tree/abd504235f3c2eed891571d62f0a424e54a2dabc | +|TF v1.9 |https://github.com/tensorflow/models/tree/d530ac540b0103caa194b4824af353f1b073553b | +|TF v1.10 |https://github.com/tensorflow/models/tree/b07b494e3514553633b132178b4c448f994d59df | +|TF v1.11 |https://github.com/tensorflow/models/tree/23b5b4227dfa1b23d7c21f0dfaf0951b16671f43 | +|TF v1.12 |https://github.com/tensorflow/models/tree/r1.12.0 | +|TF v1.13 |https://github.com/tensorflow/models/tree/r1.13.0 | +|Latest version |https://github.com/tensorflow/models | + +Hướng dẫn này ban đầu được hoàn thành sử dụng TensorFlow v1.5 và [GitHub commit](https://github.com/tensorflow/models/tree/079d67d9a0b3407e8d074a200780f3835413ef99) của TensorFlow Object Detection API. Nếu các phần của hướng dẫn này không hoạt động , có thể bạn sẽ cần phải cài đặt TensorFlow v1.5 và tải về chính xác GitHub commit này thay vì phiên bản mới nhất.) + +#### 2b. Tải về Faster-RCNN-Inception-V2-COCO model từ TensorFlow's model zoo +TensorFlow cung cấp một số object detection model (pre-trained classifiers cùng kiến trúc mạng nơ-ron tương ứng) tại đây [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md). Một số model (như SSD-MobileNet model) có kiến trúc cho phép nhận diện nhanh hơn nhưng đánh đổi bằng việc độ chính xác thấp, trong khi một số model (như Faster-RCNN model) nhận diện chậm hơn nhưng độ chính xác tốt hơn. Tôi khởi đầu bằng SSD-MobileNet-V1 model, nhưng nó nhận diện không tốt lắm các thẻ bài có trong ảnh. Tôi huấn luyện lại model với Faster-RCNN-Inception-V2, và nó nhận diện tốt hơn đáng kể, nhưng với tốc độ chậm hơn. + +

+ +

+ +Bạn có thể chọn model phù hợp để đào tạo objection detection classifier của bạn. Nếu bạn muốn sử dụng object detector trên các thiết bị có cấu hình phần cứng hạn chế (như smart phone hay Raspberry Pi), hãy sử dụng SDD-MobileNet model. Nếu bạn chạy model trên các laptop hay PC có cấu hình mạnh, sử dụng RCNN models. + +This tutorial will use the Faster-RCNN-Inception-V2 model. [Download the model here.](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz) Open the downloaded faster_rcnn_inception_v2_coco_2018_01_28.tar.gz file with a file archiver such as WinZip or 7-Zip and extract the faster_rcnn_inception_v2_coco_2018_01_28 folder to the C:\tensorflow1\models\research\object_detection folder. (Note: The model date and version will likely change in the future, but it should still work with this tutorial.) + +#### 2c. Download this tutorial's repository from GitHub +Download the full repository located on this page (scroll to the top and click Clone or Download) and extract all the contents directly into the C:\tensorflow1\models\research\object_detection directory. (You can overwrite the existing "README.md" file.) This establishes a specific directory structure that will be used for the rest of the tutorial. + +At this point, here is what your \object_detection folder should look like: + +

+ +

+ +This repository contains the images, annotation data, .csv files, and TFRecords needed to train a "Pinochle Deck" playing card detector. You can use these images and data to practice making your own Pinochle Card Detector. It also contains Python scripts that are used to generate the training data. It has scripts to test out the object detection classifier on images, videos, or a webcam feed. You can ignore the \doc folder and its files; they are just there to hold the images used for this readme. + +If you want to practice training your own "Pinochle Deck" card detector, you can leave all the files as they are. You can follow along with this tutorial to see how each of the files were generated, and then run the training. You will still need to generate the TFRecord files (train.record and test.record) as described in Step 4. + +You can also download the frozen inference graph for my trained Pinochle Deck card detector [from this Dropbox link](https://www.dropbox.com/s/va9ob6wcucusse1/inference_graph.zip?dl=0) and extract the contents to \object_detection\inference_graph. This inference graph will work "out of the box". You can test it after all the setup instructions in Step 2a - 2f have been completed by running the Object_detection_image.py (or video or webcam) script. + +If you want to train your own object detector, delete the following files (do not delete the folders): +- All files in \object_detection\images\train and \object_detection\images\test +- The “test_labels.csv” and “train_labels.csv” files in \object_detection\images +- All files in \object_detection\training +- All files in \object_detection\inference_graph + +Now, you are ready to start from scratch in training your own object detector. This tutorial will assume that all the files listed above were deleted, and will go on to explain how to generate the files for your own training dataset. + +#### 2d. Set up new Anaconda virtual environment +Next, we'll work on setting up a virtual environment in Anaconda for tensorflow-gpu. From the Start menu in Windows, search for the Anaconda Prompt utility, right click on it, and click “Run as Administrator”. If Windows asks you if you would like to allow it to make changes to your computer, click Yes. + +In the command terminal that pops up, create a new virtual environment called “tensorflow1” by issuing the following command: +``` +C:\> conda create -n tensorflow1 pip python=3.5 +``` +Then, activate the environment and update pip by issuing: +``` +C:\> activate tensorflow1 + +(tensorflow1) C:\>python -m pip install --upgrade pip +``` +Install tensorflow-gpu in this environment by issuing: +``` +(tensorflow1) C:\> pip install --ignore-installed --upgrade tensorflow-gpu +``` + +(Note: You can also use the CPU-only version of TensorFow, but it will run much slower. If you want to use the CPU-only version, just use "tensorflow" instead of "tensorflow-gpu" in the previous command.) + +Install the other necessary packages by issuing the following commands: +``` +(tensorflow1) C:\> conda install -c anaconda protobuf +(tensorflow1) C:\> pip install pillow +(tensorflow1) C:\> pip install lxml +(tensorflow1) C:\> pip install Cython +(tensorflow1) C:\> pip install contextlib2 +(tensorflow1) C:\> pip install jupyter +(tensorflow1) C:\> pip install matplotlib +(tensorflow1) C:\> pip install pandas +(tensorflow1) C:\> pip install opencv-python +``` +(Note: The ‘pandas’ and ‘opencv-python’ packages are not needed by TensorFlow, but they are used in the Python scripts to generate TFRecords and to work with images, videos, and webcam feeds.) + +#### 2e. Configure PYTHONPATH environment variable +A PYTHONPATH variable must be created that points to the \models, \models\research, and \models\research\slim directories. Do this by issuing the following commands (from any directory): +``` +(tensorflow1) C:\> set PYTHONPATH=C:\tensorflow1\models;C:\tensorflow1\models\research;C:\tensorflow1\models\research\slim +``` +(Note: Every time the "tensorflow1" virtual environment is exited, the PYTHONPATH variable is reset and needs to be set up again. You can use "echo %PYTHONPATH% to see if it has been set or not.) + +#### 2f. Compile Protobufs and run setup.py +Next, compile the Protobuf files, which are used by TensorFlow to configure model and training parameters. Unfortunately, the short protoc compilation command posted on TensorFlow’s Object Detection API [installation page](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) does not work on Windows. Every .proto file in the \object_detection\protos directory must be called out individually by the command. + +In the Anaconda Command Prompt, change directories to the \models\research directory: +``` +(tensorflow1) C:\> cd C:\tensorflow1\models\research +``` + +Then copy and paste the following command into the command line and press Enter: +``` +protoc --python_out=. .\object_detection\protos\anchor_generator.proto .\object_detection\protos\argmax_matcher.proto .\object_detection\protos\bipartite_matcher.proto .\object_detection\protos\box_coder.proto .\object_detection\protos\box_predictor.proto .\object_detection\protos\eval.proto .\object_detection\protos\faster_rcnn.proto .\object_detection\protos\faster_rcnn_box_coder.proto .\object_detection\protos\grid_anchor_generator.proto .\object_detection\protos\hyperparams.proto .\object_detection\protos\image_resizer.proto .\object_detection\protos\input_reader.proto .\object_detection\protos\losses.proto .\object_detection\protos\matcher.proto .\object_detection\protos\mean_stddev_box_coder.proto .\object_detection\protos\model.proto .\object_detection\protos\optimizer.proto .\object_detection\protos\pipeline.proto .\object_detection\protos\post_processing.proto .\object_detection\protos\preprocessor.proto .\object_detection\protos\region_similarity_calculator.proto .\object_detection\protos\square_box_coder.proto .\object_detection\protos\ssd.proto .\object_detection\protos\ssd_anchor_generator.proto .\object_detection\protos\string_int_label_map.proto .\object_detection\protos\train.proto .\object_detection\protos\keypoint_box_coder.proto .\object_detection\protos\multiscale_anchor_generator.proto .\object_detection\protos\graph_rewriter.proto .\object_detection\protos\calibration.proto .\object_detection\protos\flexible_grid_anchor_generator.proto +``` +This creates a name_pb2.py file from every name.proto file in the \object_detection\protos folder. + +**(Note: TensorFlow occassionally adds new .proto files to the \protos folder. If you get an error saying ImportError: cannot import name 'something_something_pb2' , you may need to update the protoc command to include the new .proto files.)** + +Finally, run the following commands from the C:\tensorflow1\models\research directory: +``` +(tensorflow1) C:\tensorflow1\models\research> python setup.py build +(tensorflow1) C:\tensorflow1\models\research> python setup.py install +``` + +#### 2g. Test TensorFlow setup to verify it works +The TensorFlow Object Detection API is now all set up to use pre-trained models for object detection, or to train a new one. You can test it out and verify your installation is working by launching the object_detection_tutorial.ipynb script with Jupyter. From the \object_detection directory, issue this command: +``` +(tensorflow1) C:\tensorflow1\models\research\object_detection> jupyter notebook object_detection_tutorial.ipynb +``` +This opens the script in your default web browser and allows you to step through the code one section at a time. You can step through each section by clicking the “Run” button in the upper toolbar. The section is done running when the “In [ * ]” text next to the section populates with a number (e.g. “In [1]”). + +(Note: part of the script downloads the ssd_mobilenet_v1 model from GitHub, which is about 74MB. This means it will take some time to complete the section, so be patient.) + +Once you have stepped all the way through the script, you should see two labeled images at the bottom section the page. If you see this, then everything is working properly! If not, the bottom section will report any errors encountered. See the [Appendix](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#appendix-common-errors) for a list of errors I encountered while setting this up. + +**Note: If you run the full Jupyter Notebook without getting any errors, but the labeled pictures still don't appear, try this: go in to object_detection/utils/visualization_utils.py and comment out the import statements around lines 29 and 30 that include matplotlib. Then, try re-running the Jupyter notebook.** + +

+ +

+ From be82069f6f37fcf5b446c0267fd796403c94c1f0 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Wed, 18 Mar 2020 00:23:59 +0700 Subject: [PATCH 07/33] Update README_Vietnamese.md --- README_Vietnamese.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index 7d401ae1..aae21c11 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -31,7 +31,7 @@ Repository này chứa tất cả những file cần thiết để đào tạo m

-## Introduction +## Giới thiệu Mục đích của hướng dẫn này là giải thích chi tiết cách đào tạo một mạng nơ-ron tích chập (CNN) của riêng bạn để nhận dạng và phân loại cho nhiều vật thể. Vào cuối hướng dẫn này, bạn sẽ có một chương trình có thể nhận dạng và vẽ khoanh vùng các đối tượng cụ thể trong ảnh, videos, hoặc với đầu vào của webcam. Đã có một số hướng dẫn chi tiết về cách sử dụng TensorFlow's Object Detection API để huấn luyện một bộ phân loại cho một đối tượng. Tuy nhiên, hầu hết đều sử dụng hệ điều hành Linux. Nếu bạn giống như tôi, chắc bạn cũng đắn đo một chút khi cài đặt Linux trên PC - gaming với card đồ họa mạnh mẽ mà bạn dùng để đào tạo bộ phân loại. Object Detection API dường như đã được phát triển trên hệ điều hành Linux. Để thiết lập TensorFlow đào tạo một model trên Window, có một số cách giải quyết và cần được sử dụng để thay thế các lệnh đã hoạt động tốt trên Linux. Ngoài ra, hướng dẫn này sẽ giúp bạn đào tạo một bộ phân loại mà có thể nhận diện được nhiều vật thể, chứ không chỉ một. @@ -86,7 +86,7 @@ TensorFlow cung cấp một số object detection model (pre-trained classifiers Bạn có thể chọn model phù hợp để đào tạo objection detection classifier của bạn. Nếu bạn muốn sử dụng object detector trên các thiết bị có cấu hình phần cứng hạn chế (như smart phone hay Raspberry Pi), hãy sử dụng SDD-MobileNet model. Nếu bạn chạy model trên các laptop hay PC có cấu hình mạnh, sử dụng RCNN models. -This tutorial will use the Faster-RCNN-Inception-V2 model. [Download the model here.](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz) Open the downloaded faster_rcnn_inception_v2_coco_2018_01_28.tar.gz file with a file archiver such as WinZip or 7-Zip and extract the faster_rcnn_inception_v2_coco_2018_01_28 folder to the C:\tensorflow1\models\research\object_detection folder. (Note: The model date and version will likely change in the future, but it should still work with this tutorial.) +Hướng dẫn này sẽ sử dụng Faster-RCNN-Inception-V2 model. [Tải về model tại đây](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz) Mở file faster_rcnn_inception_v2_coco_2018_01_28.tar.gz đã tải về và sử dụng WinZip hoặc 7-Zip sau đó giải nén thư mục faster_rcnn_inception_v2_coco_2018_01_28 đến thư mục C:\tensorflow1\models\research\object_detection. (Lưu ý: Phiên bản của model có thể sẽ thay đổi trong tương lai, nhưng nó vẫn sẽ hoạt động theo hướng dẫn này.) #### 2c. Download this tutorial's repository from GitHub Download the full repository located on this page (scroll to the top and click Clone or Download) and extract all the contents directly into the C:\tensorflow1\models\research\object_detection directory. (You can overwrite the existing "README.md" file.) This establishes a specific directory structure that will be used for the rest of the tutorial. From 2b9376ddb7d2c885d5e0dc6092289460d96e6d61 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Wed, 18 Mar 2020 18:41:21 +0700 Subject: [PATCH 08/33] Update README_Vietnamese.md --- README_Vietnamese.md | 72 ++++++++++++++++++++++---------------------- 1 file changed, 36 insertions(+), 36 deletions(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index aae21c11..34747962 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -88,50 +88,50 @@ Bạn có thể chọn model phù hợp để đào tạo objection detection cl Hướng dẫn này sẽ sử dụng Faster-RCNN-Inception-V2 model. [Tải về model tại đây](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz) Mở file faster_rcnn_inception_v2_coco_2018_01_28.tar.gz đã tải về và sử dụng WinZip hoặc 7-Zip sau đó giải nén thư mục faster_rcnn_inception_v2_coco_2018_01_28 đến thư mục C:\tensorflow1\models\research\object_detection. (Lưu ý: Phiên bản của model có thể sẽ thay đổi trong tương lai, nhưng nó vẫn sẽ hoạt động theo hướng dẫn này.) -#### 2c. Download this tutorial's repository from GitHub -Download the full repository located on this page (scroll to the top and click Clone or Download) and extract all the contents directly into the C:\tensorflow1\models\research\object_detection directory. (You can overwrite the existing "README.md" file.) This establishes a specific directory structure that will be used for the rest of the tutorial. +#### 2c. Tải xuống repository của hướng dẫn này từ Github +Tải xuống toàn bộ repository tại trang này (cuộn lên đầu trang và click vào Clone or Download) và giải nén tất cả các file vào thư mục có đường dẫn C:\tensorflow1\models\research\object_detection. (Bạn có thể ghi đè lên file "README.md" hiện có.) Vậy là chúng ta đã hoàn thành việc thiết lập thư mục để sử dụng trong suốt hướng dẫn này. -At this point, here is what your \object_detection folder should look like: +Tại đây, cấu trúc các file trong thư mục \object_detection sẽ trông như thế này:

-This repository contains the images, annotation data, .csv files, and TFRecords needed to train a "Pinochle Deck" playing card detector. You can use these images and data to practice making your own Pinochle Card Detector. It also contains Python scripts that are used to generate the training data. It has scripts to test out the object detection classifier on images, videos, or a webcam feed. You can ignore the \doc folder and its files; they are just there to hold the images used for this readme. +Repository bao gồm images, annotation data, .csv files và TFRecords cần thiết để đào tạo một trình nhận diện quân bài "Pinochle Deck". Bạn có thể sử dụng hình ảnh và dữ diệu này để luyện tập và tạo ra một trình nhận diện quân bài của riêng bạn. Nó bao gồm Python scripts thứ sẽ được sử dụng để tạo ra training data. Nó cũng có các scripts để kiểm thử object detection classifier trên hình ảnh, video hoặc đầu vào từ webcam. Bạn có thể bỏ qua thư mục \doc và các file bên trong, vì đây chỉ là nơi giữ các hình ảnh cho file readme này. -If you want to practice training your own "Pinochle Deck" card detector, you can leave all the files as they are. You can follow along with this tutorial to see how each of the files were generated, and then run the training. You will still need to generate the TFRecord files (train.record and test.record) as described in Step 4. +Nếu bạn muốn luyện tập đào tạo một trình nhận diện quân bài "Pinochle Deck" của bạn, hãy giữ nguyên các file hiện có. Bạn có thể theo tiếp hướng dẫn này để biết làm cách nào mà các file được tạo ra, cách để bắt đầu đào tạo. Bạn sẽ cần tạo ra các file TFRecord (train.record và test.record) như mô tả ở Bước 4. -You can also download the frozen inference graph for my trained Pinochle Deck card detector [from this Dropbox link](https://www.dropbox.com/s/va9ob6wcucusse1/inference_graph.zip?dl=0) and extract the contents to \object_detection\inference_graph. This inference graph will work "out of the box". You can test it after all the setup instructions in Step 2a - 2f have been completed by running the Object_detection_image.py (or video or webcam) script. +Bạn có thể tải về các inference graph về nhận diện quân bài Pinochle Deck đã được huấn luyện sẵn của tôi [from this Dropbox link](https://www.dropbox.com/s/va9ob6wcucusse1/inference_graph.zip?dl=0) và giải nén các file bên trong đến \object_detection\inference_graph. Đây là inference graph đã hoạt động tốt. Bạn có thể test no sau khi đã hoàn thành các thiết lập tại Bước 2a - 2f và chạy file Object_detection_image.py (hoặc video hoặc webcam). -If you want to train your own object detector, delete the following files (do not delete the folders): -- All files in \object_detection\images\train and \object_detection\images\test -- The “test_labels.csv” and “train_labels.csv” files in \object_detection\images -- All files in \object_detection\training -- All files in \object_detection\inference_graph +Nếu bạn muốn tự đào tạo một object detector, hãy tiến hành xóa các file được liệt kê dưới đây (không xóa thư mục): +- Tất cả các file trong \object_detection\images\train and \object_detection\images\test +- File “test_labels.csv” và “train_labels.csv” files trong thư mục \object_detection\images +- Tất cả các file trong \object_detection\training +- Tất cả các file trong \object_detection\inference_graph -Now, you are ready to start from scratch in training your own object detector. This tutorial will assume that all the files listed above were deleted, and will go on to explain how to generate the files for your own training dataset. +Bây giờ, bạn đã sẵn sàng để bắt đầu huấn luyện object detector của bạn. Đảm bảo rằng các bước ở trên đã hoàn thành và các file liệt kê ở trên đã được xóa, sau đây tôi sẽ tiếp tục giải thích các tạo ra những file cho bộ dữ liệu đào tạo của riêng bạn. -#### 2d. Set up new Anaconda virtual environment -Next, we'll work on setting up a virtual environment in Anaconda for tensorflow-gpu. From the Start menu in Windows, search for the Anaconda Prompt utility, right click on it, and click “Run as Administrator”. If Windows asks you if you would like to allow it to make changes to your computer, click Yes. +#### 2d. Thiết lập một Anaconda virtual environment mới +Tiếp theo, chúng ta sẽ tiến hành thiết lập một Anaconda virtual environment cho tensorflow-gpu. Từ menu Start của Windows, tìm kiếm Anaconda Prompt, click vào nó, và chọn “Run as Administrator”. Nếu Windows hỏi bạn có đồng ý cho chạy với quyền Admin không thì hãy click vào Yes. -In the command terminal that pops up, create a new virtual environment called “tensorflow1” by issuing the following command: +Tại cửa sổ terminal vừa xuất hiện, tạo một virtual environment đặt tên là “tensorflow1” bằng cách sử dụng lệnh dưới đây: ``` C:\> conda create -n tensorflow1 pip python=3.5 ``` -Then, activate the environment and update pip by issuing: +Sau đó, kích hoạt môi trường và cập nhật pip bằng lệnh: ``` C:\> activate tensorflow1 (tensorflow1) C:\>python -m pip install --upgrade pip ``` -Install tensorflow-gpu in this environment by issuing: +Cài đặt tensorflow-gpu tại môi trường này bằng lệnh: ``` (tensorflow1) C:\> pip install --ignore-installed --upgrade tensorflow-gpu ``` -(Note: You can also use the CPU-only version of TensorFow, but it will run much slower. If you want to use the CPU-only version, just use "tensorflow" instead of "tensorflow-gpu" in the previous command.) +(Lưu ý: Bạn có thể chỉ sử dụng phiên bản TensorFlow dành cho CPU, nhưng nó sẽ chạy chậm hơn nhiều. Nếu bạn muốn sử dụng phiên bản, thay thế "tensorflow" bởi "tensorflow-gpu" ở lệnh cài đặt phía trên.) -Install the other necessary packages by issuing the following commands: +Cài đặt các packages cần thiết khác bằng các lệnh dưới đây: ``` (tensorflow1) C:\> conda install -c anaconda protobuf (tensorflow1) C:\> pip install pillow @@ -143,49 +143,49 @@ Install the other necessary packages by issuing the following commands: (tensorflow1) C:\> pip install pandas (tensorflow1) C:\> pip install opencv-python ``` -(Note: The ‘pandas’ and ‘opencv-python’ packages are not needed by TensorFlow, but they are used in the Python scripts to generate TFRecords and to work with images, videos, and webcam feeds.) +(Lưu ý: Thư viện ‘pandas’ và ‘opencv-python’ không cần thiết cho TensorFlow, nhưng chúng ta sẽ cần nó cho các Python scripts để tạo ra file TFRecords và làm việc với hình ảnh, video và đầu vào từ webcam.) -#### 2e. Configure PYTHONPATH environment variable -A PYTHONPATH variable must be created that points to the \models, \models\research, and \models\research\slim directories. Do this by issuing the following commands (from any directory): +#### 2e. Cấu hình đường dẫn PYTHONPATH cho môi trường +Một đường dẫn PYTHONPATH thì cần phải được cấu hình như \models, \models\research, và \models\research\slim directories. Cấu hình nó bằng cách chạy lệnh dưới đây (từ bất kỳ thư mục nào): ``` (tensorflow1) C:\> set PYTHONPATH=C:\tensorflow1\models;C:\tensorflow1\models\research;C:\tensorflow1\models\research\slim ``` -(Note: Every time the "tensorflow1" virtual environment is exited, the PYTHONPATH variable is reset and needs to be set up again. You can use "echo %PYTHONPATH% to see if it has been set or not.) +(Lưu ý: Mỗi khi "tensorflow1" virtual environment bị tắt, thì biến PYTHONPATH cần phải được thiết lập lại. Bạn có thể chạy "echo %PYTHONPATH% để xem nó có cần thiết lập hay không.) -#### 2f. Compile Protobufs and run setup.py -Next, compile the Protobuf files, which are used by TensorFlow to configure model and training parameters. Unfortunately, the short protoc compilation command posted on TensorFlow’s Object Detection API [installation page](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) does not work on Windows. Every .proto file in the \object_detection\protos directory must be called out individually by the command. +#### 2f. Biên dịch Protobufs và chạy setup.py +Tiếp theo, biên dịch các file Protobuf, cái mà được sử dụng bởi TensorFlow để cấu hình model và các tham số huấn luyện. Không may rằng, lệnh biên dịch protoc ngắn gọn được đăng tại TensorFlow’s Object Detection API [installation page](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) không hoạt động trên Windows. Mỗi file .proto trong \object_detection\protos cần được biên dịch thủ công riêng bởi lệnh. -In the Anaconda Command Prompt, change directories to the \models\research directory: +Tại Anaconda Command Prompt, đổi đường dẫn thư mục tới thư mục \models\research: ``` (tensorflow1) C:\> cd C:\tensorflow1\models\research ``` -Then copy and paste the following command into the command line and press Enter: +Sau đó copy và paste các lệnh dưới đây vào command line sau đó nhấn Enter: ``` protoc --python_out=. .\object_detection\protos\anchor_generator.proto .\object_detection\protos\argmax_matcher.proto .\object_detection\protos\bipartite_matcher.proto .\object_detection\protos\box_coder.proto .\object_detection\protos\box_predictor.proto .\object_detection\protos\eval.proto .\object_detection\protos\faster_rcnn.proto .\object_detection\protos\faster_rcnn_box_coder.proto .\object_detection\protos\grid_anchor_generator.proto .\object_detection\protos\hyperparams.proto .\object_detection\protos\image_resizer.proto .\object_detection\protos\input_reader.proto .\object_detection\protos\losses.proto .\object_detection\protos\matcher.proto .\object_detection\protos\mean_stddev_box_coder.proto .\object_detection\protos\model.proto .\object_detection\protos\optimizer.proto .\object_detection\protos\pipeline.proto .\object_detection\protos\post_processing.proto .\object_detection\protos\preprocessor.proto .\object_detection\protos\region_similarity_calculator.proto .\object_detection\protos\square_box_coder.proto .\object_detection\protos\ssd.proto .\object_detection\protos\ssd_anchor_generator.proto .\object_detection\protos\string_int_label_map.proto .\object_detection\protos\train.proto .\object_detection\protos\keypoint_box_coder.proto .\object_detection\protos\multiscale_anchor_generator.proto .\object_detection\protos\graph_rewriter.proto .\object_detection\protos\calibration.proto .\object_detection\protos\flexible_grid_anchor_generator.proto ``` -This creates a name_pb2.py file from every name.proto file in the \object_detection\protos folder. +Việc này tạo một file name_pb2.pt từ mỗi file name.proto trong thư mục \object_detection\protos. -**(Note: TensorFlow occassionally adds new .proto files to the \protos folder. If you get an error saying ImportError: cannot import name 'something_something_pb2' , you may need to update the protoc command to include the new .proto files.)** +**(Lưu ý: TensorFlow thỉnh thoảng cập nhật các file .proto mới tại thư mục \protos. Nếu bạn gặp phải lỗi về ImportError: cannot import name 'something_something_pb2' , bạn có thể cần phải cập nhật các lệnh proto để thêm vào các file .proto mới.)** -Finally, run the following commands from the C:\tensorflow1\models\research directory: +Cuối cùng, chạy các lệnh sau từ thư mục C:\tensorflow1\models\research: ``` (tensorflow1) C:\tensorflow1\models\research> python setup.py build (tensorflow1) C:\tensorflow1\models\research> python setup.py install ``` -#### 2g. Test TensorFlow setup to verify it works -The TensorFlow Object Detection API is now all set up to use pre-trained models for object detection, or to train a new one. You can test it out and verify your installation is working by launching the object_detection_tutorial.ipynb script with Jupyter. From the \object_detection directory, issue this command: +#### 2g. Kiểm tra thiết lập TensorFlow để đảm bảo nó hoạt động +Phía trên là đầy đủ các cài đặt về TensorFlow Object Detection API để object detection, hoặc đào tạo một model mới. Bạn có thể kiểm thử nó để đảm bảo rằng các cài đặt hoạt động bằng cách chạy Jupyter notebook object_detection_tutorial.ipynb. Từ thư mục \object_detection, chạy lệnh sau: ``` (tensorflow1) C:\tensorflow1\models\research\object_detection> jupyter notebook object_detection_tutorial.ipynb ``` -This opens the script in your default web browser and allows you to step through the code one section at a time. You can step through each section by clicking the “Run” button in the upper toolbar. The section is done running when the “In [ * ]” text next to the section populates with a number (e.g. “In [1]”). +Lệnh này sẽ mở notebook tại một cửa sổ mới trên trình duyệt web của bạn và cho phép bạn chạy từng lệnh một trong notebook. Bạn có thể chuyển tiếp giữa các cell code bằng việc click vào nút "Run" tại thanh công cụ phía trên. Hoặc bạn có thể chọn "Run All" đê chạy tất cả các cell code trong notebook. -(Note: part of the script downloads the ssd_mobilenet_v1 model from GitHub, which is about 74MB. This means it will take some time to complete the section, so be patient.) +(Lưu ý: có đoạn tải về ssd_mobilenet_v1 model từ GitHub, khoảng 74MB. Nên nó sẽ mất một chút thời gian để hoàn thành, hãy kiên nhẫn đợi) -Once you have stepped all the way through the script, you should see two labeled images at the bottom section the page. If you see this, then everything is working properly! If not, the bottom section will report any errors encountered. See the [Appendix](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#appendix-common-errors) for a list of errors I encountered while setting this up. +Khi bạn đã chạy tất cả script trong notebook, bạn sẽ thấy hai hình ảnh đã được gán nhãn ở phần dưới cùng của trang. Nếu bạn thấy nó, tất cả đã hoạt động tốt! Nếu không, phần dưới cùng sẽ báo ra một số lỗi mà có thể gặp phải. Xem tại [Appendix](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#appendix-common-errors) là một loạt các lỗi mà tôi gặp phải trong quá trình thiết lập. -**Note: If you run the full Jupyter Notebook without getting any errors, but the labeled pictures still don't appear, try this: go in to object_detection/utils/visualization_utils.py and comment out the import statements around lines 29 and 30 that include matplotlib. Then, try re-running the Jupyter notebook.** +**Lưu ý: Nếu bạn chạy toàn bộ Jupyter Notebook mà không gặp lỗi nào, nhưng hình ảnh có gán nhãn không hiện lên, thử cái này: đi đến thư mục object_detection/utils/visualization_utils.py và comment lại các dòng 29 và 30 nơi chứa matplotlib. Sau đó, chạy lại toàn bộ Jupyter notebook.**

From c62c1a241664be365116740a8e6bc275221877a3 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Wed, 18 Mar 2020 18:42:41 +0700 Subject: [PATCH 09/33] Update README_Vietnamese.md --- README_Vietnamese.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index 34747962..7c1566ea 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -3,7 +3,7 @@ ## Tóm lược *Lần cập nhật gần nhất: 6/22/2019 với TensorFlow phiên bản 1.13.1* -*Một phiên bản tiếng Hàn của hướng dẫn này đã có ở thư mục [translate folder](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10/blob/master/translate/README.md) (thanks @cocopambag!). Nếu bạn muốn đóng góp một bản dịch bằng một ngôn ngữ khác, bạn có thể thêm nó như là một pull request và tôi sẽ merge nó khi có thể* +*Một phiên bản tiếng Hàn và tiếng Việt của hướng dẫn này đã có ở thư mục [translate folder](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10/blob/master/translate/README.md) (thanks @cocopambag and @[winter2897](https://github.com/winter2897)). Nếu bạn muốn đóng góp một bản dịch bằng một ngôn ngữ khác, bạn có thể thêm nó như là một pull request và tôi sẽ merge nó khi có thể* Repository này là một hướng dẫn về cách sử dụng TensorFlow's Object Detection API để đào tạo một object detection classsifier cho multiple objects trên Window 10, 8, hoặc 7. (Với vài thay đổi nhỏ, nó cũng hoạt động được trên các hệ điều hành khác dựa trên nhân Linux). Bản gốc được viết dựa trên Tensorflow phiên bản 1.5, tuy nhiên nó vẫn hoạt động trên các phiên bản mới nhất của TensorFlow. From d87d97a1e325b96efca60d4fb42215f52aa0e545 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Wed, 18 Mar 2020 19:37:15 +0700 Subject: [PATCH 10/33] Update Readme --- README_Vietnamese.md | 281 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 281 insertions(+) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index 7c1566ea..923048e3 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -191,3 +191,284 @@ Khi bạn đã chạy tất cả script trong notebook, bạn sẽ thấy hai h

+### 3. Thu thập và gán nhãn hình ảnh +Sau khi TensorFlow Object Detection API đã thiết lập thành công và sẵn sàng chạy, chúng ta cần cung cần cung cấp các hình ảnh mà sẽ dùng để đào tạo một detection classifier mới. + +#### 3a. Thu thập hình ảnh +TensorFlow cần lượng lớn hình ảnh của một object để có thể đào tạo một detection classifier model tốt. Để đào tạo một bộ phân loại mạnh mẽ, các hình ảnh đào tạo cần có ngẫu nhiên các objects trong ảnh cùng với các objects mong muốn, và nên có đa dạng về nền và điều kiện ánh sáng khác nhau. Cần có một số hình ảnh mà trong đó object mong muốn bị che khuất một phần, chồng chéo với một thứ khác, hoặc chỉ ở giữa bức ảnh. + +Với Pinochle Card Detection classifier của tôi, tôi cso 6 objects khác nhau mà tôi muốn phát hiện (Thứ tự các quân bài chín, mười Ri, Q, K và Xì - Đây chỉ là thứ tự, tôi không nhận diện quân Xì). Tôi đã sử dụng iPhone để chụp 40 bức ảnh, với nhiều các vật thể không muốn cũng có trong ảnh. Sau đó, tôi chụp khoảng 100 bức khác cùng với đa dạng các quân bài cùng trong một bức ảnh. Tôi muốn phát hiện các thể khi chúng trùng lên nhau, vì vậy tôi chắc chắn rằng các thẻ có thể chồng lên nhau trong nhiều hình ảnh. + +

+ +

+ +Bạn có thể sử dụng điện thoại của bạn để chụp các hình ảnh về vật thể hoặc bạn có thể tải chúng từ Google Image Search. Tôi khuyến nghị bạn nên có ít nhất 200 ảnh tất cả. Tôi sử dụng 311 ảnh để đào tạo bộ phát hiện các quân bài của mình. + +Đảm bảo rằng các hình ảnh không quá lớn. Chúng nên nhỏ hơn 200KB mỗi bức, và độ phân giải của chúng không lớn hơn 720x1280. Hình ảnh càng lớn thì càng mất nhiều thời gian để đào tạo. Bạn có thể sử dụng resizer.py script trong repository để giảm kích thước của các hình ảnh xuống. + +Sau khi bạn có tất cả các hình ảnh bạn cần, chia chúng ra 20% đến thư mục \object_detection\images\test, và 80% còn lại vào thư mục \object_detection\images\train. Đảm bảo rằng có đa dạng hình ảnh trong cả hai thư mục \test và \train. + +#### 3b. Label Pictures +Here comes the fun part! With all the pictures gathered, it’s time to label the desired objects in every picture. LabelImg is a great tool for labeling images, and its GitHub page has very clear instructions on how to install and use it. + +[LabelImg GitHub link](https://github.com/tzutalin/labelImg) + +[LabelImg download link](https://www.dropbox.com/s/tq7zfrcwl44vxan/windows_v1.6.0.zip?dl=1) + +Download and install LabelImg, point it to your \images\train directory, and then draw a box around each object in each image. Repeat the process for all the images in the \images\test directory. This will take a while! + +

+ +

+ +LabelImg saves a .xml file containing the label data for each image. These .xml files will be used to generate TFRecords, which are one of the inputs to the TensorFlow trainer. Once you have labeled and saved each image, there will be one .xml file for each image in the \test and \train directories. + +### 4. Generate Training Data +With the images labeled, it’s time to generate the TFRecords that serve as input data to the TensorFlow training model. This tutorial uses the xml_to_csv.py and generate_tfrecord.py scripts from [Dat Tran’s Raccoon Detector dataset](https://github.com/datitran/raccoon_dataset), with some slight modifications to work with our directory structure. + +First, the image .xml data will be used to create .csv files containing all the data for the train and test images. From the \object_detection folder, issue the following command in the Anaconda command prompt: +``` +(tensorflow1) C:\tensorflow1\models\research\object_detection> python xml_to_csv.py +``` +This creates a train_labels.csv and test_labels.csv file in the \object_detection\images folder. + +Next, open the generate_tfrecord.py file in a text editor. Replace the label map starting at line 31 with your own label map, where each object is assigned an ID number. This same number assignment will be used when configuring the labelmap.pbtxt file in Step 5b. + +For example, say you are training a classifier to detect basketballs, shirts, and shoes. You will replace the following code in generate_tfrecord.py: +``` +# TO-DO replace this with label map +def class_text_to_int(row_label): + if row_label == 'nine': + return 1 + elif row_label == 'ten': + return 2 + elif row_label == 'jack': + return 3 + elif row_label == 'queen': + return 4 + elif row_label == 'king': + return 5 + elif row_label == 'ace': + return 6 + else: + None +``` +With this: +``` +# TO-DO replace this with label map +def class_text_to_int(row_label): + if row_label == 'basketball': + return 1 + elif row_label == 'shirt': + return 2 + elif row_label == 'shoe': + return 3 + else: + None +``` +Then, generate the TFRecord files by issuing these commands from the \object_detection folder: +``` +python generate_tfrecord.py --csv_input=images\train_labels.csv --image_dir=images\train --output_path=train.record +python generate_tfrecord.py --csv_input=images\test_labels.csv --image_dir=images\test --output_path=test.record +``` +These generate a train.record and a test.record file in \object_detection. These will be used to train the new object detection classifier. + +### 5. Create Label Map and Configure Training +The last thing to do before training is to create a label map and edit the training configuration file. + +#### 5a. Label map +The label map tells the trainer what each object is by defining a mapping of class names to class ID numbers. Use a text editor to create a new file and save it as labelmap.pbtxt in the C:\tensorflow1\models\research\object_detection\training folder. (Make sure the file type is .pbtxt, not .txt !) In the text editor, copy or type in the label map in the format below (the example below is the label map for my Pinochle Deck Card Detector): +``` +item { + id: 1 + name: 'nine' +} + +item { + id: 2 + name: 'ten' +} + +item { + id: 3 + name: 'jack' +} + +item { + id: 4 + name: 'queen' +} + +item { + id: 5 + name: 'king' +} + +item { + id: 6 + name: 'ace' +} +``` +The label map ID numbers should be the same as what is defined in the generate_tfrecord.py file. For the basketball, shirt, and shoe detector example mentioned in Step 4, the labelmap.pbtxt file will look like: +``` +item { + id: 1 + name: 'basketball' +} + +item { + id: 2 + name: 'shirt' +} + +item { + id: 3 + name: 'shoe' +} +``` + +#### 5b. Configure training +Finally, the object detection training pipeline must be configured. It defines which model and what parameters will be used for training. This is the last step before running training! + +Navigate to C:\tensorflow1\models\research\object_detection\samples\configs and copy the faster_rcnn_inception_v2_pets.config file into the \object_detection\training directory. Then, open the file with a text editor. There are several changes to make to the .config file, mainly changing the number of classes and examples, and adding the file paths to the training data. + +Make the following changes to the faster_rcnn_inception_v2_pets.config file. Note: The paths must be entered with single forward slashes (NOT backslashes), or TensorFlow will give a file path error when trying to train the model! Also, the paths must be in double quotation marks ( " ), not single quotation marks ( ' ). + +- Line 9. Change num_classes to the number of different objects you want the classifier to detect. For the above basketball, shirt, and shoe detector, it would be num_classes : 3 . +- Line 106. Change fine_tune_checkpoint to: + - fine_tune_checkpoint : "C:/tensorflow1/models/research/object_detection/faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt" + +- Lines 123 and 125. In the train_input_reader section, change input_path and label_map_path to: + - input_path : "C:/tensorflow1/models/research/object_detection/train.record" + - label_map_path: "C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt" + +- Line 130. Change num_examples to the number of images you have in the \images\test directory. + +- Lines 135 and 137. In the eval_input_reader section, change input_path and label_map_path to: + - input_path : "C:/tensorflow1/models/research/object_detection/test.record" + - label_map_path: "C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt" + +Save the file after the changes have been made. That’s it! The training job is all configured and ready to go! + +### 6. Run the Training +**UPDATE 9/26/18:** +*As of version 1.9, TensorFlow has deprecated the "train.py" file and replaced it with "model_main.py" file. I haven't been able to get model_main.py to work correctly yet (I run in to errors related to pycocotools). Fortunately, the train.py file is still available in the /object_detection/legacy folder. Simply move train.py from /object_detection/legacy into the /object_detection folder and then continue following the steps below.* + +Here we go! From the \object_detection directory, issue the following command to begin training: +``` +python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config +``` +If everything has been set up correctly, TensorFlow will initialize the training. The initialization can take up to 30 seconds before the actual training begins. When training begins, it will look like this: + +

+ +

+ +Each step of training reports the loss. It will start high and get lower and lower as training progresses. For my training on the Faster-RCNN-Inception-V2 model, it started at about 3.0 and quickly dropped below 0.8. I recommend allowing your model to train until the loss consistently drops below 0.05, which will take about 40,000 steps, or about 2 hours (depending on how powerful your CPU and GPU are). Note: The loss numbers will be different if a different model is used. MobileNet-SSD starts with a loss of about 20, and should be trained until the loss is consistently under 2. + +You can view the progress of the training job by using TensorBoard. To do this, open a new instance of Anaconda Prompt, activate the tensorflow1 virtual environment, change to the C:\tensorflow1\models\research\object_detection directory, and issue the following command: +``` +(tensorflow1) C:\tensorflow1\models\research\object_detection>tensorboard --logdir=training +``` +This will create a webpage on your local machine at YourPCName:6006, which can be viewed through a web browser. The TensorBoard page provides information and graphs that show how the training is progressing. One important graph is the Loss graph, which shows the overall loss of the classifier over time. + +

+ +

+ +The training routine periodically saves checkpoints about every five minutes. You can terminate the training by pressing Ctrl+C while in the command prompt window. I typically wait until just after a checkpoint has been saved to terminate the training. You can terminate training and start it later, and it will restart from the last saved checkpoint. The checkpoint at the highest number of steps will be used to generate the frozen inference graph. + +### 7. Export Inference Graph +Now that training is complete, the last step is to generate the frozen inference graph (.pb file). From the \object_detection folder, issue the following command, where “XXXX” in “model.ckpt-XXXX” should be replaced with the highest-numbered .ckpt file in the training folder: +``` +python export_inference_graph.py --input_type image_tensor --pipeline_config_path training/faster_rcnn_inception_v2_pets.config --trained_checkpoint_prefix training/model.ckpt-XXXX --output_directory inference_graph +``` +This creates a frozen_inference_graph.pb file in the \object_detection\inference_graph folder. The .pb file contains the object detection classifier. + +### 8. Use Your Newly Trained Object Detection Classifier! +The object detection classifier is all ready to go! I’ve written Python scripts to test it out on an image, video, or webcam feed. + +Before running the Python scripts, you need to modify the NUM_CLASSES variable in the script to equal the number of classes you want to detect. (For my Pinochle Card Detector, there are six cards I want to detect, so NUM_CLASSES = 6.) + +To test your object detector, move a picture of the object or objects into the \object_detection folder, and change the IMAGE_NAME variable in the Object_detection_image.py to match the file name of the picture. Alternatively, you can use a video of the objects (using Object_detection_video.py), or just plug in a USB webcam and point it at the objects (using Object_detection_webcam.py). + +To run any of the scripts, type “idle” in the Anaconda Command Prompt (with the “tensorflow1” virtual environment activated) and press ENTER. This will open IDLE, and from there, you can open any of the scripts and run them. + +If everything is working properly, the object detector will initialize for about 10 seconds and then display a window showing any objects it’s detected in the image! + +

+ +

+ +If you encounter errors, please check out the Appendix: it has a list of errors that I ran in to while setting up my object detection classifier. You can also trying Googling the error. There is usually useful information on Stack Exchange or in TensorFlow’s Issues on GitHub. + +## Appendix: Common Errors +It appears that the TensorFlow Object Detection API was developed on a Linux-based operating system, and most of the directions given by the documentation are for a Linux OS. Trying to get a Linux-developed software library to work on Windows can be challenging. There are many little snags that I ran in to while trying to set up tensorflow-gpu to train an object detection classifier on Windows 10. This Appendix is a list of errors I ran in to, and their resolutions. + +#### 1. ModuleNotFoundError: No module named 'deployment' or No module named 'nets' + +This error occurs when you try to run object_detection_tutorial.ipynb or train.py and you don’t have the PATH and PYTHONPATH environment variables set up correctly. Exit the virtual environment by closing and re-opening the Anaconda Prompt window. Then, issue “activate tensorflow1” to re-enter the environment, and then issue the commands given in Step 2e. + +You can use “echo %PATH%” and “echo %PYTHONPATH%” to check the environment variables and make sure they are set up correctly. + +Also, make sure you have run these commands from the \models\research directory: +``` +setup.py build +setup.py install +``` + +#### 2. ImportError: cannot import name 'preprocessor_pb2' + +#### ImportError: cannot import name 'string_int_label_map_pb2' + +#### (or similar errors with other pb2 files) + +This occurs when the protobuf files (in this case, preprocessor.proto) have not been compiled. Re-run the protoc command given in Step 2f. Check the \object_detection\protos folder to make sure there is a name_pb2.py file for every name.proto file. + +#### 3. object_detection/protos/.proto: No such file or directory + +This occurs when you try to run the +``` +“protoc object_detection/protos/*.proto --python_out=.” +``` +command given on the TensorFlow Object Detection API installation page. Sorry, it doesn’t work on Windows! Copy and paste the full command given in Step 2f instead. There’s probably a more graceful way to do it, but I don’t know what it is. + +#### 4. Unsuccessful TensorSliceReader constructor: Failed to get "file path" … The filename, directory name, or volume label syntax is incorrect. + +This error occurs when the filepaths in the training configuration file (faster_rcnn_inception_v2_pets.config or similar) have not been entered with backslashes instead of forward slashes. Open the .config file and make sure all file paths are given in the following format: +``` +“C:/path/to/model.file” +``` + +#### 5. ValueError: Tried to convert 't' to a tensor and failed. Error: Argument must be a dense tensor: range(0, 3) - got shape [3], but wanted []. + +The issue is with models/research/object_detection/utils/learning_schedules.py Currently it is +``` +rate_index = tf.reduce_max(tf.where(tf.greater_equal(global_step, boundaries), + range(num_boundaries), + [0] * num_boundaries)) +``` +Wrap list() around the range() like this: + +``` +rate_index = tf.reduce_max(tf.where(tf.greater_equal(global_step, boundaries), + list(range(num_boundaries)), + [0] * num_boundaries)) +``` + +[Ref: Tensorflow Issue#3705](https://github.com/tensorflow/models/issues/3705#issuecomment-375563179) + +#### 6. ImportError: DLL load failed: The specified procedure could not be found. (or other DLL-related errors) +This error occurs because the CUDA and cuDNN versions you have installed are not compatible with the version of TensorFlow you are using. The easiest way to resolve this error is to use Anaconda's cudatoolkit package rather than manually installing CUDA and cuDNN. If you ran into these errors, try creating a new Anaconda virtual environment: +``` +conda create -n tensorflow2 pip python=3.5 +``` +Then, once inside the environment, install TensorFlow using CONDA rather than PIP: +``` +conda install tensorflow-gpu +``` +Then restart this guide from Step 2 (but you can skip the part where you install TensorFlow in Step 2d). + +#### 7. In Step 2g, the Jupyter Notebook runs all the way through with no errors, but no pictures are displayed at the end. +If you run the full Jupyter Notebook without getting any errors, but the labeled pictures still don't appear, try this: go in to object_detection/utils/visualization_utils.py and comment out the import statements around lines 29 and 30 that include matplotlib. Then, try re-running the Jupyter notebook. (The visualization_utils.py script changes quite a bit, so it might not be exactly line 29 and 30.) From 763267435530953c881072cccdc5c2328f29e683 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Wed, 18 Mar 2020 19:38:20 +0700 Subject: [PATCH 11/33] Update README_Vietnamese.md --- README_Vietnamese.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index 923048e3..f7a1703c 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -3,7 +3,7 @@ ## Tóm lược *Lần cập nhật gần nhất: 6/22/2019 với TensorFlow phiên bản 1.13.1* -*Một phiên bản tiếng Hàn và tiếng Việt của hướng dẫn này đã có ở thư mục [translate folder](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10/blob/master/translate/README.md) (thanks @cocopambag and @[winter2897](https://github.com/winter2897)). Nếu bạn muốn đóng góp một bản dịch bằng một ngôn ngữ khác, bạn có thể thêm nó như là một pull request và tôi sẽ merge nó khi có thể* +*Một phiên bản tiếng Hàn và tiếng Việt của hướng dẫn này đã có ở thư mục [translate folder](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10/blob/master/translate/README.md) (thanks @cocopambag and @[winter2897](https://github.com/winter2897)). Nếu bạn muốn đóng góp một bản dịch bằng một ngôn ngữ khác, bạn có thể thêm nó như là một pull request và tôi sẽ merge nó khi có thể.* Repository này là một hướng dẫn về cách sử dụng TensorFlow's Object Detection API để đào tạo một object detection classsifier cho multiple objects trên Window 10, 8, hoặc 7. (Với vài thay đổi nhỏ, nó cũng hoạt động được trên các hệ điều hành khác dựa trên nhân Linux). Bản gốc được viết dựa trên Tensorflow phiên bản 1.5, tuy nhiên nó vẫn hoạt động trên các phiên bản mới nhất của TensorFlow. From 73915a43b549865d83f15b558c2c07cc8790c6d9 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Wed, 18 Mar 2020 20:47:31 +0700 Subject: [PATCH 12/33] Update README_Vietnamese.md --- README_Vietnamese.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index f7a1703c..08acb03e 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -197,7 +197,7 @@ Sau khi TensorFlow Object Detection API đã thiết lập thành công và sẵ #### 3a. Thu thập hình ảnh TensorFlow cần lượng lớn hình ảnh của một object để có thể đào tạo một detection classifier model tốt. Để đào tạo một bộ phân loại mạnh mẽ, các hình ảnh đào tạo cần có ngẫu nhiên các objects trong ảnh cùng với các objects mong muốn, và nên có đa dạng về nền và điều kiện ánh sáng khác nhau. Cần có một số hình ảnh mà trong đó object mong muốn bị che khuất một phần, chồng chéo với một thứ khác, hoặc chỉ ở giữa bức ảnh. -Với Pinochle Card Detection classifier của tôi, tôi cso 6 objects khác nhau mà tôi muốn phát hiện (Thứ tự các quân bài chín, mười Ri, Q, K và Xì - Đây chỉ là thứ tự, tôi không nhận diện quân Xì). Tôi đã sử dụng iPhone để chụp 40 bức ảnh, với nhiều các vật thể không muốn cũng có trong ảnh. Sau đó, tôi chụp khoảng 100 bức khác cùng với đa dạng các quân bài cùng trong một bức ảnh. Tôi muốn phát hiện các thể khi chúng trùng lên nhau, vì vậy tôi chắc chắn rằng các thẻ có thể chồng lên nhau trong nhiều hình ảnh. +Với Pinochle Card Detection classifier của tôi, tôi cso 6 objects khác nhau mà tôi muốn phát hiện (Thứ tự các quân bài chín, mười Ri, Q, K và Át - Đây chỉ là thứ tự, tôi không nhận diện quân Át). Tôi đã sử dụng iPhone để chụp 40 bức ảnh, với nhiều các vật thể không muốn cũng có trong ảnh. Sau đó, tôi chụp khoảng 100 bức khác cùng với đa dạng các quân bài cùng trong một bức ảnh. Tôi muốn phát hiện các thể khi chúng trùng lên nhau, vì vậy tôi chắc chắn rằng các thẻ có thể chồng lên nhau trong nhiều hình ảnh.

From badd7e23e7bbd30ff54ebcf3ad17312f54e31489 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Thu, 19 Mar 2020 19:41:35 +0700 Subject: [PATCH 13/33] Update README_Vietnamese.md --- README_Vietnamese.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index 08acb03e..0827fd2f 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -210,19 +210,19 @@ Bạn có thể sử dụng điện thoại của bạn để chụp các hình Sau khi bạn có tất cả các hình ảnh bạn cần, chia chúng ra 20% đến thư mục \object_detection\images\test, và 80% còn lại vào thư mục \object_detection\images\train. Đảm bảo rằng có đa dạng hình ảnh trong cả hai thư mục \test và \train. #### 3b. Label Pictures -Here comes the fun part! With all the pictures gathered, it’s time to label the desired objects in every picture. LabelImg is a great tool for labeling images, and its GitHub page has very clear instructions on how to install and use it. +Chúng ta đến với phần thú vị rồi đây! Cùng với tất cả hình ảnh mà bạn đã thu thập, bây giờ là lúc gán nhãn cho các vật thể mà bạn muốn nhận diện trong mỗi bức ảnh. LabelImg là một công cụ tuyệt vời để làm việc này, và GitHub của có đầy đủ các hướng dẫn để cài đặt và sử dụng nó. -[LabelImg GitHub link](https://github.com/tzutalin/labelImg) +[Link Github của LabelImg](https://github.com/tzutalin/labelImg) -[LabelImg download link](https://www.dropbox.com/s/tq7zfrcwl44vxan/windows_v1.6.0.zip?dl=1) +[Link tải về LabelImg](https://www.dropbox.com/s/tq7zfrcwl44vxan/windows_v1.6.0.zip?dl=1) -Download and install LabelImg, point it to your \images\train directory, and then draw a box around each object in each image. Repeat the process for all the images in the \images\test directory. This will take a while! +Tải xuống và cài đặt LabelImg, chuyển đường dẫn đến thư mục chứa ảnh đào tạo của bạn \images\train, và với mỗi ảnh vẽ một hình chữ nhật (box) bao quanh mỗi vật thể mà bạn muốn nhận dạng. Lặp lại quá trình này với tất cả các ảnh trong tập kiểm thử \images\test. Quá này này sẽ mất kha khá thời gian đấy! (**Lưu ý: Bạn hãy để định dạng là PascalVOC thay vì YOLO nhé!**)

-LabelImg saves a .xml file containing the label data for each image. These .xml files will be used to generate TFRecords, which are one of the inputs to the TensorFlow trainer. Once you have labeled and saved each image, there will be one .xml file for each image in the \test and \train directories. +LabelImg lưu một file .xml bao gồm nhãn cho mỗi ảnh. Mỗi file .xml sẽ được sử dụng để tạo ra các file TFRecords, cái sẽ là đầu vào cho bộ huấn luyện với TensorFlow. Khi bạn gán nhãn và lưu mỗi ảnh, sẽ có một file .xml cho mỗi ảnh trong thư mục \test và \train. ### 4. Generate Training Data With the images labeled, it’s time to generate the TFRecords that serve as input data to the TensorFlow training model. This tutorial uses the xml_to_csv.py and generate_tfrecord.py scripts from [Dat Tran’s Raccoon Detector dataset](https://github.com/datitran/raccoon_dataset), with some slight modifications to work with our directory structure. From 3cdf449727c32ed5df5c295af3d7ef6e8db41418 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Thu, 19 Mar 2020 22:39:54 +0700 Subject: [PATCH 14/33] Update README_Vietnamese.md --- README_Vietnamese.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index 0827fd2f..1fda3e6b 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -226,7 +226,7 @@ LabelImg lưu một file .xml bao gồm nhãn cho mỗi ảnh. Mỗi file .xml s ### 4. Generate Training Data With the images labeled, it’s time to generate the TFRecords that serve as input data to the TensorFlow training model. This tutorial uses the xml_to_csv.py and generate_tfrecord.py scripts from [Dat Tran’s Raccoon Detector dataset](https://github.com/datitran/raccoon_dataset), with some slight modifications to work with our directory structure. - +Cung First, the image .xml data will be used to create .csv files containing all the data for the train and test images. From the \object_detection folder, issue the following command in the Anaconda command prompt: ``` (tensorflow1) C:\tensorflow1\models\research\object_detection> python xml_to_csv.py From 9843cf291b3d9d3a064fe2d599a78f9fb856fe6f Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Thu, 19 Mar 2020 23:11:17 +0700 Subject: [PATCH 15/33] Update README_Vietnamese.md --- README_Vietnamese.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index 1fda3e6b..38a3eed5 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -225,17 +225,17 @@ Tải xuống và cài đặt LabelImg, chuyển đường dẫn đến thư m LabelImg lưu một file .xml bao gồm nhãn cho mỗi ảnh. Mỗi file .xml sẽ được sử dụng để tạo ra các file TFRecords, cái sẽ là đầu vào cho bộ huấn luyện với TensorFlow. Khi bạn gán nhãn và lưu mỗi ảnh, sẽ có một file .xml cho mỗi ảnh trong thư mục \test và \train. ### 4. Generate Training Data -With the images labeled, it’s time to generate the TFRecords that serve as input data to the TensorFlow training model. This tutorial uses the xml_to_csv.py and generate_tfrecord.py scripts from [Dat Tran’s Raccoon Detector dataset](https://github.com/datitran/raccoon_dataset), with some slight modifications to work with our directory structure. -Cung -First, the image .xml data will be used to create .csv files containing all the data for the train and test images. From the \object_detection folder, issue the following command in the Anaconda command prompt: +Cùng với bộ dataset đã được gán nhãn, đây là lúc để tạo ra các file TFRecords cái mà sẽ làm đầu vào cho việc huấn luyện model với TensorFlow. Hướng dẫn này sử dụng file xml_to_csv.py và generate_tfrecord.py từ [Dat Tran’s Raccoon Detector dataset](https://github.com/datitran/raccoon_dataset), cùng với một số sử đổi nhỏ để có thể chạy được trong cấu trúc thư mục của chúng ta. + +Đầu tiên, các file ảnh và file .xml sẽ được sử dụng để tạo ra file .cvs bao gồm tất cả dữ liệu cho tập train và test. Từ thư mục \object_detection, ta chạy lệnh sau trong Anaconda Prompt: ``` (tensorflow1) C:\tensorflow1\models\research\object_detection> python xml_to_csv.py ``` -This creates a train_labels.csv and test_labels.csv file in the \object_detection\images folder. +Kết thúc lệnh các file train_labels.csv và test_labels.csv sẽ được tạo ra tại thư mục \object_detection\images. -Next, open the generate_tfrecord.py file in a text editor. Replace the label map starting at line 31 with your own label map, where each object is assigned an ID number. This same number assignment will be used when configuring the labelmap.pbtxt file in Step 5b. +Tiếp theo, mở file generate_tfrecord.py trong một Text Editor. Thay thế các nhãn tại dòng thứ 31 bằng các nhãn của bạn, trong đó mỗi đối tượng được gán một ID. Việc đánh số thứ tự sẽ được sử dụng khi cấu hình file the labelmap.pbtxt tại Bước 5b. -For example, say you are training a classifier to detect basketballs, shirts, and shoes. You will replace the following code in generate_tfrecord.py: +Ví dụ, bạn đang đào tạo một bộ phân loại để phát hiện bóng rổ, áo sơ-mi và giày. Bạn sẽ cần thay thế code trong file generate_tfrecord.py: ``` # TO-DO replace this with label map def class_text_to_int(row_label): @@ -254,7 +254,7 @@ def class_text_to_int(row_label): else: None ``` -With this: +Thành: ``` # TO-DO replace this with label map def class_text_to_int(row_label): @@ -267,12 +267,12 @@ def class_text_to_int(row_label): else: None ``` -Then, generate the TFRecord files by issuing these commands from the \object_detection folder: +Sau đó, tạo các file TFRecord bằng cách sử dụng lệnh dưới đây tại thư mục \object_detection: ``` python generate_tfrecord.py --csv_input=images\train_labels.csv --image_dir=images\train --output_path=train.record python generate_tfrecord.py --csv_input=images\test_labels.csv --image_dir=images\test --output_path=test.record ``` -These generate a train.record and a test.record file in \object_detection. These will be used to train the new object detection classifier. +Các file train.record và a test.record sẽ được tạo ra tại thư mục \object_detection. Chúng sẽ được sử dụng để đào tạo một bộ phân loại vật thể mới. ### 5. Create Label Map and Configure Training The last thing to do before training is to create a label map and edit the training configuration file. From 73677a024723e5762add5862491c8bb3e5bc9864 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Sat, 21 Mar 2020 20:40:46 +0700 Subject: [PATCH 16/33] Update README_Vietnamese.md --- README_Vietnamese.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index 38a3eed5..94bf269b 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -274,11 +274,11 @@ python generate_tfrecord.py --csv_input=images\test_labels.csv --image_dir=image ``` Các file train.record và a test.record sẽ được tạo ra tại thư mục \object_detection. Chúng sẽ được sử dụng để đào tạo một bộ phân loại vật thể mới. -### 5. Create Label Map and Configure Training -The last thing to do before training is to create a label map and edit the training configuration file. +### 5. Tạo Label Map và cấu hình đào tạo +Điều cuối cùng cần chuẩn bị trước khi đi vào huấn luyện model là tạo một file định nghĩa các nhãn (Label Map) và cấu hình file đào tạo. -#### 5a. Label map -The label map tells the trainer what each object is by defining a mapping of class names to class ID numbers. Use a text editor to create a new file and save it as labelmap.pbtxt in the C:\tensorflow1\models\research\object_detection\training folder. (Make sure the file type is .pbtxt, not .txt !) In the text editor, copy or type in the label map in the format below (the example below is the label map for my Pinochle Deck Card Detector): +#### 5a. Định nghĩa các nhãn +Label Map thì nói với bộ huấn luyện rằng tên mỗi vật thể được ánh xạ tương ứng với số ID. Sử dụng một Text Eidtor để tạo một file mới và lưu nó lại như là labelmap.pbtxt trong thư mục C:\tensorflow1\models\research\object_detection\training. (Đảm bảo rằng đuôi file là .pbtxt chứ không phải .txt !) Trong Text Editor, chỉnh sửa file Label Map theo format dưới đây (Ví dụ dưới đây là Label Map của bộ nhận diện các quân bài Pinochle): ``` item { id: 1 @@ -310,7 +310,7 @@ item { name: 'ace' } ``` -The label map ID numbers should be the same as what is defined in the generate_tfrecord.py file. For the basketball, shirt, and shoe detector example mentioned in Step 4, the labelmap.pbtxt file will look like: +Số ID trong Label Map phải giống với số đã được định nghĩa trong file generate_tfrecord.py. Ví dự với bộ phát hiện bóng rổ, áo sơ-mi, và giày được đề cập trong Bước 4, file labelmap.pbtxt sẽ trông như thế này: ``` item { id: 1 From 14a63960166f2c72e3e77eaab09b785dad1814b9 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Sat, 21 Mar 2020 21:25:36 +0700 Subject: [PATCH 17/33] Update README_Vietnamese.md --- README_Vietnamese.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index 94bf269b..f4ded12e 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -329,27 +329,27 @@ item { ``` #### 5b. Configure training -Finally, the object detection training pipeline must be configured. It defines which model and what parameters will be used for training. This is the last step before running training! +Cuối cùng, quá trình đạo tạo bộ phát hiện vật thể cần được thiết lập. Nó thì định nghĩa cho model biết những tham số nào sẽ được sử dụng trong quá trình đào tạo. Đây là bước cuối cùng trước khi tiến hành chạy đào tạo! -Navigate to C:\tensorflow1\models\research\object_detection\samples\configs and copy the faster_rcnn_inception_v2_pets.config file into the \object_detection\training directory. Then, open the file with a text editor. There are several changes to make to the .config file, mainly changing the number of classes and examples, and adding the file paths to the training data. +Chuyển đến thư mục C:\tensorflow1\models\research\object_detection\samples\configs và sao chép file faster_rcnn_inception_v2_pets.config đến thư mục \object_detection\training. Sau đó, mở file này trong một Text Editor. Cần một số thay đổi trong file .config, ví dụ cần phải thay đổi số lượng, và thêm đường dẫn đến thư mục chứa dataset. -Make the following changes to the faster_rcnn_inception_v2_pets.config file. Note: The paths must be entered with single forward slashes (NOT backslashes), or TensorFlow will give a file path error when trying to train the model! Also, the paths must be in double quotation marks ( " ), not single quotation marks ( ' ). +Tiến hành các thay đổi với file faster_rcnn_inception_v2_pets.config file theo hướng dẫn dưới đây. Lưu ý: Đường dẫn cần phải được nhập bằng dấu gạch chéo đơn (/) (KHÔNG phải dấu gạch chéo ngược (\)), dẫn đến TensorFlow sẽ có thể sinh ra lỗi với đường dẫn file! Ngoài ra, các đường dẫn phải được để trong dấu ngoặc kép ("), không phải là dấu ngoặc kép đơn ('). -- Line 9. Change num_classes to the number of different objects you want the classifier to detect. For the above basketball, shirt, and shoe detector, it would be num_classes : 3 . -- Line 106. Change fine_tune_checkpoint to: +- Dòng 9. Thay đổi num_classes thành số lượng objects mà bạn muốn phát hiện. Ví dụ trên với bộ phát hiện bóng rổ, áo sơ-mi, và giày thì num_classes bằng: 3. +- Line 106. Thay đổi fine_tune_checkpoint thành: - fine_tune_checkpoint : "C:/tensorflow1/models/research/object_detection/faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt" -- Lines 123 and 125. In the train_input_reader section, change input_path and label_map_path to: +- Lines 123 và 125. Trong mục train_input_reader, thay đổi input_path và label_map_path thành: - input_path : "C:/tensorflow1/models/research/object_detection/train.record" - label_map_path: "C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt" -- Line 130. Change num_examples to the number of images you have in the \images\test directory. +- Line 130. Thay đổi num_examples thành số lượng ảnh test bạn có trong thư mục \images\test directory. -- Lines 135 and 137. In the eval_input_reader section, change input_path and label_map_path to: +- Lines 135 và 137. Trong mục eval_input_reader, thay đổi input_path and label_map_path thành: - input_path : "C:/tensorflow1/models/research/object_detection/test.record" - label_map_path: "C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt" -Save the file after the changes have been made. That’s it! The training job is all configured and ready to go! +Lưu lại các file sau khi đã thay đổi. Vậy là xong! Cấu hình đào tạo đã sẵn sàng để chạy huấn luyện model! ### 6. Run the Training **UPDATE 9/26/18:** From 38bd9553621987ab93a363aba3de5a4ea4b6fb5c Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Sat, 21 Mar 2020 21:29:54 +0700 Subject: [PATCH 18/33] Update README_Vietnamese.md --- README_Vietnamese.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index f4ded12e..52fc6eb9 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -351,8 +351,8 @@ Tiến hành các thay đổi với file faster_rcnn_inception_v2_pets.config fi Lưu lại các file sau khi đã thay đổi. Vậy là xong! Cấu hình đào tạo đã sẵn sàng để chạy huấn luyện model! -### 6. Run the Training -**UPDATE 9/26/18:** +### 6. Chạy quá trình đào tạo +**CẬP NHẬT 9/26/18:** *As of version 1.9, TensorFlow has deprecated the "train.py" file and replaced it with "model_main.py" file. I haven't been able to get model_main.py to work correctly yet (I run in to errors related to pycocotools). Fortunately, the train.py file is still available in the /object_detection/legacy folder. Simply move train.py from /object_detection/legacy into the /object_detection folder and then continue following the steps below.* Here we go! From the \object_detection directory, issue the following command to begin training: From 32ff8c14433a0a943a3793bb83f8fae98a96a9d6 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Mon, 23 Mar 2020 18:50:32 +0700 Subject: [PATCH 19/33] Update README_Vietnamese.md --- README_Vietnamese.md | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index 52fc6eb9..b8b1f28d 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -353,38 +353,39 @@ Lưu lại các file sau khi đã thay đổi. Vậy là xong! Cấu hình đào ### 6. Chạy quá trình đào tạo **CẬP NHẬT 9/26/18:** -*As of version 1.9, TensorFlow has deprecated the "train.py" file and replaced it with "model_main.py" file. I haven't been able to get model_main.py to work correctly yet (I run in to errors related to pycocotools). Fortunately, the train.py file is still available in the /object_detection/legacy folder. Simply move train.py from /object_detection/legacy into the /object_detection folder and then continue following the steps below.* +*Ở phiên bản 1.9, TensorFlow đã thay thế file "train.py" bằng file "model_main.py". Tôi chưa tìm ra cách đê file model_main.py hoạt động chính xác (Tôi chạy và nó xảy ra lỗi). May mắn thay, file train.pt vẫn còn ở trong thư mục /object_detection/legacy. Đơn giản chỉ cần di chuyên file train.py từ thư mục /object_detection/legacy đến thư mục /object_detection và tiếp thục thực hiện theo các bước dưới đây* + +Nào chúng ta bắt đầu! Chuyển đến đường dẫn \object_detection, sau đó gõ lệnh sau để bắt đầu training: -Here we go! From the \object_detection directory, issue the following command to begin training: ``` python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config ``` -If everything has been set up correctly, TensorFlow will initialize the training. The initialization can take up to 30 seconds before the actual training begins. When training begins, it will look like this: +Nếu mọi thứ đã được thiết lập đúng, TensorFLow sẽ khởi tạo quá trình đào tạo. Việc khởi tạo này cần khoảng 30 trước khi bắt đầu đào tạo. Khi quá trình đào tạo bắt đầu, nó sẽ trông như thế này:

-Each step of training reports the loss. It will start high and get lower and lower as training progresses. For my training on the Faster-RCNN-Inception-V2 model, it started at about 3.0 and quickly dropped below 0.8. I recommend allowing your model to train until the loss consistently drops below 0.05, which will take about 40,000 steps, or about 2 hours (depending on how powerful your CPU and GPU are). Note: The loss numbers will be different if a different model is used. MobileNet-SSD starts with a loss of about 20, and should be trained until the loss is consistently under 2. +Sau mỗi bước đào tạo, hàm loss sẽ được tính ra. Nó sẽ bắt đầu với giá trị cao và giảm dần trong suốt quá trình đào tạo. Với việc đào tạo model Faster-RCNN-Inception-V2 của tôi, nó bắt đầu vào khoảng 3.0 và nhanh chóng giảm xuống dưới 0.8. Tôi khuyên bạn nên đào tạo đến khi hàm loss giảm xuống dưới 0.05, việc này cần đến 40,000 bước, hoặc 2 giờ (phụ thuộc vào cấu hình CPU và GPU của bạn). Lưu ý: Hàm mất mát sẽ khác với các model khác nhau được sử dụng. MobileNet-SSD bắt đầu với hàm loss khoảng 20, và cần được đào tạo cho đến khi giảm xuống dưới 2. -You can view the progress of the training job by using TensorBoard. To do this, open a new instance of Anaconda Prompt, activate the tensorflow1 virtual environment, change to the C:\tensorflow1\models\research\object_detection directory, and issue the following command: +Bạn có thể thấy quá trình của việc đào tạo bằng việc sử dụng TensorBoard. Để làm được điều này, mở một terminal mới của Anaconda Prompt, kích hoạt môi trường tensorflow1, chuyển đường dẫn đến thư mục C:\tensorflow1\models\research\object_detection, sau đó chạy lệnh dưới đây: ``` (tensorflow1) C:\tensorflow1\models\research\object_detection>tensorboard --logdir=training ``` -This will create a webpage on your local machine at YourPCName:6006, which can be viewed through a web browser. The TensorBoard page provides information and graphs that show how the training is progressing. One important graph is the Loss graph, which shows the overall loss of the classifier over time. +Lệnh này sẽ tạo ra một trang web trên máy cục bộ của bạn tại YourPCName:6006, ta có thể xem nó qua trình duyệt web. Trang TensorBoard cung cấp thông tin và biểu đồ về quá trình đào tạo. Một trong những biểu đồ quan trọng là biểu đồ Loss, cái mà minh họa cho tổng giá trị mất mát của bộ phân loại theo thời gian.

-The training routine periodically saves checkpoints about every five minutes. You can terminate the training by pressing Ctrl+C while in the command prompt window. I typically wait until just after a checkpoint has been saved to terminate the training. You can terminate training and start it later, and it will restart from the last saved checkpoint. The checkpoint at the highest number of steps will be used to generate the frozen inference graph. +Trong quá trình đào tạo các checkpoints sẽ được lưu lại sau mỗi 5 phút. Bạn có thể kết thúc quá trìnhd đào tạo bằng cách ấn Ctrl+C trong cửa sổ terminal. Tôi thường đợi sau khi một checkpint đã được lưu sau đó mới dừng đào tạo. Bạn có thể kết thúc và bắt đầu đào tạo lại tiếp, và nó sẽ bắt đầu từ cái checkpoint gần nhất được lưu lại. Checkpoint tại bước đào tạo lớn nhất sẽ được dùng để tạo ra Inference Graph (model). -### 7. Export Inference Graph -Now that training is complete, the last step is to generate the frozen inference graph (.pb file). From the \object_detection folder, issue the following command, where “XXXX” in “model.ckpt-XXXX” should be replaced with the highest-numbered .ckpt file in the training folder: +### 7. Tạo Inference Graph +Bây giờ quá trình đào tạo đã hoàn tất, bước cuối cùng là tạo ra inference graph (.pb file). Từ thư mục \object_detection, chạy lệnh dưới đây, với “XXXX” trong “model.ckpt-XXXX” bằng giá trị trong tệp .ckpt được đánh số cao nhât trong thư mục đào tạo: ``` python export_inference_graph.py --input_type image_tensor --pipeline_config_path training/faster_rcnn_inception_v2_pets.config --trained_checkpoint_prefix training/model.ckpt-XXXX --output_directory inference_graph ``` -This creates a frozen_inference_graph.pb file in the \object_detection\inference_graph folder. The .pb file contains the object detection classifier. +Lệnh này sẽ tạo một file frozen_inference_graph.pb trong thư mục \object_detection\inference_graph. File .pb chứa model phát hiện phân loại đối tượng. ### 8. Use Your Newly Trained Object Detection Classifier! The object detection classifier is all ready to go! I’ve written Python scripts to test it out on an image, video, or webcam feed. From 05b8da0396c9c78c3155207b744a7c61e75789b0 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Tue, 24 Mar 2020 07:10:29 +0700 Subject: [PATCH 20/33] Update README_Vietnamese.md --- README_Vietnamese.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index b8b1f28d..9fa72785 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -387,24 +387,24 @@ python export_inference_graph.py --input_type image_tensor --pipeline_config_pat ``` Lệnh này sẽ tạo một file frozen_inference_graph.pb trong thư mục \object_detection\inference_graph. File .pb chứa model phát hiện phân loại đối tượng. -### 8. Use Your Newly Trained Object Detection Classifier! -The object detection classifier is all ready to go! I’ve written Python scripts to test it out on an image, video, or webcam feed. +### 8. Sử dụng Object Detection Classifier mới vừa được đào tạo! +Object Detection Classifier đã sẵn sàng để chạy! Tôi đã viết một file Python để test nó trên một ảnh, video hoặc đầu vào từ webcam. -Before running the Python scripts, you need to modify the NUM_CLASSES variable in the script to equal the number of classes you want to detect. (For my Pinochle Card Detector, there are six cards I want to detect, so NUM_CLASSES = 6.) +Trước khi chạy file Python, bạn cần chỉnh lại biến NUM_CLASSES bằng với số lượng class mà bạn muốn nhận diện. (Với bộ nhận diện quân bài Pinochle, vì có 6 quân bài tôi muốn nhận diện, nên NUM_CLASSES = 6.) -To test your object detector, move a picture of the object or objects into the \object_detection folder, and change the IMAGE_NAME variable in the Object_detection_image.py to match the file name of the picture. Alternatively, you can use a video of the objects (using Object_detection_video.py), or just plug in a USB webcam and point it at the objects (using Object_detection_webcam.py). +Để test bộ phát hiện vật thể của bạn, di chuyển hình ảnh chứa vật thể đó đến thư mục \object_detection, và thay đổi đường dẫn ảnh IMAGE_NAME trong file Object_detection_image.py bằng đường dẫn hình ảnh của bạn. Ngoài ra, bạn có thể sử dụng video chứa vật thể (sử dụng file Object_detection_video.py), hoặc chỉ cần cắm USB webcam và hướng nó vào nơi chứa vật thể (sử dụng Object_detection_webcam.py). -To run any of the scripts, type “idle” in the Anaconda Command Prompt (with the “tensorflow1” virtual environment activated) and press ENTER. This will open IDLE, and from there, you can open any of the scripts and run them. +Để chạy bất kì một file test nào, gõ "idle" trong Anaconda Command Prompt (cùng với môi trường ảo “tensorflow1” đã được kích hoạt) và nhấn ENTER. Việc này sẽ mở IDLE, và từ đây, bạn có thể mở bất kỳ các kịch bản test nào và chạy chúng. -If everything is working properly, the object detector will initialize for about 10 seconds and then display a window showing any objects it’s detected in the image! +Nếu mọi thứ hoạt động bình thường, trình phát hiện đối tượng sẽ được khởi tạo trong khoảng 10 giây và sau đó hiện thỉ một cửa sổ với box của vật thể đã phát hiện được trong ảnh!

-If you encounter errors, please check out the Appendix: it has a list of errors that I ran in to while setting up my object detection classifier. You can also trying Googling the error. There is usually useful information on Stack Exchange or in TensorFlow’s Issues on GitHub. +Nếu bạn gặp lỗi, vui lòng kiểm tra Phụ lục: tại đây chứa một danh sách các lỗi và tôi gặp trong quá trình thiết lập bộ phát hiện và phân loại đối tượng của mình. Bạn cũng có thể thử tìm kiếm trên Google về lỗi bạn gặp phải. Thường có những thông tin hữu ích trên Stack Exchange hoặc các vấn đề với TensorFlow trên GitHub. -## Appendix: Common Errors +## Phụ lục: Các lỗi thường gặp It appears that the TensorFlow Object Detection API was developed on a Linux-based operating system, and most of the directions given by the documentation are for a Linux OS. Trying to get a Linux-developed software library to work on Windows can be challenging. There are many little snags that I ran in to while trying to set up tensorflow-gpu to train an object detection classifier on Windows 10. This Appendix is a list of errors I ran in to, and their resolutions. #### 1. ModuleNotFoundError: No module named 'deployment' or No module named 'nets' From feb9b4fa52ab71fae73178c380cff7afd0c3c502 Mon Sep 17 00:00:00 2001 From: Tran Hai Quan Date: Tue, 24 Mar 2020 09:47:09 +0700 Subject: [PATCH 21/33] Update README_Vietnamese.md --- README_Vietnamese.md | 36 +++++++++++++++++++----------------- 1 file changed, 19 insertions(+), 17 deletions(-) diff --git a/README_Vietnamese.md b/README_Vietnamese.md index 9fa72785..c00b03ae 100644 --- a/README_Vietnamese.md +++ b/README_Vietnamese.md @@ -405,15 +405,16 @@ Nếu mọi thứ hoạt động bình thường, trình phát hiện đối tư Nếu bạn gặp lỗi, vui lòng kiểm tra Phụ lục: tại đây chứa một danh sách các lỗi và tôi gặp trong quá trình thiết lập bộ phát hiện và phân loại đối tượng của mình. Bạn cũng có thể thử tìm kiếm trên Google về lỗi bạn gặp phải. Thường có những thông tin hữu ích trên Stack Exchange hoặc các vấn đề với TensorFlow trên GitHub. ## Phụ lục: Các lỗi thường gặp -It appears that the TensorFlow Object Detection API was developed on a Linux-based operating system, and most of the directions given by the documentation are for a Linux OS. Trying to get a Linux-developed software library to work on Windows can be challenging. There are many little snags that I ran in to while trying to set up tensorflow-gpu to train an object detection classifier on Windows 10. This Appendix is a list of errors I ran in to, and their resolutions. +Dường như TensorFlow Object Detection API được phát triển trên hệ điều hành Linux và hầu hết tài liệu hướng dẫn đều dành cho hệ điều hành Linux. Việc cố gắng đê các thư viện được phát triển cho Linux hoạt động được trên Windows là một thách thức. Có rất nhiều vấn đề mà tôi gặp phải khi phải cố gắng thiết lập tensorflow-gpu để đào tạo một trình phát hiện và phân loại đối tượng trên Windows 10. Phụ lục này là danh sách các lỗi mà tôi gặp phải và hướng khắc phục chúng. + #### 1. ModuleNotFoundError: No module named 'deployment' or No module named 'nets' -This error occurs when you try to run object_detection_tutorial.ipynb or train.py and you don’t have the PATH and PYTHONPATH environment variables set up correctly. Exit the virtual environment by closing and re-opening the Anaconda Prompt window. Then, issue “activate tensorflow1” to re-enter the environment, and then issue the commands given in Step 2e. +Lỗi này gặp phải khi bạn cố gắng chạy file object_detection_tutorial.ipynb hoặc train.py khi bạn chưa thiết lập các biến môi trường PATH và PYTHONPATH một cách chính xác. Thoát khỏi môi trường ảnh bằng cách đóng và mở lại cửa sổ Anaconda Prompt. Sau đó, thực hiện “activate tensorflow1” để mở lại môi trường, bằng cách thực hiện các lệnh đã được hướng dẫn ở bước Step 2e. -You can use “echo %PATH%” and “echo %PYTHONPATH%” to check the environment variables and make sure they are set up correctly. +Bạn có thể sử dụng “echo %PATH%” và “echo %PYTHONPATH%” để kiểm tra các biến môi trường và đảm bảo mọi thứ đã được thiết lập đúng. -Also, make sure you have run these commands from the \models\research directory: +Ngoài ra, đảm bảo bạn đã chạy các lệnh với đường dẫn là \models\research: ``` setup.py build setup.py install @@ -423,34 +424,35 @@ setup.py install #### ImportError: cannot import name 'string_int_label_map_pb2' -#### (or similar errors with other pb2 files) +#### (hoặc lỗi tương tự với các file pb2 khác) + +Lỗi này xảy ra khi các file (trong trường hợp này, preprocessor.proto) chưa được biên dịch. Chạy lại lệnh protoc theo chỉ dẫn ở Bước 2f. Kiểm tra thư mục \object_detection\protos để đảm bảo rằng mỗi file name_pb2.py sẽ có một file name.proto tương ứng. -This occurs when the protobuf files (in this case, preprocessor.proto) have not been compiled. Re-run the protoc command given in Step 2f. Check the \object_detection\protos folder to make sure there is a name_pb2.py file for every name.proto file. #### 3. object_detection/protos/.proto: No such file or directory -This occurs when you try to run the +Lỗi này xảy ra khi bạn cố gắng chạy lệnh: ``` “protoc object_detection/protos/*.proto --python_out=.” ``` -command given on the TensorFlow Object Detection API installation page. Sorry, it doesn’t work on Windows! Copy and paste the full command given in Step 2f instead. There’s probably a more graceful way to do it, but I don’t know what it is. +Lệnh này được đưa ra trên trang cài đặt TensorFlow Object Detection API. Rất tiếc, nó không hoạt động trên Windows! Thay vào đó copy và paste tất các các lệnh được đưa trong Bước 2f. Có thể có cách đơn giản hơn để làm việc này, nhưng tôi chưa tìm ra lệnh đó là gì. -#### 4. Unsuccessful TensorSliceReader constructor: Failed to get "file path" … The filename, directory name, or volume label syntax is incorrect. +#### 4. Unsuccessful TensorSliceReader constructor: Failed to get "file path" … Tên tệp, tên thư mục, hoặc số lượng nhãn không chính xác. -This error occurs when the filepaths in the training configuration file (faster_rcnn_inception_v2_pets.config or similar) have not been entered with backslashes instead of forward slashes. Open the .config file and make sure all file paths are given in the following format: +Lỗi này xảy ra khi đường dẫn trong file cấu hình hình đào tạo (faster_rcnn_inception_v2_pets.config hoặc tương tự) chư được nhập bằng dấu gạch chéo ngược thay vì dấu gạch chéo xuôi. Mở file .config và đảm bảo rằng tất cả các đường dẫn tuân theo định dạng sau: ``` “C:/path/to/model.file” ``` #### 5. ValueError: Tried to convert 't' to a tensor and failed. Error: Argument must be a dense tensor: range(0, 3) - got shape [3], but wanted []. -The issue is with models/research/object_detection/utils/learning_schedules.py Currently it is +Vấn đề với models/research/object_detection/utils/learning_schedules.py hiện tại nó là ``` rate_index = tf.reduce_max(tf.where(tf.greater_equal(global_step, boundaries), range(num_boundaries), [0] * num_boundaries)) ``` -Wrap list() around the range() like this: +Sửa lại list() xung quanh range() giống như thế này: ``` rate_index = tf.reduce_max(tf.where(tf.greater_equal(global_step, boundaries), @@ -461,15 +463,15 @@ rate_index = tf.reduce_max(tf.where(tf.greater_equal(global_step, boundaries), [Ref: Tensorflow Issue#3705](https://github.com/tensorflow/models/issues/3705#issuecomment-375563179) #### 6. ImportError: DLL load failed: The specified procedure could not be found. (or other DLL-related errors) -This error occurs because the CUDA and cuDNN versions you have installed are not compatible with the version of TensorFlow you are using. The easiest way to resolve this error is to use Anaconda's cudatoolkit package rather than manually installing CUDA and cuDNN. If you ran into these errors, try creating a new Anaconda virtual environment: +Lỗi này xảy ra do các phiên bản CUDA và cuDNN bạn cài đặt không tương thích với phiên bản TensorFLow bạn đang sử dụng. Cách dễ nhất để khắc phục lỗi này là sử dụng các gói cudatookit của Anaconda thay vì cài đặt thủ công CUDA và cuDNN. Nếu bạn gặp phải lỗi này, hãy thử tạo một Anaconda virtual environment mới: ``` conda create -n tensorflow2 pip python=3.5 ``` -Then, once inside the environment, install TensorFlow using CONDA rather than PIP: +Sau đó, khi đã kích hoạt môi trường, cài đặt TensorFlow sử dụng CONDA thay vì PIP: ``` conda install tensorflow-gpu ``` -Then restart this guide from Step 2 (but you can skip the part where you install TensorFlow in Step 2d). +Sau đó, thực hiện lại hướng dẫn này từ Bước 2 ( nhưng bạn có thể bỏ qua việc cài đặt TensorFlow tại Bước 2d). -#### 7. In Step 2g, the Jupyter Notebook runs all the way through with no errors, but no pictures are displayed at the end. -If you run the full Jupyter Notebook without getting any errors, but the labeled pictures still don't appear, try this: go in to object_detection/utils/visualization_utils.py and comment out the import statements around lines 29 and 30 that include matplotlib. Then, try re-running the Jupyter notebook. (The visualization_utils.py script changes quite a bit, so it might not be exactly line 29 and 30.) +#### 7. Tại Bước 2g, Jupyter Notebook chạy mà không xảy ra lỗi, nhưng không có hình ảnh nào được hiển thị vào cuối cùng. +Nếu bạn chạy Jupyter Notebook mà không gặp bất kì lỗi nào, nhưng các hình ảnh được gán nhãn vấn không xuất hiện, thử cách sau: truy cập file object_detection/utils/visualization_utils.py và comment các câu lệnh tại dòng 29 và 30 cái mà bao gồm matplotlib. Sau đó, thử chạy lại Jupyter notebook. ( File visualization_utils.py được thay đổi khá nhiều, do đó nó có thể không chính xác tại dòng 29 và 30.) From 39b822fbfdf6b66960e3cf7f73fe513f9add8853 Mon Sep 17 00:00:00 2001 From: winter2897 Date: Fri, 27 Mar 2020 23:44:14 +0700 Subject: [PATCH 22/33] Fix Readme_vienamese.md --- README_Vietnamese.md => translate/README_Vietnamese.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename README_Vietnamese.md => translate/README_Vietnamese.md (99%) diff --git a/README_Vietnamese.md b/translate/README_Vietnamese.md similarity index 99% rename from README_Vietnamese.md rename to translate/README_Vietnamese.md index c00b03ae..dbbda771 100644 --- a/README_Vietnamese.md +++ b/translate/README_Vietnamese.md @@ -15,7 +15,7 @@ Tôi đã làm một YouTube video tóm tắt về hướng dẫn này. Bất k File readme mô tả tất cả các bước cần thiết để bắt đầu đào tạo một Object detection classifier của riêng bạn: 1. [Cài đặt Anaconda, CUDA, và cuDNN](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#1-install-anaconda-cuda-and-cudnn) -2. [Thiết lập cấu trúc thư mục Object Detection và Anaconda Virtual Environment](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#2-set-up-tensorflow-directory-and-anaconda-virtual-environment) +2. [Thiết lập Thư mục TensorFlow và Anaconda Virtual Environment](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#2-set-up-tensorflow-directory-and-anaconda-virtual-environment) 3. [Thu thập và gán nhãn hình ảnh](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#3-gather-and-label-pictures) 4. [Tạo dữ liệu đào tạo](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#4-generate-training-data) 5. [Tạo một label map và thiết lập trước khi đào tạo](https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#5-create-label-map-and-configure-training) From dbd1532b35e1ff666cef910ce8625abb86f9563a Mon Sep 17 00:00:00 2001 From: winter2897 Date: Fri, 27 Mar 2020 23:46:19 +0700 Subject: [PATCH 23/33] Fix Readme_vienamese.md no image --- translate/doc/C_drive_tensorflow1.jpg | Bin 0 -> 83977 bytes translate/doc/YouTube video.jpg | Bin 0 -> 124983 bytes translate/doc/collage.jpg | Bin 0 -> 335044 bytes translate/doc/detector1.jpg | Bin 0 -> 101365 bytes translate/doc/detector2.jpg | Bin 0 -> 107503 bytes translate/doc/jupyter_notebook_dogs.jpg | Bin 0 -> 167717 bytes translate/doc/labels.jpg | Bin 0 -> 138396 bytes translate/doc/loss_graph.JPG | Bin 0 -> 22343 bytes translate/doc/models.jpg | Bin 0 -> 48545 bytes translate/doc/object_detection_directory.jpg | Bin 0 -> 100182 bytes translate/doc/rcnn_vs_ssd.jpg | Bin 0 -> 235615 bytes 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