Correct pathes and fix error

.codecov.yml

@@ -0,0 +1,42 @@
+#see https://github.com/codecov/support/wiki/Codecov-Yaml
+codecov:
+  notify:
+    require_ci_to_pass: yes
+
+coverage:
+  precision: 0  # 2 = xx.xx%, 0 = xx%
+  round: nearest # how coverage is rounded: down/up/nearest
+  range: 40...100 # custom range of coverage colors from red -> yellow -> green
+  status:
+    # https://codecov.readme.io/v1.0/docs/commit-status
+    project:
+      default:
+        against: auto
+        target: 90% # specify the target coverage for each commit status
+        threshold: 20% # allow this little decrease on project
+        # https://github.com/codecov/support/wiki/Filtering-Branches
+        # branches: master
+        if_ci_failed: error
+    # https://github.com/codecov/support/wiki/Patch-Status
+    patch:
+      default:
+        against: auto
+        target: 40% # specify the target "X%" coverage to hit
+        # threshold: 50% # allow this much decrease on patch
+    changes: false
+
+parsers:
+  gcov:
+    branch_detection:
+      conditional: true
+      loop: true
+      macro: false
+      method: false
+  javascript:
+    enable_partials: false
+
+comment:
+  layout: header, diff
+  require_changes: false
+  behavior: default  # update if exists else create new
+  # branches: *

.gitignore

@@ -106,3 +106,10 @@ ENV/
 
 # mypy
 .mypy_cache/
+
+# PyCharm IDE
+.idea/
+
+# Temporary
+temp/
+temporary/

.travis.yml

@@ -0,0 +1,24 @@
+# this file is *not* meant to cover or endorse the use of travis, but rather to
+# help confirm pull requests to this project.
+
+language: python
+
+matrix:
+  include:
+#    - python: 2.7
+#      env: TOXENV=py27
+    - python: 3.5
+      env: TOXENV=py35
+    - python: 3.6
+      env: TOXENV=py36
+    - python: 3.7
+      env: TOXENV=py37
+
+install:
+  - pip install -r requirements.txt
+  - pip install tox codecov
+  - pip list
+
+script: tox
+
+after_success: codecov

MANIFEST.in

@@ -0,0 +1,28 @@
+# Manifest syntax https://docs.python.org/2/distutils/sourcedist.html
+graft wheelhouse
+
+recursive-exclude __pycache__  *.pyc *.pyo *.orig
+
+# Include the README
+include *.md
+
+# Include the license file
+include LICENSE
+
+# Include the data files
+recursive-include model_data *.txt *.cfg *.yaml *.csv *.jpg
+
+# Include scripts
+recursive-include scripts *.py
+
+# Include the Requirements
+include requirements.txt
+
+# Exclude build configs
+exclude *.yml
+
+prune .git
+prune venv
+prune temp
+prune results
+prune test*

README.md

@@ -1,99 +1,91 @@
 # keras-yolo3
 
+[![Build Status](https://travis-ci.org/Borda/keras-yolo3.svg?branch=master)](https://travis-ci.org/Borda/keras-yolo3)
+[![Build status](https://ci.appveyor.com/api/projects/status/24m00vife2wae7k0/branch/master?svg=true)](https://ci.appveyor.com/project/Borda/keras-yolo3/branch/master)
+[![CircleCI](https://circleci.com/gh/Borda/keras-yolo3.svg?style=svg)](https://circleci.com/gh/Borda/keras-yolo3)
+[![codecov](https://codecov.io/gh/Borda/keras-yolo3/branch/master/graph/badge.svg)](https://codecov.io/gh/Borda/keras-yolo3)
+[![Codacy Badge](https://api.codacy.com/project/badge/Grade/e03dbbb0f0fd48baa70f637456f1fe36)](https://www.codacy.com/project/Borda/keras-yolo3/dashboard?utm_source=github.com&utm_medium=referral&utm_content=Borda/keras-yolo3&utm_campaign=Badge_Grade_Dashboard)
+[![CodeFactor](https://www.codefactor.io/repository/github/borda/keras-yolo3/badge)](https://www.codefactor.io/repository/github/borda/keras-yolo3)
 [![license](https://img.shields.io/github/license/mashape/apistatus.svg)](LICENSE)
 
 ## Introduction
 
-A Keras implementation of YOLOv3 (Tensorflow backend) inspired by [allanzelener/YAD2K](https://github.com/allanzelener/YAD2K).
-
+A [Keras](https://keras.io/) implementation of YOLOv3 ([Tensorflow backend](https://www.tensorflow.org/)) inspired by [allanzelener/YAD2K](https://github.com/allanzelener/YAD2K).
 
 ---
 
 ## Quick Start
 
+For more model and configuration please see  [YOLO website](http://pjreddie.com/darknet/yolo/) and [darknet](https://github.com/pjreddie/darknet/tree/master/cfg) repository.
+
 1. Download YOLOv3 weights from [YOLO website](http://pjreddie.com/darknet/yolo/).
+    ```bash
+    wget -O ./model_data/yolo3.weights  \
+       https://pjreddie.com/media/files/yolov3.weights  \
+       --progress=bar:force:noscroll
+    ```
+    alternatively you can download light version `yolov3-tiny.weights` 
 2. Convert the Darknet YOLO model to a Keras model.
+    ```bash
+    python3 scripts/convert_weights.py \
+        --config_path ./model_data/yolo.cfg \
+        --weights_path ./model_data/yolo.weights \
+        --output_path ./model_data/yolo.h5
+    ```
 3. Run YOLO detection.
+    ```bash
+    python3 scripts/predict.py \
+       --path_weights ./model_data/yolo.h5 \
+       --path_anchors ./model_data/yolo_anchors.csv \
+       --path_classes ./model_data/coco_classes.txt \
+       --path_output ./results \
+       --path_image ./model_data/bike-car-dog.jpg \
+       --path_video person.mp4
+    ```
+    For Full YOLOv3, just do in a similar way, just specify model path and anchor path with `--path_weights <model_file>` and `--path_anchors <anchor_file>`.
+4. MultiGPU usage: use `--gpu_num N` to use N GPUs. It is passed to the Keras [multi_gpu_model()](https://keras.io/utils/#multi_gpu_model).
 
-```
-wget https://pjreddie.com/media/files/yolov3.weights
-python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
-python yolo_video.py [OPTIONS...] --image, for image detection mode, OR
-python yolo_video.py [video_path] [output_path (optional)]
-```
-
-For Tiny YOLOv3, just do in a similar way, just specify model path and anchor path with `--model model_file` and `--anchors anchor_file`.
-
-### Usage
-Use --help to see usage of yolo_video.py:
-```
-usage: yolo_video.py [-h] [--model MODEL] [--anchors ANCHORS]
-                     [--classes CLASSES] [--gpu_num GPU_NUM] [--image]
-                     [--input] [--output]
-
-positional arguments:
-  --input        Video input path
-  --output       Video output path
-
-optional arguments:
-  -h, --help         show this help message and exit
-  --model MODEL      path to model weight file, default model_data/yolo.h5
-  --anchors ANCHORS  path to anchor definitions, default
-                     model_data/yolo_anchors.txt
-  --classes CLASSES  path to class definitions, default
-                     model_data/coco_classes.txt
-  --gpu_num GPU_NUM  Number of GPU to use, default 1
-  --image            Image detection mode, will ignore all positional arguments
-```
 ---
 
-4. MultiGPU usage: use `--gpu_num N` to use N GPUs. It is passed to the [Keras multi_gpu_model()](https://keras.io/utils/#multi_gpu_model).
-
 ## Training
 
+For training you can use [VOC dataset](http://host.robots.ox.ac.uk/pascal/VOC/), [COCO datset](cocodataset.org) or your own...
+
 1. Generate your own annotation file and class names file.  
-    One row for one image;  
-    Row format: `image_file_path box1 box2 ... boxN`;  
-    Box format: `x_min,y_min,x_max,y_max,class_id` (no space).  
-    For VOC dataset, try `python voc_annotation.py`  
+    * One row for one image;  
+    * Row format: `image_file_path box1 box2 ... boxN`;  
+    * Box format: `x_min,y_min,x_max,y_max,class_id` (no space).  
+    * For VOC dataset.  
+    Run one of following scrips for dataset conversion 
+    * `scripts/annotation_voc.py` 
+    * `scripts/annotation_coco.py` 
+    * `scripts/annotation_csv.py`  
     Here is an example:
-    ```
+    ```text
     path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3
     path/to/img2.jpg 120,300,250,600,2
     ...
     ```
-
-2. Make sure you have run `python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5`  
-    The file model_data/yolo_weights.h5 is used to load pretrained weights.
-
-3. Modify train.py and start training.  
-    `python train.py`  
-    Use your trained weights or checkpoint weights with command line option `--model model_file` when using yolo_video.py
+2. Make sure you have run `python scripts/convert_weights.py <...>`.
+    The file `model_data/yolo_weights.h5` is used to load pre-trained weights.
+3. Modify train.py and start training.  `python train.py`.
+    Use your trained weights or checkpoint weights with command line option `--model model_file` when using `yolo_interactive.py`.
     Remember to modify class path or anchor path, with `--classes class_file` and `--anchors anchor_file`.
 
-If you want to use original pretrained weights for YOLOv3:  
-    1. `wget https://pjreddie.com/media/files/darknet53.conv.74`  
-    2. rename it as darknet53.weights  
-    3. `python convert.py -w darknet53.cfg darknet53.weights model_data/darknet53_weights.h5`  
-    4. use model_data/darknet53_weights.h5 in train.py
+If you want to use original pre-trained weights for YOLOv3:  
+  1. `wget https://pjreddie.com/media/files/darknet53.conv.74`  
+  2. rename it as `darknet53.weights`  
+  3. `python convert.py -w darknet53.cfg darknet53.weights model_data/darknet53_weights.h5`  
+  4. use `model_data/darknet53_weights.h5` in `train.py`
 
 ---
 
 ## Some issues to know
 
-1. The test environment is
-    - Python 3.5.2
-    - Keras 2.1.5
-    - tensorflow 1.6.0
-
+1. The test environment is Python 3.5.2 ; Keras 2.1.5 ; tensorflow 1.6.0
 2. Default anchors are used. If you use your own anchors, probably some changes are needed.
-
 3. The inference result is not totally the same as Darknet but the difference is small.
-
 4. The speed is slower than Darknet. Replacing PIL with opencv may help a little.
-
 5. Always load pretrained weights and freeze layers in the first stage of training. Or try Darknet training. It's OK if there is a mismatch warning.
-
-6. The training strategy is for reference only. Adjust it according to your dataset and your goal. And add further strategy if needed.
-
-7. For speeding up the training process with frozen layers train_bottleneck.py can be used. It will compute the bottleneck features of the frozen model first and then only trains the last layers. This makes training on CPU possible in a reasonable time. See [this](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html) for more information on bottleneck features.
+6. The training strategy is for reference only. Adjust it according to your dataset and your goal. and add further strategy if needed.
+7. For speeding up the training process with frozen layers train_bottleneck.py can be used. It will compute the bottleneck features of the frozen model first and then only trains the last layers. This makes training on CPU possible in a reasonable time. See this [post](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html) for more information on bottleneck features.

appveyor.yml

@@ -0,0 +1,48 @@
+# https://www.appveyor.com/docs/appveyor-yml/
+environment:
+
+  # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the
+  # /E:ON and /V:ON options are not enabled in the batch script interpreter
+  # See: http://stackoverflow.com/a/13751649/163740
+  CMD_IN_ENV: "cmd /E:ON /V:ON /C obvci_appveyor_python_build_env.cmd"
+
+  matrix:
+
+    # Pre-installed Python versions, which Appveyor may upgrade to
+    # a later point release.
+    # See: http://www.appveyor.com/docs/installed-software#python
+
+    - PYTHON: "C:\\Python35-x64"
+      PYTHON_VERSION: "3.5.x"
+      PYTHON_ARCH: "64"
+      TOXENV: "py35"
+
+    - PYTHON: "C:\\Python36-x64"
+      PYTHON_VERSION: "3.6.x"
+      PYTHON_ARCH: "64"
+      TOXENV: "py36"
+
+    - PYTHON: "C:\\Python37-x64"
+      PYTHON_VERSION: "3.7.x"
+      PYTHON_ARCH: "64"
+      TOXENV: "py37"
+
+build: off
+
+install:
+  # If there is a newer build queued for the same PR, cancel this one.
+  # The AppVeyor 'rollout builds' option is supposed to serve the same
+  # purpose but it is problematic because it tends to cancel builds pushed
+  # directly to master instead of just PR builds (or the converse).
+  # credits: JuliaLang developers.
+  - SET PATH=%PYTHON%;%PYTHON%\Scripts;%path%
+  - python --version
+  - pip --version
+  - pip install -r requirements.txt
+  - pip install tox codecov
+  - pip list
+  - dir
+
+test_script: tox
+
+on_success: codecov

circle.yml

@@ -0,0 +1,78 @@
+version: 2.0
+
+jobs:
+  Py2:
+    docker:
+      - image: circleci/python:2.7
+    steps: &steps
+      - checkout
+
+      - run:
+          name: Install Packages
+          command: |
+            sudo apt-get update
+            sudo apt-get install pkg-config python-dev python-tk
+
+      - run:
+          name: Install PyPI dependences
+          command: |
+            pip install -r requirements.txt --user
+            sudo pip install coverage pytest pytest-cov codecov
+            python --version ; pip --version ; pwd ; ls
+
+      - run:
+          name: Testing
+          command: |
+            coverage run --source yolo3 -m py.test yolo3 scripts -v --doctest-modules --junitxml=test-reports/pytest_junit.xml
+            coverage report && coverage xml -o test-reports/coverage.xml
+            codecov
+
+      - run:
+          name: Sample Detection
+          command: |
+            export DISPLAY=""
+            # download and conver weights
+            wget -O ./model_data/tiny-yolo.weights  https://pjreddie.com/media/files/yolov3-tiny.weights  --progress=bar:force:noscroll
+            python ./scripts/convert_weights.py --config_path ./model_data/tiny-yolo.cfg --weights_path ./model_data/tiny-yolo.weights --output_path ./model_data/tiny-yolo.h5
+            mkdir ./results
+            # download sample image and video
+            wget -O ./results/dog.jpg https://raw.githubusercontent.com/pjreddie/darknet/master/data/dog.jpg
+            wget -O ./results/volleyball.mp4  https://d2v9y0dukr6mq2.cloudfront.net/video/preview/UnK3Qzg/crowds-of-poeple-hot-summer-day-at-wasaga-beach-ontario-canada-during-heatwave_n2t3d8trl__SB_PM.mp4
+            # run sample detections
+            python ./scripts/predict.py -w ./model_data/tiny-yolo.h5 -a ./model_data/tiny-yolo_anchors.csv --model_image_size 416 416 -c ./model_data/coco_classes.txt -o ./results -i ./results/dog.jpg -v ./results/volleyball.mp4
+            ls -l results/*
+
+      - run:
+          name: Sample Training
+          command: |
+            export DISPLAY=""
+            # download the dataset
+            wget -O ./model_data/VOCtrainval_2007.tar  http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
+            tar xopf ./model_data/VOCtrainval_2007.tar --directory ./model_data/
+            # prepare dataset for usage
+            python ./scripts/annotation_voc.py --path_dataset ./model_data/VOCdevkit --classes bicycle car person --sets 2007,train 2007,val --path_output ./model_data
+            # prepare very tlim training on 2 and 1 epoch
+            printf "image-size: [416, 416]\nbatch-size:\n  body: 4\n  fine: 4\nepochs:\n  body: 2\n  fine: 1\nvalid-split: 0.2\ngenerator:\n  augument: false\n  nb_threads: 1" > ./model_data/train_tiny-yolo_test.yaml
+            # cut the dataset size
+            python -c "lines = open('model_data/VOC_2007_val.txt', 'r').readlines(); open('model_data/VOC_2007_val.txt', 'w').writelines(lines[:250])"
+            # start the training
+            python ./scripts/train.py --path_dataset ./model_data/VOC_2007_val.txt --path_weights ./model_data/tiny-yolo.h5 --path_anchors ./model_data/tiny-yolo_anchors.csv --path_classes ./model_data/voc_classes.txt --path_output ./model_data --path_config ./model_data/train_tiny-yolo_test.yaml
+            # use the train model
+            python ./scripts/predict.py -w ./model_data/tiny-yolo_trained_final.h5 -a ./model_data/tiny-yolo_anchors.csv --model_image_size 416 416 -c ./model_data/voc_classes.txt -o ./results -i ./model_data/bike-car-dog.jpg
+            ls -l results/*
+
+      - store_test_results:
+          path: test-reports
+      - store_artifacts:
+          path: test-reports
+
+  Py3:
+    docker:
+      - image: circleci/python:3.6
+    steps: *steps
+
+workflows:
+  version: 2
+  build:
+    jobs:
+      - Py3