Preparing for YoloV4 *WAITING-SUPPORT*

.gitignore

@@ -21,6 +21,8 @@ libraries/nitramite_alpr/
 models/coco.names
 models/yolov3.cfg
 models/yolov3.weights
+models/yolov4.cfg
+models/yolov4.weights
 models/car_CNN_13AUGM_CMCMCMCMF.h5py
 models/color_model.h5
 

README.md

@@ -82,7 +82,8 @@ Python side
 (Windows only)
 1. Download Python 3.6 ( https://www.python.org/ftp/python/3.6.0/python-3.6.0-amd64.exe ) 
 2. Install dependencies `pip install -r requirements_windows.txt`
-3. Run `Setup.py` OR Download <b>YOLOv3-608</b> weights, cfg, coco.names https://pjreddie.com/darknet/yolo/
+3. Run `Setup.py` OR Download cfg and weights from here https://github.com/AlexeyAB/darknet#pre-trained-models, cfg, coco.names https://pjreddie.com/darknet/yolo/
+Ensure that cfg file width and height is 608
 4. Extract weights, cfg and coco to `models` folder
 5. Download Postgresql server ( https://www.postgresql.org/ ) I am using version <b>11.6</b> but
 its also tested with version 12.

Setup.py

@@ -14,14 +14,14 @@
 urllib.request.urlretrieve(url, models_folder + 'coco.names')
 
 # Download cfg
-print('Download yolov3.cfg')
-url = 'https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg'
-urllib.request.urlretrieve(url, models_folder + 'yolov3.cfg')
+print('Download yolovX cfg')
+url = 'https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg'
+urllib.request.urlretrieve(url, models_folder + 'yolov4.cfg')
 
 # Download weights
-print('Download yolov3.weights')
-url = 'https://pjreddie.com/media/files/yolov3.weights'
-urllib.request.urlretrieve(url, models_folder + 'yolov3.weights')
+print('Download yolovX weights')
+url = 'https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights'
+urllib.request.urlretrieve(url, models_folder + 'yolov4.weights')
 
 # Completed
 print('Setup finished!')

module/object_detection.py

@@ -38,7 +38,7 @@ def filter_negative_values(array_boxes):
 def analyze_image(image_object, bool_move_processed):
     try:
         # Load Yolo
-        net = cv2.dnn.readNet(models_path + "yolov3.weights", models_path + "yolov3.cfg")
+        net = cv2.dnn.readNet(models_path + "yolov4.weights", models_path + "yolov4.cfg")
 
         classes = []
         with open(models_path + "coco.names", "r") as f:

requirements_windows.txt

@@ -1,7 +1,7 @@
 numpy==1.18.1
 matplotlib==3.1.2
 Pillow==6.2.2
-opencv-python==4.1.2.30
+opencv-python==4.4.0.44
 tensorflow==2.0.1
 tensorflow-gpu==2.0.1
 Keras==2.3.1