updating files

Author: Asadullah-Dal17

.gitignore

@@ -0,0 +1,2 @@
+*.mp4
+*.avi

README.md

@@ -1,14 +1,28 @@
 # Yolov4-opencv-python
 
-yolov4 object detection using opencv python, its simplest way to run inference on yolo
+Yolov4 object detection using opencv python, its simplest way to run inference on yolo V4
 
-implementation detail available on [_**Darknet**_](https://github.com/pjreddie/darknet)
+## YoloV4 objector Demo Video
+
+https://user-images.githubusercontent.com/66181793/122593546-41e6c980-d07f-11eb-8e18-bcc63d550183.mp4
 
 ---
 
-## Installation you need opencv-contrib-python
+Implementation detail available on [_**Darknet**_](https://github.com/pjreddie/darknet)
+
+All the Frozen-Inference graph (pre-trained network) available on Darknet
+
+---
+
+## TODO
+
+- [x] [**YoloV4 object detector with opencv-python implementation**](https://youtu.be/1aL6tewfxFY)
+
+- [ ] I will create distance estimation using yolo object detection, its coming up soon In sha Allah 😊
 
-[opencv contrib](https://pypi.org/project/opencv-contrib-python/)
+## Installation
+
+You Need [**Opencv Contrib**](https://pypi.org/project/opencv-contrib-python/)
 
 --> **windows**
 
@@ -18,9 +32,17 @@ implementation detail available on [_**Darknet**_](https://github.com/pjreddie/d
 
 `pip3 install opencv-contrib-python`
 
-then just clone this repository and you are good to go.
+### Clone this repo
+
+`git clone https://github.com/Asadullah-Dal17/yolov4-opencv-python`
+
+Guess what you are done that was not hard, isn't it.😉
+
+I have use tiny weights, check out more on [_Darknet_](https://github.com/pjreddie/darknet) github for more
+
+Check out my Complete Video Tutorial on Yolov4 object detection YouTube [**Video Tutorial**](https://youtu.be/1aL6tewfxFY)
+
 
-I have use tiny weights, check out more on darknet github for more
 
 - [x] I will create distance estimation using yolo object detection, its coming up soon In sha Allah 😊  
 Project Repository [Yolov4 Distance Estiamtion](https://github.com/Asadullah-Dal17/Yolov4-Detector-and-Distance-Estimator) 

output.avi

@@ -1,44 +1,45 @@
-import cv2
+import cv2 as cv
 import time
-
-CONFIDENCE_THRESHOLD = 0.2
-NMS_THRESHOLD = 0.4
-COLORS = [(0, 255, 255), (255, 255, 0), (0, 255, 0), (255, 0, 0)]
-
-class_names = []
-with open("classes.txt", "r") as f:
-    class_names = [cname.strip() for cname in f.readlines()]
-
-vc = cv2.VideoCapture(0)
-
-net = cv2.dnn.readNet("yolov4-tiny.weights", "yolov4-tiny.cfg")
-net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
-net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA_FP16)
-
-model = cv2.dnn_DetectionModel(net)
+Conf_threshold = 0.4
+NMS_threshold = 0.4
+COLORS = [(0, 255, 0), (0, 0, 255), (255, 0, 0),
+          (255, 255, 0), (255, 0, 255), (0, 255, 255)]
+
+class_name = []
+with open('classes.txt', 'r') as f:
+    class_name = [cname.strip() for cname in f.readlines()]
+# print(class_name)
+net = cv.dnn.readNet('yolov4-tiny.weights', 'yolov4-tiny.cfg')
+net.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA)
+net.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA_FP16)
+
+model = cv.dnn_DetectionModel(net)
 model.setInputParams(size=(416, 416), scale=1/255, swapRB=True)
 
-while cv2.waitKey(1) < 1:
-    (grabbed, frame) = vc.read()
-    if not grabbed:
-        exit()
-
-    start = time.time()
-    classes, scores, boxes = model.detect(
-        frame, CONFIDENCE_THRESHOLD, NMS_THRESHOLD)
-    end = time.time()
 
-    start_drawing = time.time()
+cap = cv.VideoCapture('output.avi')
+starting_time = time.time()
+frame_counter = 0
+while True:
+    ret, frame = cap.read()
+    frame_counter += 1
+    if ret == False:
+        break
+    classes, scores, boxes = model.detect(frame, Conf_threshold, NMS_threshold)
     for (classid, score, box) in zip(classes, scores, boxes):
         color = COLORS[int(classid) % len(COLORS)]
-        label = "%s : %f" % (class_names[classid[0]], score)
-        cv2.rectangle(frame, box, color, 2)
-        cv2.putText(frame, label, (box[0], box[1] - 10),
-                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-    end_drawing = time.time()
-
-    fps_label = "FPS: %.2f (excluding drawing time of %.2fms)" % (
-        1 / (end - start), (end_drawing - start_drawing) * 1000)
-    cv2.putText(frame, fps_label, (0, 25),
-                cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
-    cv2.imshow("detections", frame)
+        label = "%s : %f" % (class_name[classid[0]], score)
+        cv.rectangle(frame, box, color, 1)
+        cv.putText(frame, label, (box[0], box[1]-10),
+                   cv.FONT_HERSHEY_COMPLEX, 0.3, color, 1)
+    endingTime = time.time() - starting_time
+    fps = frame_counter/endingTime
+    # print(fps)
+    cv.putText(frame, f'FPS: {fps}', (20, 50),
+               cv.FONT_HERSHEY_COMPLEX, 0.7, (0, 255, 0), 2)
+    cv.imshow('frame', frame)
+    key = cv.waitKey(1)
+    if key == ord('q'):
+        break
+cap.release()
+cv.destroyAllWindows()

yolov4.py

@@ -0,0 +1,69 @@
+import cv2 as cv
+import time
+Conf_threshold = 0.6
+NMS_threshold = 0.4
+COLORS = [(0, 255, 0), (0, 0, 255), (255, 0, 0),
+          (255, 255, 0), (255, 0, 255), (0, 255, 255)]
+
+class_name = []
+with open('classes.txt', 'r') as f:
+    class_name = [cname.strip() for cname in f.readlines()]
+# print(class_name)
+net = cv.dnn.readNet('yolov4-tiny.weights', 'yolov4-tiny.cfg')
+net.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA)
+net.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA_FP16)
+
+model = cv.dnn_DetectionModel(net)
+model.setInputParams(size=(416, 416), scale=1/255, swapRB=True)
+
+
+cap = cv.VideoCapture('pexels-alex-pelsh-6896028.mp4')
+frame_width = cap.get(cv.CAP_PROP_FRAME_WIDTH)
+frame_height = cap.get(cv.CAP_PROP_FRAME_HEIGHT)
+
+fourcc = cv.VideoWriter_fourcc('M', 'J', 'P', 'G')
+# cap.set(cv.CAP_PROP_FPS, 7)
+dim = (int(frame_width/4), int(frame_height/4))
+print(dim)
+out = cv.VideoWriter('OutputVideo3.avi', fourcc, 30.0, dim)
+starting_time = time.time()
+frame_counter = 0
+while True:
+    ret, frame = cap.read()
+
+    frame_counter += 1
+    if ret == False:
+        break
+
+    # if frame_counter == 100:
+        # break
+
+    frame = cv.resize(frame, dim, interpolation=cv.INTER_AREA)
+    classes, scores, boxes = model.detect(frame, Conf_threshold, NMS_threshold)
+    for (classid, score, box) in zip(classes, scores, boxes):
+        color = COLORS[int(classid) % len(COLORS)]
+        label = "%s : %f" % (class_name[classid[0]], score)
+        cv.rectangle(frame, box, color, 1)
+        # cv.line(frame, (box[0]-3, box[1]-15),
+        #         (box[0]+110, box[1]-15), (0, 0, 0), 15)
+        cv.rectangle(frame, (box[0]-2, box[1]-20),
+                     (box[0]+120, box[1]-4), (100, 130, 100), -1)
+        cv.putText(frame, label, (box[0], box[1]-10),
+                   cv.FONT_HERSHEY_COMPLEX, 0.4, color, 1)
+    endingTime = time.time() - starting_time
+    fps = frame_counter/endingTime
+    # print(fps)
+    cv.line(frame, (18, 43), (140, 43), (0, 0, 0), 27)
+    cv.putText(frame, f'FPS: {round(fps,2)}', (20, 50),
+               cv.FONT_HERSHEY_COMPLEX, 0.7, (0, 255, 255), 2)
+    cv.imshow('frame', frame)
+
+    out.write(frame)
+    key = cv.waitKey(1)
+    if key == ord('q'):
+        break
+out.release()
+
+cap.release()
+cv.destroyAllWindows()
+print('done')