Update demo.py

demo.py

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+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+net = cv2.dnn.readNetFromDarknet("yolov3_training.cfg",r"yolov3_training_10000.weights")
+
+# Name custom object
+classes = ["Car","Van","Number Plate","Bicycle","MotorCycles","Lorry","HeavyLorry","Bus","Hand Tractors","Land Vehicle","JCB","Threewheel"]
+
+cap = cv2.VideoCapture("vehicle3.asf")
+
+while 1:
+    _, img = cap.read()
+    img = cv2.resize(img,(1280,720))
+    hight,width,_ = img.shape
+    blob = cv2.dnn.blobFromImage(img, 1/255,(416,416),(0,0,0),swapRB = True,crop= False)
+
+    net.setInput(blob)
+
+    output_layers_name = net.getUnconnectedOutLayersNames()
+
+    layerOutputs = net.forward(output_layers_name)
+
+    boxes =[]
+    confidences = []
+    class_ids = []
+
+    for output in layerOutputs:
+        for detection in output:
+            score = detection[5:]
+            class_id = np.argmax(score)
+            confidence = score[class_id]
+            if confidence > 0.7:
+                center_x = int(detection[0] * width)
+                center_y = int(detection[1] * hight)
+                w = int(detection[2] * width)
+                h = int(detection[3] * hight)
+                x = int(center_x - w/2)
+                y = int(center_y - h/2)
+                boxes.append([x,y,w,h])
+                confidences.append((float(confidence)))
+                class_ids.append(class_id)
+
+
+    indexes = cv2.dnn.NMSBoxes(boxes,confidences,.5,.4)
+
+    boxes =[]
+    confidences = []
+    class_ids = []
+
+    for output in layerOutputs:
+        for detection in output:
+            score = detection[5:]
+            class_id = np.argmax(score)
+            confidence = score[class_id]
+            if confidence > 0.5:
+                center_x = int(detection[0] * width)
+                center_y = int(detection[1] * hight)
+                w = int(detection[2] * width)
+                h = int(detection[3]* hight)
+
+                x = int(center_x - w/2)
+                y = int(center_y - h/2)
+
+
+
+                boxes.append([x,y,w,h])
+                confidences.append((float(confidence)))
+                class_ids.append(class_id)
+
+    indexes = cv2.dnn.NMSBoxes(boxes,confidences,.8,.4)
+    font = cv2.FONT_HERSHEY_PLAIN
+    colors = np.random.uniform(0,255,size =(len(boxes),3))
+    if  len(indexes)>0:
+        for i in indexes.flatten():
+            x,y,w,h = boxes[i]
+            label = str(classes[class_ids[i]])
+            confidence = str(round(confidences[i],2))
+            color = colors[i]
+            cv2.rectangle(img,(x,y),(x+w,y+h),color,2)
+            cv2.putText(img,label + " " + confidence, (x,y+400),font,2,color,2)
+
+    cv2.imshow('img',img)
+    if cv2.waitKey(1) == ord('q'):
+        break
+    
+cap.release()
+cv2.destroyAllWindows()
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