Update demo.py

demo.py

-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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