Delete RealTimeVehicleDetection.py

RealTimeVehicleDetection.py

-import cv2
-import numpy as np
-import time
-
-#Load YOLO
-net = cv2.dnn.readNet("yolov3.weights","yolov3.cfg") # Original yolov3
-#net = cv2.dnn.readNet("yolov3-tiny.weights","yolov3-tiny.cfg") #Tiny Yolo
-classes = []
-with open("coco.names","r") as f:
-    classes = [line.strip() for line in f.readlines()]
-
-print(classes)
-['person', 'bicycle', 'car', 'motorbike', 'aeroplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'sofa', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tvmonitor', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
-
-layer_names = net.getLayerNames()
-outputlayers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
-outputlayers
-
-colors= np.random.uniform(0,255,size=(len(classes),3))
-
-# loading image
-cap=cv2.VideoCapture("highway.mp4") #0 for 1st webcam
-#cap = cv2.VideoCapture(0)
-font = cv2.FONT_HERSHEY_PLAIN
-starting_time = time.time()
-frame_id = 0
-
-while True:
-    _, frame = cap.read()  #
-    frame_id += 1
-
-    height, width, channels = frame.shape
-    # detecting objects
-    blob = cv2.dnn.blobFromImage(frame, 0.00392, (320, 320), (0, 0, 0), True, crop=False)  # reduce 416 to 320
-
-    net.setInput(blob)
-    outs = net.forward(outputlayers)
-    # print(outs[1])
-
-    # Showing info on screen/ get confidence score of algorithm in detecting an object in blob
-    class_ids = []
-    confidences = []
-    boxes = []
-    for out in outs:
-        for detection in out:
-            scores = detection[5:]
-            class_id = np.argmax(scores)
-            confidence = scores[class_id]
-            if confidence > 0.3:
-                # onject detected
-                center_x = int(detection[0] * width)
-                center_y = int(detection[1] * height)
-                w = int(detection[2] * width)
-                h = int(detection[3] * height)
-
-                # cv2.circle(img,(center_x,center_y),10,(0,255,0),2)
-                # rectangle co-ordinaters
-                x = int(center_x - w / 2)
-                y = int(center_y - h / 2)
-                # cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
-
-                boxes.append([x, y, w, h])  # put all rectangle areas
-                confidences.append(
-                    float(confidence))  # how confidence was that object detected and show that percentage
-                class_ids.append(class_id)  # name of the object tha was detected
-
-    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.4, 0.6)
-
-    for i in range(len(boxes)):
-        if i in indexes:
-            x, y, w, h = boxes[i]
-            label = str(classes[class_ids[i]])
-            confidence = confidences[i]
-            color = colors[class_ids[i]]
-            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
-            cv2.putText(frame, label + " " + str(round(confidence, 2)), (x, y + 30), font, 1, (255, 255, 255), 2)
-
-    elapsed_time = time.time() - starting_time
-    fps = frame_id / elapsed_time
-    cv2.putText(frame, "FPS:" + str(round(fps, 2)), (10, 50), font, 2, (0, 0, 0), 1)
-
-    cv2.imshow("Image", frame)
-    key = cv2.waitKey(2)  # wait 1ms the loop will start again and we will process the next frame
-
-    if key == 27:  # esc key stops the process
-        break;
-
-cap.release()
-cv2.destroyAllWindows()
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