79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
import cv2
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import numpy as np
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import time
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# Load Yolo
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net = cv2.dnn.readNet("server-ia/data/modeles/yolov3-tiny/yolov3-tiny.weights", "server-ia/data/modeles/yolov3-tiny/yolov3-tiny.cfg")
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classes = []
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with open("server-ia/data/modeles/yolov3-tiny/coco.names", "r") as f:
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classes = [line.strip() for line in f.readlines()]
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layer_names = net.getLayerNames()
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output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
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colors = np.random.uniform(0, 255, size=(len(classes), 3))
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# Loading image
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cap = cv2.VideoCapture(0)
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font = cv2.FONT_HERSHEY_PLAIN
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starting_time = time.time()
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frame_id = 0
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while True:
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_, frame = cap.read()
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frame_id += 1
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height, width, channels = frame.shape
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# Detecting objects
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blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
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net.setInput(blob)
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outs = net.forward(output_layers)
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# Showing informations on the screen
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class_ids = []
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confidences = []
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boxes = []
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for out in outs:
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for detection in out:
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scores = detection[5:]
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class_id = np.argmax(scores)
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confidence = scores[class_id]
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if confidence > 0.5:
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# Object detected
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center_x = int(detection[0] * width)
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center_y = int(detection[1] * height)
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w = int(detection[2] * width)
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h = int(detection[3] * height)
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# Rectangle coordinates
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x = int(center_x - w / 2)
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y = int(center_y - h / 2)
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boxes.append([x, y, w, h])
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confidences.append(float(confidence))
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class_ids.append(class_id)
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indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.3)
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for i in range(len(boxes)):
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if i in indexes:
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x, y, w, h = boxes[i]
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label = str(classes[class_ids[i]])
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confidence = confidences[i]
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color = colors[class_ids[i]]
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cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
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cv2.putText(frame, label + " " + str(round(confidence, 2)), (x, y + 30), font, 3, color, 3)
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elapsed_time = time.time() - starting_time
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fps = frame_id / elapsed_time
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cv2.putText(frame, "FPS: " + str(round(fps, 2)), (10, 50), font, 4, (0, 0, 0), 3)
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cv2.imshow("Image", frame)
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key = cv2.waitKey(1)
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if key == 27:
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break
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cap.release()
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cv2.destroyAllWindows()
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