77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
import numpy as np
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import cv2
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cap = cv2.VideoCapture('recordings/test1.webm')
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ret, frame1 = cap.read()
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ret, frame2 = cap.read()
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while(cap.isOpened()):
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diff = cv2.absdiff(frame1, frame2)
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gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
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blur = cv2.GaussianBlur(gray, (5,5), 0)
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_, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
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dilated = cv2.dilate(thresh, None, iterations=3)
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contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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for contour in contours:
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(x, y, w, h) = cv2.boundingRect(contour)
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if cv2.contourArea(contour) < 9000:
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continue
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cv2.rectangle(frame1, (x, y), (x+w, y+h), (0, 255, 0), 2)
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cv2.putText(frame1, "Status: {}".format('Movement'), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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cv2.imshow("feed", frame1)
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frame1 = frame2
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ret, frame2 = cap.read()
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if cv2.waitKey(25) & 0xFF == ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows()
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# import numpy as np
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# import cv2
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# cap = cv2.VideoCapture('recordings/test1.webm')
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# while(cap.isOpened()):
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# # Capture frame-by-frame
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# ret, frame = cap.read()
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# # Our operations on the frame come here
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# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# # Display the resulting frame
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# cv2.imshow('frame',gray)
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# if cv2.waitKey(25) & 0xFF == ord('q'):
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# break
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# # When everything done, release the capture
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# cap.release()
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# cv2.destroyAllWindows()
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# from imageai.Detection import VideoObjectDetection
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# import os
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# execution_path = os.getcwd()
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# detector = VideoObjectDetection()
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# detector.setModelTypeAsYOLOv3()
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# detector.setModelPath( os.path.join(execution_path , "yolo.h5"))
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# detector.loadModel()
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# video_path = detector.detectObjectsFromVideo(input_file_path=os.path.join( execution_path, "recordings/traffic-mini.mp4"),
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# output_file_path=os.path.join(execution_path, "outputs/traffic_mini_detected_1")
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# , frames_per_second=29, log_progress=True)
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# print(video_path)
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