91 lines
2.6 KiB
Python
91 lines
2.6 KiB
Python
from imutils.video import VideoStream
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import argparse
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import datetime
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import imutils
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import time
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import cv2
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import os
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import numpy as np
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import traceback
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import _thread
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class ContourExtractor:
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#X = {frame_number: contours, }
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extractedContours = dict()
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def __init__(self, videoPath):
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print("ContourExtractror initiated")
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print(videoPath)
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min_area = 100
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max_area = 30000
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threashold = 10
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# initialize the first frame in the video stream
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vs = cv2.VideoCapture(videoPath)
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res = vs.read()[0]
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firstFrame = None
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# loop over the frames of the video
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frameCount = 0
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while res:
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res, frame = vs.read()
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# resize the frame, convert it to grayscale, and blur it
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if frame is None:
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return
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#frame = imutils.resize(frame, width=500)
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cv2.imshow( "frame", frame )
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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gray = cv2.GaussianBlur(gray, (31, 31), 0)
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# if the first frame is None, initialize it
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if firstFrame is None:
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firstFrame = gray
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continue
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frameDelta = cv2.absdiff(gray, firstFrame)
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thresh = cv2.threshold(frameDelta, threashold, 255, cv2.THRESH_BINARY)[1]
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# dilate the thresholded image to fill in holes, then find contours
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thresh = cv2.dilate(thresh, None, iterations=3)
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cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cnts = imutils.grab_contours(cnts)
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# loop over the contours
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for c in cnts:
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if cv2.contourArea(c) < min_area or cv2.contourArea(c) > max_area:
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continue
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(x, y, w, h) = cv2.boundingRect(c)
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cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
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text = "Occupied"
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self.extractedContours[frameCount] = cnts
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frameCount += 1
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#cv2.imshow( "annotated", frame )
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#cv2.waitKey(10) & 0XFF
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def displayContours(self):
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values = self.extractedContours.values()
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frame = np.zeros(shape=[1080, 1920, 3], dtype=np.uint8)
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#frame = imutils.resize(frame, width=500)
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for x in values:
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for v in x:
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(x, y, w, h) = cv2.boundingRect(v)
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cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
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cv2.imshow("changes overlayed", frame)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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