131 lines
4.4 KiB
Python
131 lines
4.4 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 traceback
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import _thread
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import imageio
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import numpy as np
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from threading import Thread
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from multiprocessing import Queue, Process, Pool
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from multiprocessing.pool import ThreadPool
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import concurrent.futures
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from VideoReader import VideoReader
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from queue import Queue
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import threading
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from multiprocessing.pool import ThreadPool
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from Config import Config
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class ContourExtractor:
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#X = {frame_number: [(contour, (x,y,w,h)), ...], }
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def getextractedContours(self):
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return self.extractedContours
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def __init__(self, config):
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self.frameBuffer = Queue(16)
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self.extractedContours = dict()
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self.min_area = config["min_area"]
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self.max_area = config["max_area"]
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self.threashold = config["threashold"]
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self.resizeWidth = config["resizeWidth"]
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self.videoPath = config["inputPath"]
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self.xDim = 0
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self.yDim = 0
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self.config = config
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print("ContourExtractor initiated")
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def extractContours(self):
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extractedContours = dict()
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videoReader = VideoReader(self.config)
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self.xDim = videoReader.w
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self.yDim = videoReader.h
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videoReader.fillBuffer()
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frameCount, frame = videoReader.pop()
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#init compare image
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frame = imutils.resize(frame, width=self.resizeWidth)
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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#gray = np.asarray(gray[:,:,1]/2 + gray[:,:,2]/2).astype(np.uint8)
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gray = cv2.GaussianBlur(gray, (5, 5), 0)
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self.firstFrame = gray
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threads = 16
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start = time.time()
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with ThreadPool(threads) as pool:
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while not videoReader.videoEnded():
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#FrameCount, frame = videoReader.pop()
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if frameCount % (60*30) == 0:
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print(f"{frameCount/(60*30)} Minutes processed in {round((time.time() - start), 2)} each")
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start = time.time()
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if videoReader.buffer.qsize() == 0:
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time.sleep(.5)
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tmpData = [videoReader.pop() for i in range(0, videoReader.buffer.qsize())]
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frameCount = tmpData[-1][0]
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pool.map(self.getContours, tmpData)
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videoReader.thread.join()
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return self.extractedContours
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def getContours(self, data):
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frameCount, frame = data
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firstFrame = self.firstFrame
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frame = imutils.resize(frame, width=self.resizeWidth)
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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gray = cv2.GaussianBlur(gray, (5, 5), 0)
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frameDelta = cv2.absdiff(gray, firstFrame)
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thresh = cv2.threshold(frameDelta, self.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=4)
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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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contours = []
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for c in cnts:
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ca = cv2.contourArea(c)
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if ca < self.min_area or ca > self.max_area:
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continue
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(x, y, w, h) = cv2.boundingRect(c)
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contours.append((x, y, w, h))
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if len(contours) != 0 and contours is not None:
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# this should be thread safe
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self.extractedContours[frameCount] = contours
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def displayContours(self):
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values = self.extractedContours.values()
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for xx in values:
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for v1 in xx:
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(x, y, w, h) = v1[1]
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v = v1[0]
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frame = np.zeros(shape=[self.yDim, self.xDim, 3], dtype=np.uint8)
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frame = imutils.resize(frame, width=512)
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frame[y:y+v.shape[0], x:x+v.shape[1]] = v
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cv2.imshow("changes overlayed", frame)
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cv2.waitKey(10) & 0XFF
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cv2.destroyAllWindows()
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def exportContours(self):
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values = self.extractedContours.values()
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frames = []
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for xx in values:
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for v1 in xx:
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(x, y, w, h) = v1[1]
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v = v1[0]
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frame = np.zeros(shape=[self.yDim, self.xDim, 3], dtype=np.uint8)
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frame = imutils.resize(frame, width=512)
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frame[y:y+v.shape[0], x:x+v.shape[1]] = v
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frames.append(frame)
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return frames
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