150 lines
5.5 KiB
Python
150 lines
5.5 KiB
Python
from Application.VideoReader import VideoReader
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from Application.Config import Config
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from threading import Thread, activeCount
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from multiprocessing import Queue, Process, Pool
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from multiprocessing.pool import ThreadPool
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from queue import Queue
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import imutils
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import time
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import cv2
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import numpy as np
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import os
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class ContourExtractor:
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# extracedContours = {frame_number: [(contour, (x,y,w,h)), ...], }
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# dict with frame numbers as keys and the contour bounds of every contour for that frame
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def getExtractedContours(self):
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return self.extractedContours
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def getExtractedMasks(self):
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return self.extractedMasks
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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.extractedMasks = 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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self.lastFrames = None
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self.averages = dict()
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print("ContourExtractor initiated")
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def extractContours(self):
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videoReader = VideoReader(self.config)
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self.fps = videoReader.getFPS()
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self.length = videoReader.getLength()
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videoReader.fillBuffer()
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threads = self.config["videoBufferLength"]
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self.start = time.time()
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# start a bunch of frames and let them read from the video reader buffer until the video reader reaches EOF
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with ThreadPool(2) as pool:
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while True:
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while not videoReader.videoEnded() and videoReader.buffer.qsize() == 0:
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time.sleep(0.5)
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tmpData = [videoReader.pop() for i in range(0, videoReader.buffer.qsize())]
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if videoReader.videoEnded():
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break
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pool.map(self.computeMovingAverage, (tmpData,))
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pool.map(self.async2, (tmpData,))
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# for data in tmpData:
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# self.getContours(data)
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frameCount = tmpData[-1][0]
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videoReader.thread.join()
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return self.extractedContours, self.extractedMasks
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def async2(self, tmpData):
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with ThreadPool(os.cpu_count()) as pool2:
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pool2.map(self.getContours, tmpData)
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def getContours(self, data):
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frameCount, frame = data
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# wait for the reference frame, which is calculated by averaging some revious frames
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while frameCount not in self.averages:
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time.sleep(0.1)
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firstFrame = self.averages.pop(frameCount, None)
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if frameCount % (10 * self.fps) == 1:
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print(
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f" \r \033[K {round((frameCount/self.fps)*100/self.length, 2)} % processed in {round(time.time() - self.start, 2)}s",
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end="\r",
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)
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gray = self.prepareFrame(frame)
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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=10)
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# cv2.imshow("changes x", thresh)
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# cv2.waitKey(10) & 0XFF
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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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masks = []
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for c in cnts:
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ca = cv2.contourArea(c)
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(x, y, w, h) = cv2.boundingRect(c)
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if ca < self.min_area or ca > self.max_area:
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continue
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contours.append((x, y, w, h))
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# the mask has to be packed like this, since np doesn't have a bit array,
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# meaning every bit in the mask would take up 8bits, which migth be too much
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masks.append(np.packbits(np.copy(thresh[y : y + h, x : x + w]), axis=0))
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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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self.extractedMasks[frameCount] = masks
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def prepareFrame(self, frame):
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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, (3, 3), 0)
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return gray
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def computeMovingAverage(self, frames):
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avg = []
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averageFrames = self.config["avgNum"]
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if frames[0][0] < averageFrames:
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frame = frames[0][1]
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frame = self.prepareFrame(frame)
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for j in range(0, len(frames)):
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frameNumber, _ = frames[j]
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self.averages[frameNumber] = frame
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# put last x frames into a buffer
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self.lastFrames = frames[-averageFrames:]
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return
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if self.lastFrames is not None:
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frames = self.lastFrames + frames
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tmp = [[j, frames, averageFrames] for j in range(averageFrames, len(frames))]
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with ThreadPool(int(os.cpu_count())) as pool:
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pool.map(self.averageDaFrames, tmp)
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self.lastFrames = frames[-averageFrames:]
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def averageDaFrames(self, dat):
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j, frames, averageFrames = dat
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frameNumber, frame = frames[j]
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frame = self.prepareFrame(frame)
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avg = frame / averageFrames
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for jj in range(0, averageFrames - 1):
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avg += self.prepareFrame(frames[j - jj][1]) / averageFrames
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self.averages[frameNumber] = np.array(np.round(avg), dtype=np.uint8)
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