第一种方法的输出.rar_opencv_python opencv_提取前景_背景减除_视频背景减除

import numpy as np import cv2 class BackGroundSubtractor: # When constructing background subtractor, we # take in two arguments: # 1) alpha: The background learning factor, its value should # be between 0 and 1. The higher the value, the more quickly # your program learns the changes in the background. Therefore, # for a static background use a lower value, like 0.001. But if # your background has moving trees and stuff, use a higher value, # maybe start with 0.01. # 2) firstFrame: This is the first frame from the video/webcam. def __init__(self,alpha,firstFrame): self.alpha = alpha self.backGroundModel = firstFrame def getForeground(self,frame): # apply the background averaging formula: # NEW_BACKGROUND = CURRENT_FRAME * ALPHA + OLD_BACKGROUND * (1 - APLHA) self.backGroundModel = frame * self.alpha + self.backGroundModel * (1 - self.alpha) # after the previous operation, the dtype of # self.backGroundModel will be changed to a float type # therefore we do not pass it to cv2.absdiff directly, # instead we acquire a copy of it in the uint8 dtype # and pass that to absdiff. return cv2.absdiff(self.backGroundModel.astype(np.uint8),frame) cam = cv2.VideoCapture('C:/Users/heyuc/Desktop/111/input1.avi') c=1 # Just a simple function to perform # some filtering before any further processing. #def denoise(frame): # frame = cv2.medianBlur(frame,5) # frame = cv2.GaussianBlur(frame,(5,5),0) # return frame # Get the width and height of frame width = int(cam.get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5) height = int(cam.get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5) # Define the codec and create VideoWriter object fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use the lower case out = cv2.VideoWriter('output.mp4', fourcc, 20.0, (width, height)) ret,frame = cam.read() if ret is True: backSubtractor = BackGroundSubtractor(0.93,frame) run = True else: run = False while(run): # Read a frame from the camera ret,frame = cam.read() # If the frame was properly read. if ret is True: # Show the filtered image cv2.imshow('input',frame) # get the foreground foreGround = backSubtractor.getForeground(frame) # Apply thresholding on the background and display the resulting mask ret, mask = cv2.threshold(foreGround, 1.5, 255, cv2.THRESH_BINARY) # Note: The mask is displayed as a RGB image, you can # display a grayscale image by converting 'foreGround' to # a grayscale before applying the threshold. cv2.imshow('mask',mask) out.write(mask) cv2.imwrite('image/'+str(c)+'.jpg',mask) c=c+1 key = cv2.waitKey(1) & 0xFF else: break if key == 27: break cam.release() cv2.destroyAllWindows()