Python cv2.absdiff方法代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def segment(image, threshold=25):
    global bg
    # find the absolute difference between background and current frame
    diff = cv2.absdiff(bg.astype("uint8"), image)

    # threshold the diff image so that we get the foreground
    thresholded = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)[1]

    # get the contours in the thresholded image
    (_, cnts, _) = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # return None, if no contours detected
    if len(cnts) == 0:
        return
    else:
        # based on contour area, get the maximum contour which is the hand
        segmented = max(cnts, key=cv2.contourArea)
        return (thresholded, segmented)

#-----------------
# MAIN FUNCTION
#----------------- 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def main():
	capture = cv2.VideoCapture(0)
	_, image = capture.read()
	previous = image.copy()
	
	
	while (cv2.waitKey(1) < 0):
		_, image = capture.read()
		diff = cv2.absdiff(image, previous)
		#image = cv2.flip(image, 3)
		#image = cv2.norm(image)
		_, diff = cv2.threshold(diff, 32, 0, cv2.THRESH_TOZERO)
		_, diff = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY)
		
		diff = cv2.medianBlur(diff, 5)
		
		cv2.imshow('video', diff)
		previous = image.copy()
		
	capture.release()
	cv2.destroyAllWindows() 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def prediction(self, image):
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        image = cv2.GaussianBlur(image, (21, 21), 0)
        if self.avg is None:
            self.avg = image.copy().astype(float)
        cv2.accumulateWeighted(image, self.avg, 0.5)
        frameDelta = cv2.absdiff(image, cv2.convertScaleAbs(self.avg))
        thresh = cv2.threshold(
                frameDelta, DELTA_THRESH, 255,
                cv2.THRESH_BINARY)[1]
        thresh = cv2.dilate(thresh, None, iterations=2)
        cnts = cv2.findContours(
                thresh.copy(), cv2.RETR_EXTERNAL,
                cv2.CHAIN_APPROX_SIMPLE)
        cnts = imutils.grab_contours(cnts)
        self.avg = image.copy().astype(float)
        return cnts 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def diff_frames(self,previous_frame,image):
        '''
        diff value for two value,
        determin if to excute the detection

        :param previous_frame:  RGB  array
        :param image:           RGB  array
        :return:                True or False
        '''
        if previous_frame is None:
            return True
        else:

            _diff = cv2.absdiff(previous_frame, image)

            diff=np.sum(_diff)/previous_frame.shape[0]/previous_frame.shape[1]/3.

            if diff>self.diff_thres:
                return True
            else:
                return False 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def diff_rect(img1, img2, pos=None):
    """find counters include pos in differences between img1 & img2 (cv2 images)"""
    diff = cv2.absdiff(img1, img2)
    diff = cv2.GaussianBlur(diff, (3, 3), 0)
    edges = cv2.Canny(diff, 100, 200)
    _, thresh = cv2.threshold(edges, 0, 255, cv2.THRESH_BINARY)
    contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
    if not contours:
        return None
    contours.sort(key=lambda c: len(c))
    # no pos provide, just return the largest different area rect
    if pos is None:
        cnt = contours[-1]
        x0, y0, w, h = cv2.boundingRect(cnt)
        x1, y1 = x0+w, y0+h
        return (x0, y0, x1, y1)
    # else the rect should contain the pos
    x, y = pos
    for i in range(len(contours)):
        cnt = contours[-1-i]
        x0, y0, w, h = cv2.boundingRect(cnt)
        x1, y1 = x0+w, y0+h
        if x0 <= x <= x1 and y0 <= y <= y1:
            return (x0, y0, x1, y1) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def get_match_confidence(img1, img2, mask=None):
    if img1.shape != img2.shape:
        return False
    ## first try, using absdiff
    # diff = cv2.absdiff(img1, img2)
    # h, w, d = diff.shape
    # total = h*w*d
    # num = (diff<20).sum()
    # print 'is_match', total, num
    # return num > total*0.90
    if mask is not None:
        img1 = img1.copy()
        img1[mask!=0] = 0
        img2 = img2.copy()
        img2[mask!=0] = 0
    ## using match
    match = cv2.matchTemplate(img1, img2, cv2.TM_CCOEFF_NORMED)
    _, confidence, _, _ = cv2.minMaxLoc(match)
    # print confidence
    return confidence 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def segment(image, threshold=25):
    global bg
    # find the absolute difference between background and current frame
    diff = cv2.absdiff(bg.astype("uint8"), image)

    # threshold the diff image so that we get the foreground
    thresholded = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)[1]

    # get the contours in the thresholded image
    (_, cnts, _) = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # return None, if no contours detected
    if len(cnts) == 0:
        return
    else:
        # based on contour area, get the maximum contour which is the hand
        segmented = max(cnts, key=cv2.contourArea)
        return (thresholded, segmented)

#--------------------------------------------------------------
# To count the number of fingers in the segmented hand region
#-------------------------------------------------------------- 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def matchAB(fileA, fileB):
    # 读取图像数据
    imgA = cv2.imread(fileA)
    imgB = cv2.imread(fileB)

    # 转换成灰色
    grayA = cv2.cvtColor(imgA, cv2.COLOR_BGR2GRAY)
    grayB = cv2.cvtColor(imgB, cv2.COLOR_BGR2GRAY)

    # 获取图片A的大小
    height, width = grayA.shape

    # 取局部图像，寻找匹配位置
    result_window = np.zeros((height, width), dtype=imgA.dtype)
    for start_y in range(0, height-100, 10):
        for start_x in range(0, width-100, 10):
            window = grayA[start_y:start_y+100, start_x:start_x+100]
            match = cv2.matchTemplate(grayB, window, cv2.TM_CCOEFF_NORMED)
            _, _, _, max_loc = cv2.minMaxLoc(match)
            matched_window = grayB[max_loc[1]:max_loc[1]+100, max_loc[0]:max_loc[0]+100]
            result = cv2.absdiff(window, matched_window)
            result_window[start_y:start_y+100, start_x:start_x+100] = result

    plt.imshow(result_window)
    plt.show() 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def segment(image, threshold=25):
    global bg
    # find the absolute difference between background and current frame
    diff = cv2.absdiff(bg.astype("uint8"), image)

    # threshold the diff image so that we get the foreground
    thresholded = cv2.threshold(diff,
                                threshold,
                                255,
                                cv2.THRESH_BINARY)[1]

    # get the contours in the thresholded image
    (cnts, _) = cv2.findContours(thresholded.copy(),
                                    cv2.RETR_EXTERNAL,
                                    cv2.CHAIN_APPROX_SIMPLE)

    # return None, if no contours detected
    if len(cnts) == 0:
        return
    else:
        # based on contour area, get the maximum contour which is the hand
        segmented = max(cnts, key=cv2.contourArea)
        return (thresholded, segmented) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def segment(image, threshold=25):
    global bg
    # find the absolute difference between background and current frame
    diff = cv2.absdiff(bg.astype("uint8"), image)

    # threshold the diff image so that we get the foreground
    thresholded = cv2.threshold(diff,
                                threshold,
                                255,
                                cv2.THRESH_BINARY)[1]

    # get the contours in the thresholded image
    (cnts, _) = cv2.findContours(thresholded.copy(),
                                 cv2.RETR_EXTERNAL,
                                 cv2.CHAIN_APPROX_SIMPLE)

    # return None, if no contours detected
    if len(cnts) == 0:
        return
    else:
        # based on contour area, get the maximum contour which is the hand
        segmented = max(cnts, key=cv2.contourArea)
        return (thresholded, segmented) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def background_subtraction(previous_frame, frame_resized_grayscale, min_area):
    """
    This function returns 1 for the frames in which the area
    after subtraction with previous frame is greater than minimum area
    defined.
    Thus expensive computation of human detection face detection
    and face recognition is not done on all the frames.
    Only the frames undergoing significant amount of change (which is controlled min_area)
    are processed for detection and recognition.
    """
    frameDelta = cv2.absdiff(previous_frame, frame_resized_grayscale)
    thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
    thresh = cv2.dilate(thresh, None, iterations=2)
    im2, cnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    temp = 0
    for c in cnts:
        # if the contour is too small, ignore it
        if cv2.contourArea(c) > min_area:
            temp = 1
    return temp 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def motion1(new_frame, base):
    motion = cv2.absdiff(base, new_frame)
    gray = cv2.cvtColor(motion, cv2.COLOR_BGR2GRAY)
    cv2.imshow('motion', gray)
    ret, motion_mask = cv2.threshold(gray, 25, 255, cv2.THRESH_BINARY_INV)

    blendsize = (3,3)
    kernel = np.ones(blendsize,'uint8')
    motion_mask = cv2.erode(motion_mask, kernel)

    # lots
    motion_mask /= 1.1429
    motion_mask += 16

    # medium
    #motion_mask /= 1.333
    #motion_mask += 32

    # minimal
    #motion_mask /= 2
    #motion_mask += 64

    cv2.imshow('motion1', motion_mask)
    return motion_mask 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def motion3(frame, counter):
    global last_frame
    global static_mask
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    if last_frame is None:
        pass
    else:
        diff = cv2.absdiff(gray, last_frame)
        cv2.imshow('motion3', diff)
        if static_mask is None:
            static_mask = np.float32(diff)
        else:
            if counter > 1000:
                c = float(1000)
            else:
                c = float(counter)
            f = float(c - 1) / c
            static_mask = f*static_mask + (1.0 - f)*np.float32(diff)
        mask_uint8 = np.uint8(static_mask)
        cv2.imshow('mask3', mask_uint8)
        ret, newmask = cv2.threshold(mask_uint8, 2, 255, cv2.THRESH_BINARY)
        cv2.imshow('newmask', newmask)
    last_frame = gray

# average of frames (the stationary stuff should be the sharpest) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def FMCenterSurroundDiff(self, GaussianMaps):
        dst = list()
        for s in range(2, 5):
            now_size = GaussianMaps[s].shape
            now_size = (now_size[1], now_size[0]) # (width, height)
            tmp = cv2.resize(GaussianMaps[s + 3], now_size, interpolation=cv2.INTER_LINEAR)
            nowdst = cv2.absdiff(GaussianMaps[s], tmp)
            dst.append(nowdst)
            tmp = cv2.resize(GaussianMaps[s + 4], now_size, interpolation=cv2.INTER_LINEAR)
            nowdst = cv2.absdiff(GaussianMaps[s], tmp)
            dst.append(nowdst)

        return dst


    # Constructing a Gaussian pyramid + taking center-surround differences 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import absdiff [as 别名]
def motionDetected(self, new_frame):
        frame = self.preprocessInputFrame(new_frame)

        gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
        gray = cv.GaussianBlur(gray, (21, 21), 0)

        if self.prevFrame is None:
            self.prevFrame = gray
            return False

        frameDiff = cv.absdiff(gray, self.prevFrame)

        # kernel = np.ones((5, 5), np.uint8)

        opening = cv.morphologyEx(frameDiff, cv.MORPH_OPEN, None)  # noqa
        closing = cv.morphologyEx(frameDiff, cv.MORPH_CLOSE, None)  # noqa

        ret1, th1 = cv.threshold(frameDiff, 10, 255, cv.THRESH_BINARY)

        height = np.size(th1, 0)
        width = np.size(th1, 1)

        nb = cv.countNonZero(th1)

        avg = (nb * 100) / (height * width)  # Calculate the average of black pixel in the image

        self.prevFrame = gray

        # cv.DrawContours(currentframe, self.currentcontours, (0, 0, 255), (0, 255, 0), 1, 2, cv.CV_FILLED)
        # cv.imshow("frame", current_frame)

        ret = avg > self.threshold   # If over the ceiling trigger the alarm

        if ret:
            self.updateMotionDetectionDts()

        return ret