Python cv2.bitwise_and方法代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def contour_filter(self, frame):
        _, contours, _ = cv2.findContours(frame,
            cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        new_frame = np.zeros(frame.shape, np.uint8)
        for i, contour in enumerate(contours):
            c_area = cv2.contourArea(contour)
            if self.contour_min_area <= c_area <= self.contour_max_area:
                mask = np.zeros(frame.shape, np.uint8)
                cv2.drawContours(mask, contours, i, 255, cv2.FILLED)
                mask = cv2.bitwise_and(frame, mask)
                new_frame = cv2.bitwise_or(new_frame, mask)
        frame = new_frame

        if self.contour_disp_flag:
            frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
            cv2.drawContours(frame, contours, -1, (255, 0, 0), 1)

        return frame


    # A number of methods corresponding to the various trackbars available. 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def SMGetSalientRegion(self, src):
        # get a binarized saliency map
        binarized_SM = self.SMGetBinarizedSM(src)
        # GrabCut
        img = src.copy()
        mask = np.where(
            (binarized_SM != 0), cv2.GC_PR_FGD, cv2.GC_PR_BGD).astype('uint8')
        bgdmodel = np.zeros((1, 65), np.float64)
        fgdmodel = np.zeros((1, 65), np.float64)
        rect = (0, 0, 1, 1)  # dummy
        iterCount = 1
        cv2.grabCut(img, mask=mask, rect=rect, bgdModel=bgdmodel,
                    fgdModel=fgdmodel, iterCount=iterCount, mode=cv2.GC_INIT_WITH_MASK)
        # post-processing
        mask_out = np.where(
            (mask == cv2.GC_FGD) + (mask == cv2.GC_PR_FGD), 255, 0).astype('uint8')
        output = cv2.bitwise_and(img, img, mask=mask_out)
        return output 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def standard_test(self):
        for fnum in range(self.start_fnum, self.stop_fnum):
            frame = util.get_frame(self.capture, fnum)
            frame = frame[280:, :]
            frame_HSV = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

            mask = cv2.inRange(frame_HSV, (self.low_H, self.low_S, self.low_V),
                (self.high_H, self.high_S, self.high_V))

            res = cv2.bitwise_and(frame, frame, mask=mask)
            res_inv = cv2.bitwise_and(frame, frame, mask=cv2.bitwise_not(mask))

            cv2.imshow(self.window_name, mask)
            cv2.imshow('Video Capture AND', res)
            cv2.imshow('Video Capture INV', res_inv)

            if cv2.waitKey(30) & 0xFF == ord('q'):
                break


    # A number of methods corresponding to the various trackbars available. 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def remove_other_color(img):
    frame = cv2.GaussianBlur(img, (3,3), 0) 
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    # define range of blue color in HSV
    lower_blue = np.array([100,128,0])
    upper_blue = np.array([215,255,255])
    # Threshold the HSV image to get only blue colors
    mask_blue = cv2.inRange(hsv, lower_blue, upper_blue)

    lower_white = np.array([0,0,128], dtype=np.uint8)
    upper_white = np.array([255,255,255], dtype=np.uint8)
    # Threshold the HSV image to get only blue colors
    mask_white = cv2.inRange(hsv, lower_white, upper_white)

    lower_black = np.array([0,0,0], dtype=np.uint8)
    upper_black = np.array([170,150,50], dtype=np.uint8)

    mask_black = cv2.inRange(hsv, lower_black, upper_black)

    mask_1 = cv2.bitwise_or(mask_blue, mask_white)
    mask = cv2.bitwise_or(mask_1, mask_black)
    # Bitwise-AND mask and original image
    #res = cv2.bitwise_and(frame,frame, mask= mask)
    return mask 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def _pre_process_input_minimal(self, img, mask, t, darker_fg=True):
        if self._buff_grey is None:
            self._buff_grey = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
            if mask is None:
                mask = np.ones_like(self._buff_grey) * 255

        cv2.cvtColor(img,cv2.COLOR_BGR2GRAY, self._buff_grey)

        cv2.erode(self._buff_grey, self._erode_kern, dst=self._buff_grey)

        if darker_fg:
            cv2.subtract(255, self._buff_grey, self._buff_grey)


        if mask is not None:
            cv2.bitwise_and(self._buff_grey, mask, self._buff_grey)
            return self._buff_grey 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def backprojection(target, roihist):
    '''图像预处理'''
    hsvt = cv2.cvtColor(target,cv2.COLOR_BGR2HSV)
    dst = cv2.calcBackProject([hsvt],[0,1],roihist,[0,180,0,256],1)
    # Now convolute with circular disc
    disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(7,7))
    cv2.filter2D(dst,-1,disc,dst)
    # threshold and binary AND
    ret,binary = cv2.threshold(dst,80,255,0)
    # 创建 核
    kernel = np.ones((5,5), np.uint8)
    iter_time = 1
    # 闭运算
    binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel,iterations=iter_time)

    thresh = cv2.merge((binary,binary,binary))
    target_filter = cv2.bitwise_and(target,thresh)
    
    return binary, target_filter 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def getSignature(img):
    imgSize = np.shape(img)

    gImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Adaptive Thresholding requires the blocksize to be odd and bigger than 1
    blockSize = 1 / 8 * imgSize[0] / 2 * 2 + 1
    if blockSize <= 1:
        blockSize = imgSize[0] / 2 * 2 + 1
    const = 10

    mask = cv2.adaptiveThreshold(gImg, maxValue = 255, adaptiveMethod = cv2.ADAPTIVE_THRESH_MEAN_C, thresholdType = cv2.THRESH_BINARY, blockSize = blockSize, C = const)
    rmask = cv2.bitwise_not(mask)

    return (cv2.bitwise_and(img, img, mask=rmask), rmask)

# First Prompt 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def diffImg(self, t0, t1, t2):
        d1 = cv.absdiff(t2, t1)
        d2 = cv.absdiff(t1, t0)
        return cv.bitwise_and(d1, d2) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def handsegment(frame):
    lower, upper = boundaries[0]
    lower = np.array(lower, dtype="uint8")
    upper = np.array(upper, dtype="uint8")
    mask1 = cv2.inRange(frame, lower, upper)

    lower, upper = boundaries[1]
    lower = np.array(lower, dtype="uint8")
    upper = np.array(upper, dtype="uint8")
    mask2 = cv2.inRange(frame, lower, upper)

    # for i,(lower, upper) in enumerate(boundaries):
    # 	# create NumPy arrays from the boundaries
    # 	lower = np.array(lower, dtype = "uint8")
    # 	upper = np.array(upper, dtype = "uint8")

    # 	# find the colors within the specified boundaries and apply
    # 	# the mask
    # 	if(i==0):
    # 		print "Harish"
    # 		mask1 = cv2.inRange(frame, lower, upper)
    # 	else:
    # 		print "Aadi"
    # 		mask2 = cv2.inRange(frame, lower, upper)
    mask = cv2.bitwise_or(mask1, mask2)
    output = cv2.bitwise_and(frame, frame, mask=mask)
    # show the images
    # cv2.imshow("images", mask)
    # cv2.imshow("images", output)
    return output 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def remove_bg(frame):
    fg_mask=bg_model.apply(frame)
    kernel = np.ones((3,3),np.uint8)
    fg_mask=cv2.erode(fg_mask,kernel,iterations = 1)
    frame=cv2.bitwise_and(frame,frame,mask=fg_mask)
    return frame

# ------------------------ BEGIN ------------------------ #

# Camera 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def diff_remove_bg(img0, img, img1):  # removes the background but requires three images
    d1 = diff(img0, img)
    d2 = diff(img, img1)
    return cv2.bitwise_and(d1, d2)


# img1=cv2.imread('subtract1.jpg') 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def motion_detection(camera, folder, until):
    """
    Uses 3 frames to look for motion, can't remember where
    I found it but it gives better result than my first try
    with comparing 2 frames.
    """
    utils.clear_directory(folder)

    # Need to get 2 images to start with
    previous_image = cv2.cvtColor(camera.read()[1], cv2.cv.CV_RGB2GRAY)
    current_image = cv2.cvtColor(camera.read()[1], cv2.cv.CV_RGB2GRAY)
    purple = (140, 25, 71)

    while True:
        now = datetime.datetime.now()
        _, image = camera.read()
        gray_image = cv2.cvtColor(image, cv2.cv.CV_RGB2GRAY)

        difference1 = cv2.absdiff(previous_image, gray_image)
        difference2 = cv2.absdiff(current_image, gray_image)
        result = cv2.bitwise_and(difference1, difference2)

        # Basic threshold, turn the bitwise_and into a black or white (haha)
        # result, white (255) being a motion
        _, result = cv2.threshold(result, 40, 255, cv2.THRESH_BINARY)

        # Let's show a square around the detected motion in the original pic
        low_point, high_point = utils.find_motion_boundaries(result.tolist())
        if low_point is not None and high_point is not None:
            cv2.rectangle(image, low_point, high_point, purple, 3)
            print 'Motion detected ! Taking picture'
            utils.save_image(image, folder, now)

        previous_image = current_image
        current_image = gray_image

        if utils.time_over(until, now):
            break

    del(camera) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def roi(img, vertices):
    
    #blank mask:
    mask = np.zeros_like(img)   
    
    #filling pixels inside the polygon defined by "vertices" with the fill color    
    cv2.fillPoly(mask, vertices, 255)
    
    #returning the image only where mask pixels are nonzero
    masked = cv2.bitwise_and(img, mask)
    return masked 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def roi(img, vertices):
    mask = np.zeros_like(img)
    cv2.fillPoly(mask, vertices, 255)
    masked = cv2.bitwise_and(img, mask)
    return masked 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import bitwise_and [as 别名]
def preprocessing_image(self, frame):
        height, width = frame.shape[:2]
        mask = np.zeros((height, width), np.uint8)

        cv2.rectangle(mask, (width / 3 + 10, height / 3 - 130),
                      (width * 2 / 3 + 10, height * 2 / 3 - 10), [255, 255, 255], thickness=-1)

        masked_img = cv2.bitwise_and(frame, frame, mask=mask)
        return masked_img