我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用cv2.BORDER_DEFAULT。
def test_box_filter_reflect_101(self): I = np.array(range(1, 50)).reshape(7, 7).astype(np.float32) r = 2 ret1 = cv.smooth.box_filter(I, r, normalize=True) ret2 = cv2.blur(I, (5,5), borderType=cv2.BORDER_DEFAULT) self.assertTrue(np.array_equal(ret1, ret2))
def resize_to_desired(input_img): h = input_img.shape[0] if h < 20: # pad the image to 20 or 21 pixels height if it is too short border = math.ceil((20 - h) / 2) new_img = cv2.copyMakeBorder(input_img, top=border, bottom=border, left=0, right=0, borderType=cv2.BORDER_DEFAULT) else: new_img = input_img return cv2.resize(new_img, (CLS_IMG_WIDTH, CLS_IMG_HEIGHT))
def getRGBS(img, PLOT = False): image = cv2.cvtColor(img,cv2.COLOR_BGR2RGB) # grab the image channels, initialize the tuple of colors, # the figure and the flattened feature vector features = [] featuresSobel = [] Grayscale = cv2.cvtColor(img, cv2.cv.CV_BGR2GRAY) histG = cv2.calcHist([Grayscale], [0], None, [16], [0, 256]) histG = histG / histG.sum() features.extend(histG[:,0].tolist()) grad_x = np.abs(cv2.Sobel(Grayscale, cv2.CV_16S, 1, 0, ksize = 3, scale = 1, delta = 0, borderType = cv2.BORDER_DEFAULT)) grad_y = np.abs(cv2.Sobel(Grayscale, cv2.CV_16S, 0, 1, ksize = 3, scale = 1, delta = 0, borderType = cv2.BORDER_DEFAULT)) abs_grad_x = cv2.convertScaleAbs(grad_x) abs_grad_y = cv2.convertScaleAbs(grad_y) dst = cv2.addWeighted(abs_grad_x,0.5,abs_grad_y,0.5,0) histSobel = cv2.calcHist([dst], [0], None, [16], [0, 256]) histSobel = histSobel / histSobel.sum() features.extend(histSobel[:,0].tolist()) Fnames = [] Fnames.extend(["Color-Gray"+str(i) for i in range(8)]) Fnames.extend(["Color-GraySobel"+str(i) for i in range(8)]) return features, Fnames
def frame(image, top=2, bottom=2, left=2, right=2, borderType=cv.BORDER_CONSTANT, color=[255, 0, 0]): ''' add borders around :image: :param image: has to be in RBG color scheme. Use `convert_to_rgb` if it's in opencv BGR scheme. :param color: array representing an RGB color. :param borderType: Other options are: cv.BORDER_REFLECT, cv.BORDER_REFLECT_101, cv.BORDER_DEFAULT, cv.BORDER_REPLICATE, cv.BORDER_WRAP ''' return cv.copyMakeBorder(image, top, bottom, left, right, borderType, value=color)
def find_contours(img): ''' :param img: (numpy array) :return: all possible rectangles (contours) ''' img_blurred = cv2.GaussianBlur(img, (5, 5), 1) # remove noise img_gray = cv2.cvtColor(img_blurred, cv2.COLOR_BGR2GRAY) # greyscale image # cv2.imshow('', img_gray) # cv2.waitKey(0) # Apply Sobel filter to find the vertical edges # Find vertical lines. Car plates have high density of vertical lines img_sobel_x = cv2.Sobel(img_gray, cv2.CV_8UC1, dx=1, dy=0, ksize=3, scale=1, delta=0, borderType=cv2.BORDER_DEFAULT) # cv2.imshow('img_sobel', img_sobel_x) # Apply optimal threshold by using Oslu algorithm retval, img_threshold = cv2.threshold(img_sobel_x, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY) # cv2.imshow('s', img_threshold) # cv2.waitKey(0) # TODO: Try to apply AdaptiveThresh # Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on. # gaus_threshold = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 115, 1) # cv2.imshow('or', img) # cv2.imshow('gaus', gaus_threshold) # cv2.waitKey(0) # Define a stuctural element as rectangular of size 17x3 (we'll use it during the morphological cleaning) element = cv2.getStructuringElement(shape=cv2.MORPH_RECT, ksize=(17, 3)) # And use this structural element in a close morphological operation morph_img_threshold = deepcopy(img_threshold) cv2.morphologyEx(src=img_threshold, op=cv2.MORPH_CLOSE, kernel=element, dst=morph_img_threshold) # cv2.dilate(img_threshold, kernel=np.ones((1,1), np.uint8), dst=img_threshold, iterations=1) # cv2.imshow('Normal Threshold', img_threshold) # cv2.imshow('Morphological Threshold based on rect. mask', morph_img_threshold) # cv2.waitKey(0) # Find contours that contain possible plates (in hierarchical relationship) contours, hierarchy = cv2.findContours(morph_img_threshold, mode=cv2.RETR_EXTERNAL, # retrieve the external contours method=cv2.CHAIN_APPROX_NONE) # all pixels of each contour plot_intermediate_steps = False if plot_intermediate_steps: plot(plt, 321, img, "Original image") plot(plt, 322, img_blurred, "Blurred image") plot(plt, 323, img_gray, "Grayscale image", cmap='gray') plot(plt, 324, img_sobel_x, "Sobel") plot(plt, 325, img_threshold, "Threshold image") # plot(plt, 326, morph_img_threshold, "After Morphological filter") plt.tight_layout() plt.show() return contours
def run(self): bytes='' while not self.thread_cancelled: try: bytes+=self.stream.raw.read(1024) a = bytes.find('\xff\xd8') b = bytes.find('\xff\xd9') if a!=-1 and b!=-1: jpg = bytes[a:b+2] bytes= bytes[b+2:] img = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8),cv2.IMREAD_COLOR) # Convert BGR to HSV hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # define range of blue color in HSV #lower_blue = np.array([self.L_RED, self.L_GREEN, self.L_BLUE], np.uint8) #upper_blue = np.array([self.U_RED, self.U_GREEN, self.L_BLUE], np.uint8) # Threshold the HSV image to get only blue colors mask = cv2.inRange(hsv, np.array([53,187,37]), np.array([97,244,153])) # Bitwise-AND mask and original image res = cv2.bitwise_and(img,img, mask= mask) #### blurred = cv2.GaussianBlur(mask, (5, 5), 0) blurred = cv2.boxFilter(mask, 0, (7, 7), mask, (-1, -1), False, cv2.BORDER_DEFAULT) thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1] cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if imutils.is_cv2() else cnts[1] cv2.filterSpeckles(mask, 0, 100, 25) ## cv2.filterSpeckles(mask, 0, 50, 25) ## cv2.filterSpeckles(mask, 0, 100, 100) for c in cnts: M = cv2.moments(c) if int(M["m00"]) != 0: cX = int(M["m10"] / M["m00"]) cY = int(M["m01"] / M["m00"]) else: (cX, cY) = (0, 0) print(cX, cY) cv2.drawContours(res, [c], -1, (0, 255, 0), 2) cv2.circle(res, (cX, cY), 7, (255, 255, 255), 1) # table.putNumber("center X", cX) cv2.imshow('img',img) cv2.imshow('mask',mask) cv2.imshow('Final',res) cv2.imshow('cam',img) #sd.putNumber('Center X', cX) ##send the x value of the center #sd.putNumber('Center Y', cY) ##send the y value of the center ## print(sd.getNumber('Center Y'), sd.getNumber('Center X')) if cv2.waitKey(1) ==27: exit(0) except ThreadError: self.thread_cancelled = True
def run(self): bytes='' while not self.thread_cancelled: ####see lines 18, 80, 88 .... try: bytes+=self.stream.raw.read(1024) ##limit max bytes read in 1 itteration? need to read more on this a = bytes.find('\xff\xd8')##find start of stream of data b = bytes.find('\xff\xd9')##find our end of data stream if a!=-1 and b!=-1: ##so as long as we have a stream of data....do the following jpg = bytes[a:b+2] ##converts to image or a specific variable... bytes= bytes[b+2:] img = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8),cv2.IMREAD_COLOR) ##decode the data # Convert BGR to HSV hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) ##converting color format for easier proccessing/ math # define range of blue color in HSV #lower_blue = np.array([self.L_RED, self.L_GREEN, self.L_BLUE], np.uint8) #upper_blue = np.array([self.U_RED, self.U_GREEN, self.L_BLUE], np.uint8) # Threshold the HSV image to get only blue colors mask = cv2.inRange(hsv, np.array([53,187,37]), np.array([97,244,153])) ##get colors in the range of these HSV values # Bitwise-AND mask and original image res = cv2.bitwise_and(img,img, mask= mask) blurred = cv2.boxFilter(mask, 0, (7, 7), mask, (-1, -1), False, cv2.BORDER_DEFAULT) ##the next few line create outlines and thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1] ##remove any noise cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) #find countors cnts = cnts[0] if imutils.is_cv2() else cnts[1] cv2.filterSpeckles(mask, 0, 100, 25) ##remove speckles aka random dots and white noise for c in cnts: M = cv2.moments(c) if int(M["m00"]) != 0: ##Checks for division by zero cX = int(M["m10"] / M["m00"]) cY = int(M["m01"] / M["m00"]) else: (cX, cY) = (0, 0) cv2.drawContours(res, [c], -1, (0, 255, 0), 2) ##draw box/highlighting cv2.circle(res, (cX, cY), 7, (255, 255, 255), 1) ##draw box/highlighting ##Try-Catch for appending cX to table try: self.table.putNumber('centerX', cX) ##Adds cX to the networktables except KeyError: print("centerX failed.") cv2.imshow('img',img) ##display original image cv2.imshow('mask',mask) ##display masked image cv2.imshow('Final',res) ##show final image cv2.imshow('cam',img) ##see line 71/comments if cv2.waitKey(1) ==27: ##now we close if esc key is pressed exit(0) except ThreadError: self.thread_cancelled = True
