我们从Python开源项目中，提取了以下4个代码示例，用于说明如何使用imutils.is_cv2()。
def draw_text(self, image, text, size, color, position): ''' Zeichnet einen Text auf das Bild. Parameter --------- text : String Anzuzeigender Text size : Integer Groesse des Textes color : Tupel Farbe des Textes >> (255,0,0) position : Tupel Position des Textes >> (x,y) ''' if imutils.is_cv2(): cv2.putText(image, text, position, cv2.FONT_HERSHEY_COMPLEX, size, color, 2, cv2.CV_AA) elif imutils.is_cv3(): cv2.putText(image, text, position, cv2.FONT_HERSHEY_COMPLEX, size, color, 2, cv2.LINE_AA)
def extract_color_histogram(image, bins=(8, 8, 8)): # extract a 3D color histogram from the HSV color space using # the supplied number of `bins` per channel hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) hist = cv2.calcHist([hsv], [0, 1, 2], None, bins, [0, 180, 0, 256, 0, 256]) # handle normalizing the histogram if we are using OpenCV 2.4.X if imutils.is_cv2(): hist = cv2.normalize(hist) # otherwise, perform "in place" normalization in OpenCV 3 (I # personally hate the way this is done else: cv2.normalize(hist, hist) # return the flattened histogram as the feature vector return hist.flatten()
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
