我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用imutils.video()。
def rotating_example(): img = cv2.imread('./data/hi.jpg') vwriter = VideoWriterRGB('hi-video.avi') frames = 0 for angle in range(0, 360, 5): rot = imutils.rotate(img, angle=angle) cv2.imshow("Angle = %d" % (angle), rot) vwriter.addFrame(rot) frames += 1 for angle in range(360, 0, -5): rot = imutils.rotate(img, angle=angle) cv2.imshow("Angle = %d" % (angle), rot) vwriter.addFrame(rot) frames += 1 vwriter.finalise() print("Created movie with %d frames" % frames)
def __init__(self, output='video.avi', fps=20, codec=["M", "J", "P", "G"]): self.output = output self.fps = fps self.codec = codec self.fourcc = cv2.VideoWriter_fourcc(*self.codec) self.writer = None (self.h, self.w) = (None, None) self.zeros = None
def addFrame(self, frame, width=300): frame = imutils.resize(frame, width) # check if the writer is None if self.writer is None: # store the image dimensions, initialzie the video writer, # and construct the zeros array (self.h, self.w) = frame.shape[:2] self.writer = cv2.VideoWriter(self.output, self.fourcc, self.fps, (self.w * 2, self.h * 2), True) self.zeros = np.zeros((self.h, self.w), dtype="uint8") # break the image into its RGB components, then construct the # RGB representation of each frame individually (B, G, R) = cv2.split(frame) R = cv2.merge([self.zeros, self.zeros, R]) G = cv2.merge([self.zeros, G, self.zeros]) B = cv2.merge([B, self.zeros, self.zeros]) # construct the final output frame, storing the original frame # at the top-left, the red channel in the top-right, the green # channel in the bottom-right, and the blue channel in the # bottom-left output = np.zeros((self.h * 2, self.w * 2, 3), dtype="uint8") output[0:self.h, 0:self.w] = frame output[0:self.h, self.w:self.w * 2] = R output[self.h:self.h * 2, self.w:self.w * 2] = G output[self.h:self.h * 2, 0:self.w] = B # write the output frame to file self.writer.write(output)
def __init__(self, output='video.avi', fps=20, codec=["M", "J", "P", "G"]): self.output = output self.fps = fps self.codec = codec self.fourcc = cv2.VideoWriter_fourcc(*self.codec) self.writer = None (self.h, self.w) = (None, None)
def addFrame(self, frame, width=600): frame = imutils.resize(frame, width) # check if the writer is None if self.writer is None: # store the image dimensions, initialzie the video writer, (self.h, self.w) = frame.shape[:2] self.writer = cv2.VideoWriter(self.output, self.fourcc, self.fps, (self.w, self.h), True) # write the output frame to file self.writer.write(frame)
def __init__(self, src=0): # initialize the video camera stream and read the first frame # from the stream self.stream = cv2.VideoCapture(src) (self.grabbed, self.frame) = self.stream.read() # initialize the variable used to indicate if the thread should # be stopped self.stopped = False
def start(self): # start the thread to read frames from the video stream Thread(target=self.update, args=()).start() return self
def update ( self ): cap = self.capture.read( ) # Capture the frame from the webcam show_cap = cap.copy() # Render needed video outputs if self.settings["hsv"]: hsv = cv2.cvtColor( cap, cv2.COLOR_BGR2HSV ) self.show( 'hsv', hsv ) if self.track: # if the program should track an item for k, v in self.limits.items( ): # Cycle through each item in the limits if k in self.settings["filters"]: # if the value is in the filters given, v[0] = self.get_upper_trackbar( k ) # set the upper to the trackbar value v[1] = self.get_lower_trackbar( k ) # set the lower to the trackbar value self.get_bounding_rect( k, cap, show_cap, k, v[0], v[1] ) # Get the bounding rect of the capture with limits if self.settings["original"]: self.show( 'original', show_cap )
def init_vision(self): self.set_default_context() # read cascades self.log.info("reading haar cascades") self.face_cascade = cv2.CascadeClassifier(dirname(__file__) + '/cascades/haarcascade_frontalface_alt2.xml') self.smile_cascade = cv2.CascadeClassifier(dirname(__file__) + '/cascades/haarcascade_smile.xml') self.leye_cascade = cv2.CascadeClassifier(dirname(__file__) + '/cascades/haarcascade_lefteye.xml') self.reye_cascade = cv2.CascadeClassifier(dirname(__file__) + '/cascades/haarcascade_righteye.xml') self.profileface_cascade = cv2.CascadeClassifier(dirname(__file__) + '/cascades/lbpcascade_profileface.xml') #######video streaming and fps ###########33 self.log.info("initializing videostream") self.vs = eye(src=0).start() self.fps = FPS().start() # TODO distance from cam self.rect = (161, 79, 150, 150) # random initial guess for foreground/face position for background extraction self.distance = 0 # initialize the known distance from the camera to the reference eye pic, which # in this case is 50cm from computer screen self.KNOWN_DISTANCE = 50.0 # initialize the known object width, which in this case, the human eye is 24mm width average self.KNOWN_WIDTH = 2.4 # initialize the reference eye image that we'll be using self.REF_IMAGE_PATH = dirname(__file__) + "/ref/eye.jpg" # load the image that contains an eye that is KNOWN TO BE 50cm # from our camera, then find the eye marker in the image, and initialize # the focal length image = cv2.imread(self.REF_IMAGE_PATH) self.radius = 24 # initialize radius for distance counter marker = self.find_eye_radius(image) # calculate camera focallenght self.focalLength = (marker * self.KNOWN_DISTANCE) / self.KNOWN_WIDTH # TODO read from config file # bools self.showfeed = True self.showboundingboxes = True self.showdetected = True ######## detected objects ###### self.detectedfaces = [] self.detectedmoods = [] # process first frame self.feed = self.process_frame() self.awake = True if self.awake: pass # TODO start vision thread self.filter = "None"
def tracking (vstate): print vstate #The vehicle process images and maintains all data ready to fly in the following state. #However, it does not send attitude messages as the vehicle is still under manual control. red1Good = red2Good = False # Set True when returned target offset is reliable. bearing = offset = 0 target = None # Initialise tuple returned from video stream # Initialise the FPS counter. #fps = FPS().start() while vstate == "tracking": # grab the frame from the threaded video stream and return left line offset # We do this to know if we have a 'lock' (goodTarget) as we come off of manual control. target = vs.readfollow() bearing = target[0] red1Good = target[1] offset = target[2] red2Good = target[3] # Print location information for `vehicle` in all frames (default printer) #print "Global Location: %s" % vehicle.location.global_frame #print "Global Location (relative altitude): %s" % vehicle.location.global_relative_frame #print "Local Location: %s" % vehicle.location.local_frame #NEDprint "Local Location: %s" % vehicle.location.local_frame # print tof_sensor.get_distance() # update the FPS counter #fps.update() # Check if operator has transferred to autopilot using TX switch. if vehicle.mode == "GUIDED_NOGPS": # print "In Guided mode..." if ( red1Good and red2Good ) == True: # print "In guided mode, setting following..." vstate = "following" else: # print "In guided mode, setting lost..." vstate = "lost" # stop the timer and display FPS information #fps.stop() #print('Elasped time in tracking state: {:.2f}'.format(fps.elapsed())) #print('Approx. FPS: {:.2f}'.format(fps.fps())) # time.sleep(1) return vstate #-------------- FUNCTION DEFINITION TO FLY IN VEHICLE STATE FOLLOWING---------------------
