What is the meaning of the output of sensor_rate in this code?-CodePudding

_, img = cap.read() zeros_image = np.zeros((img.shape[0], img.shape[1], 1), np.uint8)

img_ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
blur = cv2.GaussianBlur(img_ycrcb, (11, 11), 0)

skin_ycrcb_min = np.array((0, 0, 140))
skin_ycrcb_max = np.array((65, 253, 255))
skin_ycrcb_min = np.array((0, 0, 180))
skin_ycrcb_max = np.array((140, 255, 255))
mask = cv2.inRange(blur, skin_ycrcb_min, skin_ycrcb_max)

contours, hierarchy = cv2.findContours(
    mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2:]
valid_cntrs = []
for i, cnt in enumerate(contours):
    x, y, w, h = cv2.boundingRect(cnt)
    area = cv2.contourArea(cnt)
    cX = int(x   (w/2))
    cY = int(y   (h/2))

    if (x <= 600) & (y >= 300) & (y <= 455) & (area > 900):
        valid_cntrs.append(cnt)
        cv2.putText(img, str(f'{cX},{cY}'), (cX, cY), cv2.FONT_HERSHEY_SIMPLEX,
                    1, (255, 0, 0), 2, cv2.LINE_AA)
        cv2.rectangle(zeros_image, (x, y), (x w, y h), (255),
                      thickness=cv2.FILLED)

mask1 = np.zeros((zeros_image.shape[0], zeros_image.shape[1], 1), np.uint8)
mask_result = cv2.bitwise_or(zeros_image, zeros_image, mask=Sensor1.mask)
white_cell_number = np.sum(mask_result == 255)
sensor_rate = white_cell_number/Sensor1.full_mask_area

sensor_rate prints out values between 0.85 to 31 or less than 0.85

CodePudding user response：

Original code:

    # detect whether there is car via bitwise_and
    mask1=np.zeros((zeros_image.shape[0],zeros_image.shape[1],1),np.uint8)
    mask_result=cv2.bitwise_or(zeros_image,zeros_image,mask=Sensor1.mask)
    white_cell_number=np.sum(mask_result==255)

    # detect to control whether car is passing under the red line sensor
    sensor_rate=white_cell_number/Sensor1.full_mask_area

white_cell_number is the number of white pixels in mask_result.

sensor_rate is the ratio of white pixels vs the number of total pixels in Sensor1.full_mask_area.

Looking further up int he code:

class Sensor:
    def __init__(self,kordinat1,kordinat2,frame_weight,frame_lenght):
        self.kordinat1=kordinat1
        self.kordinat2=kordinat2
        self.frame_weight=frame_weight
        self.frame_lenght =frame_lenght
        self.mask=np.zeros((frame_weight,frame_lenght,1),np.uint8)*abs(self.kordinat2.y-self.kordinat1.y)
        self.full_mask_area=abs(self.kordinat2.x-self.kordinat1.x)
        cv2.rectangle(self.mask,(self.kordinat1.x,self.kordinat1.y),(self.kordinat2.x,self.kordinat2.y),(255),thickness=cv2.FILLED)
        self.stuation=False
        self.car_number_detected=0


video=cv2.VideoCapture("video1.mp4")
ret,frame=video.read()
cropped_image= frame[0:450, 0:450]
fgbg=cv2.createBackgroundSubtractorMOG2()
Sensor1 = Sensor(
    Kordinat(1, cropped_image.shape[1] - 35),
    Kordinat(340, cropped_image.shape[1] - 30),
    cropped_image.shape[0],
    cropped_image.shape[1])

Sensor1.full_mask_area is a rectangle defined by two points.

So sensor_rate is the fill rate of that rectangle.