有没有一种方法来减少一个值，而计数对象使用圆周率相机？

Question

我现在正在做一个大学项目，用圆周率上的相机计算物体。当检测到一个物体时，我需要每次检测到一个物体时将100计数减少1。我使用开放式简历，但我不需要相机饲料。当一个对象被拾取时，我需要将qtty_of_count的值减少1，然后这个值被发送到firebase数据库。qtty_of_count - 1的位置不正确吗？请帮忙。你知道吗

import datetime
import math
import cv2
import numpy as np

import firebase
##from firebase import firebase



# global variables
from firebase.firebase import FirebaseApplication

width = 0
height = 0
EntranceCounter = 0
ExitCounter = 0
min_area = 3000  # Adjust ths value according to your usage
_threshold = 70  # Adjust ths value according to your usage
OffsetRefLines = 150  # Adjust ths value according to your usage


# Check if an object in entering in monitored zone
def check_entrance_line_crossing(y, coor_y_entrance, coor_y_exit):
    abs_distance = abs(y - coor_y_entrance)

    if ((abs_distance <= 2) and (y < coor_y_exit)):
        return 1
    else:
        return 0


# Check if an object in exitting from monitored zone
def check_exit_line_crossing(y, coor_y_entrance, coor_y_exit):
    abs_distance = abs(y - coor_y_exit)

    if ((abs_distance <= 2) and (y > coor_y_entrance)):
        return 1
    else:
        return 0


camera = cv2.VideoCapture(0)

# force 640x480 webcam resolution
camera.set(3, 640)
camera.set(4, 480)

ReferenceFrame = None

# Frames may discard while adjusting to light
for i in range(0, 20):
    (grabbed, Frame) = camera.read()

while True:
    (grabbed, Frame) = camera.read()
    height = np.size(Frame, 0)
    width = np.size(Frame, 1)

    # if cannot grab a frame, this program ends here.
    if not grabbed:
        break

    # gray-scale and Gaussian blur filter applying
    GrayFrame = cv2.cvtColor(Frame, cv2.COLOR_BGR2GRAY)
    GrayFrame = cv2.GaussianBlur(GrayFrame, (21, 21), 0)

    if ReferenceFrame is None:
        ReferenceFrame = GrayFrame
        continue

    # Background subtraction and image manipulation
    FrameDelta = cv2.absdiff(ReferenceFrame, GrayFrame)
    FrameThresh = cv2.threshold(FrameDelta, _threshold, 255, cv2.THRESH_BINARY)[1]

    # Dilate image and find all the contours
    FrameThresh = cv2.dilate(FrameThresh, None, iterations=2)
    cnts, _ = cv2.findContours(FrameThresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    qtty_of_count =100

    # plot reference lines (entrance and exit lines)
    coor_y_entrance = (height // 2) - OffsetRefLines
    coor_y_exit = (height // 2) + OffsetRefLines
    cv2.line(Frame, (0, coor_y_entrance), (width, coor_y_entrance), (255, 0, 0), 2)
    cv2.line(Frame, (0, coor_y_exit), (width, coor_y_exit), (0, 0, 255), 2)

    # check all found count
    for c in cnts:
        # if a contour has small area, it'll be ignored
        if cv2.contourArea(c) < min_area:
            continue

        qtty_of_count = qtty_of_count - 1
        app = FirebaseApplication('https://appproject-d5d51.firebaseio.com/', None)
        update = app.put('/car', "spaces", qtty_of_count)
        print("Updated value in FB value: " + str(update))
        (x, y, w, h) = cv2.boundingRect(c)
        cv2.rectangle(Frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

        # find object's centroid
        coor_x_centroid = (x + x + w) // 2
        coor_y_centroid = (y + y + h) // 2
        ObjectCentroid = (coor_x_centroid, coor_y_centroid)
        cv2.circle(Frame, ObjectCentroid, 1, (0, 0, 0), 5)

        if (check_entrance_line_crossing(coor_y_centroid, coor_y_entrance, coor_y_exit)):
            EntranceCounter += 1

        if (check_exit_line_crossing(coor_y_centroid, coor_y_entrance, coor_y_exit)):
            ExitCounter += 1

print("Total countours found: " + str(qtty_of_count))

# Write entrance and exit counter values on frame and shows it
cv2.putText(Frame, "Entrances: {}".format(str(EntranceCounter)), (10, 50),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (250, 0, 1), 2)
cv2.putText(Frame, "Exits: {}".format(str(ExitCounter)), (10, 70),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow("Original Frame", Frame)
cv2.waitKey(1)

# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()

我需要qtty_of_count在每次检测到对象时减少1。非常感谢。你知道吗

Answer 1

除了@Kevin指出的问题之外，您的代码还在对您捕获的每一帧图像执行求值。如果你的物体在那里停留100帧，你的计数将变为零。你知道吗

要克服这一点，您应该标记图像中的每个对象，然后只计算新对象。这可以通过几种方法来实现（见kalman滤波跟踪），但是在没有发生情况的情况下，一个简单的解决方案可能是存储对象的x，y位置，并建立一个最大位置偏差来保持标签与该对象的距离。你知道吗