Question

软件：OpenCV，Python

你好，我正在一个项目中，尝试使用感兴趣的区域获取某些立方体的真实世界x，y坐标。我能够获得感兴趣的区域并找到对象的中心坐标。在我的情况下，卡住的部分是相对于感兴趣区域的中心坐标，这是一张白纸，大约8 1/4 x 11 3/4英寸。如何找到对象的真实世界中心坐标（如图所示）相对于感兴趣区域的大小？例如，坐标将以3,2返回，即在右侧x方向为3英寸，在y方向为2英寸。这只是一个原型，以后我将使用25cmx25cm的感兴趣区域。我已经附上了我的工作图片和代码以供参考。

谢谢

My Work

import cv2
import numpy as np

#find the center of an object
def center(contours):
# calculate moments for each contour
for c in contours:
    M = cv2.moments(c)
    if M["m00"] != 0:
        cX = int(M["m10"] / M["m00"])
        cY = int(M["m01"] / M["m00"])
    else:
        cX, cY = 0, 0

    tempStr = str(cX) + ", " + str(cY)
    cv2.circle(frame, (cX, cY), 1, (0, 0, 0), -1) #make a dot at the center of the object 
    cv2.putText(frame, tempStr, (cX - 25, cY - 25),cv2.FONT_HERSHEY_TRIPLEX, 0.4, (0, 0, 0), 1) #print the coordinates on the image


#get the region of interest
def find_ROI(frame):
    image = frame.copy()

    #change to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    #Get binary image 
    _, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

    #create structural element
    struc_ele = np.ones((5, 5), np.uint8)

    #Use Open Morphology
    img_open = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, struc_ele, iterations = 1)

    #find contours
    _, ctrs, _ = cv2.findContours(img_open.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # sort contours
    sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[1])

    for i, ctr in enumerate(sorted_ctrs):
        # Get bounding box
        x, y, w, h = cv2.boundingRect(ctr)

        # Getting ROI
        roi = image[y:y + h, x:x + w]

        # show ROI
        cv2.imshow('Region of Interest',roi)
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
    cv2.imshow('marked areas', image)

cap = cv2.VideoCapture(0)

if __name__ == "__main__":
    while True:
        _, frame = cap.read()
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        find_ROI(frame) 
        #Create kernel to use in filter
        kernel = np.ones((5, 5), np.uint8)

        #Create filter for yellow
        lower_yellow = np.array([15, 100, 100]) #Lower boundary values for HSV
        upper_yellow = np.array([30, 255, 255]) #Upper boundary values for HSV

        yellow_mask = cv2.inRange(hsv, lower_yellow, upper_yellow) # Threshold the HSV image to get only yellow colors
        opening_y = cv2.morphologyEx(yellow_mask, cv2.MORPH_OPEN, kernel, iterations = 2) #Use morphology open to rid of false pos and false neg (noise)
        result_y = cv2.bitwise_and(frame, frame, mask = opening_y) #bitwise and the opening filter with the original frame

        #Values for green 
        lower_green = np.array([45, 45, 10]) 
        upper_green = np.array([100, 255, 255])

        #Create filter for green
        green_mask = cv2.inRange(hsv, lower_green, upper_green)
        opening_g = cv2.morphologyEx(green_mask, cv2.MORPH_OPEN, kernel, iterations = 1)
        result_g = cv2.bitwise_and(frame, frame, mask = opening_g)

        #Tracking the color yellow
        _, contours, _ = cv2.findContours(opening_y, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        for y_pix, contour in enumerate (contours):
                area = cv2.contourArea (contour)
                if (area > 300):
                        center(contours)
                        x, y, w, h = cv2.boundingRect(contour)
                        frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 255), 2)
                        cv2.putText(frame, "Yellow", (x, y), cv2.FONT_HERSHEY_TRIPLEX, 0.7, (0, 255, 255))

        #Tracking the color green
        _, contours, _ = cv2.findContours(opening_g, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        for g_pix, contour in enumerate (contours):
                area = cv2.contourArea (contour)
                if (area > 300):
                        center(contours)
                        x, y, w, h = cv2.boundingRect(contour)
                        frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (87, 139, 46), 2)
                        cv2.putText(frame, "Green", (x, y), cv2.FONT_HERSHEY_TRIPLEX, 0.7, (87, 139, 46) )

        cv2.imshow("Detection and coordinates", frame)
        #cv2.imshow("Colors Seen ", res)



    key = cv2.waitKey(1)
    if key == 27:
        break

cap.release()
cv2.destroyAllWindows()

如何获得相对于感兴趣区域（ROI）的XY现实世界坐标？

0 个答案: