hi all
[C:\fakepath\캡처.PNG](/upfiles/15587715913520238.png)
i want to output blue circle and red circle's coordinates. (just (x, y) )
but i don't know how. :(
how to get coordinates?
I've already made tracking objects according to color. But I want to create coordinates for the object that I'm tracking
this is my code
import cv2 as cv
import numpy as np
color1 = 0
color2 = 0
ranges = 20
set_color = False
step = 0
def nothing(x):
global color1, color2
global lower_blueA1, lower_blueA2, lower_blueA3
global upper_blueA1, upper_blueA2, upper_blueA3
global lower_blueB1, lower_blueB2, lower_blueB3
global upper_blueB1, upper_blueB2, upper_blueB3
saturation_th1 = cv.getTrackbarPos('saturation_th1', 'img_result')
value_th1 = cv.getTrackbarPos('value_th1', 'img_result')
saturation_th2 = cv.getTrackbarPos('saturation_th2', 'img_result')
value_th2 = cv.getTrackbarPos('value_th2', 'img_result')
color1 = int(color1)
color2 = int(color2)
# HSV 색공간에서 마우스 클릭으로 얻은 픽셀값과 유사한 필셀값의 범위를 정합니다.
if color1 < ranges:
lower_blueA1 = np.array([color1 - ranges + 180, saturation_th1, value_th1])
upper_blueA1 = np.array([180, 255, 255])
lower_blueA2 = np.array([0, saturation_th1, value_th1])
upper_blueA2 = np.array([color1, 255, 255])
lower_blueA3 = np.array([color1, saturation_th1, value_th1])
upper_blueA3 = np.array([color1 + ranges, 255, 255])
# print(i-range+180, 180, 0, i)
# print(i, i+range)
elif color1 > 180 - ranges:
lower_blueA1 = np.array([color1, saturation_th1, value_th1])
upper_blueA1 = np.array([180, 255, 255])
lower_blueA2 = np.array([0, saturation_th1, value_th1])
upper_blueA2 = np.array([color1 + ranges - 180, 255, 255])
lower_blueA3 = np.array([color1 - ranges, saturation_th1, value_th1])
upper_blueA3 = np.array([color1, 255, 255])
# print(i, 180, 0, i+range-180)
# print(i-range, i)
else:
lower_blueA1 = np.array([color1, saturation_th1, value_th1])
upper_blueA1 = np.array([color1 + ranges, 255, 255])
lower_blueA2 = np.array([color1 - ranges, saturation_th1, value_th1])
upper_blueA2 = np.array([color1, 255, 255])
lower_blueA3 = np.array([color1 - ranges, saturation_th1, value_th1])
upper_blueA3 = np.array([color1, 255, 255])
# print(i, i+range)
# print(i-range, i)
if color2 < ranges:
lower_blueB1 = np.array([color2 - ranges + 180, saturation_th2, value_th2])
upper_blueB1 = np.array([180, 255, 255])
lower_blueB2 = np.array([0, saturation_th2, value_th2])
upper_blueB2 = np.array([color2, 255, 255])
lower_blueB3 = np.array([color2, saturation_th2, value_th2])
upper_blueB3 = np.array([color2 + ranges, 255, 255])
# print(i-range+180, 180, 0, i)
# print(i, i+range)
elif color2 > 180 - ranges:
lower_blueB1 = np.array([color2, saturation_th2, value_th2])
upper_blueB1 = np.array([180, 255, 255])
lower_blueB2 = np.array([0, saturation_th2, value_th2])
upper_blueB2 = np.array([color2 + ranges - 180, 255, 255])
lower_blueB3 = np.array([color2 - ranges, saturation_th2, value_th2])
upper_blueB3 = np.array([color2, 255, 255])
# print(i, 180, 0, i+range-180)
# print(i-range, i)
else:
lower_blueB1 = np.array([color2, saturation_th2, value_th2])
upper_blueB1 = np.array([color2 + ranges, 255, 255])
lower_blueB2 = np.array([color2 - ranges, saturation_th2, value_th2])
upper_blueB2 = np.array([color2, 255, 255])
lower_blueB3 = np.array([color2 - ranges, saturation_th2, value_th2])
upper_blueB3 = np.array([color2, 255, 255])
# print(i, i+range)
# print(i-range, i)
cv.namedWindow('img_color')
cv.namedWindow('img_result')
cv.createTrackbar('saturation_th1', 'img_result', 0, 255, nothing)
cv.setTrackbarPos('saturation_th1', 'img_result', 30)
cv.createTrackbar('value_th1', 'img_result', 0, 255, nothing)
cv.setTrackbarPos('value_th1', 'img_result', 30)
cv.createTrackbar('saturation_th2', 'img_result', 0, 255, nothing)
cv.setTrackbarPos('saturation_th2', 'img_result', 30)
cv.createTrackbar('value_th2', 'img_result', 0, 255, nothing)
cv.setTrackbarPos('value_th2', 'img_result', 30)
cap = cv.VideoCapture(1)
while(True):
ret,img_color = cap.read()
img_color = cv.flip(img_color, 1)
if ret == False:
continue;
img_color2 = img_color.copy()
img_hsv = cv.cvtColor(img_color2, cv.COLOR_BGR2HSV)
height, width = img_color.shape[:2]
cx = int(width / 2)
cy = int(height / 2)
if set_color == False:
rectangle_color = (0, 255, 0)
if step == 1:
rectangle_color = (0, 0, 255)
cv.rectangle(img_color, (cx - 20, cy - 20), (cx + 20, cy + 20), rectangle_color, 5)
else:
# 범위 값으로 HSV 이미지에서 마스크를 생성합니다.
img_maskA1 = cv.inRange(img_hsv, lower_blueA1, upper_blueA1)
img_maskA2 = cv.inRange(img_hsv, lower_blueA2, upper_blueA2)
img_maskA3 = cv.inRange(img_hsv, lower_blueA3, upper_blueA3)
temp = cv.bitwise_or(img_maskA1, img_maskA2)
img_maskA = cv.bitwise_or(img_maskA3, temp)
img_maskB1 = cv.inRange(img_hsv, lower_blueB1, upper_blueB1)
img_maskB2 = cv.inRange(img_hsv, lower_blueB2, upper_blueB2)
img_maskB3 = cv.inRange(img_hsv, lower_blueB3, upper_blueB3)
temp = cv.bitwise_or(img_maskB1, img_maskB2)
img_maskB = cv.bitwise_or(temp, img_maskB3)
# 모폴로지 연산
kernel = np.ones((11,11), np.uint8)
img_maskA = cv.morphologyEx(img_maskA, cv.MORPH_OPEN, kernel)
img_maskA = cv.morphologyEx(img_maskA, cv.MORPH_CLOSE, kernel)
kernel = np.ones((11,11), np.uint8)
img_maskB = cv.morphologyEx(img_maskB, cv.MORPH_OPEN, kernel)
img_maskB = cv.morphologyEx(img_maskB, cv.MORPH_CLOSE, kernel)
# 마스크 이미지로 원본 이미지에서 범위값에 해당되는 영상 부분을 획득합니다.
img_maskC = cv.bitwise_or(img_maskA, img_maskB)
img_result = cv.bitwise_and(img_color, img_color, mask=img_maskC)
# 라벨링
numOfLabelsA, img_labelA, statsA, centroidsA = cv.connectedComponentsWithStats(img_maskA)
for idx, centroid in enumerate(centroidsA):
if statsA[idx][0] == 0 and statsA[idx][1] == 0:
continue
if np.any(np.isnan(centroid)):
continue
x,y,width,height,area = statsA[idx]
centerX1,centerY1 = int(centroid[0]), int(centroid[1])
if area > 1500:
cv.circle(img_color, (centerX1, centerY1), 10, (0,0,255), 10)
cv.rectangle(img_color, (x,y), (x+width,y+height), (0,0,255))
numOfLabelsB, img_labelB, statsB, centroidsB = cv.connectedComponentsWithStats(img_maskB)
for idx, centroid in enumerate(centroidsB):
if statsB[idx][0] == 0 and statsB[idx][1] == 0:
continue
if np.any(np.isnan(centroid)):
continue
x,y,width,height,area = statsB[idx]
centerX2,centerY2 = int(centroid[0]), int(centroid[1])
if area > 1500:
cv.circle(img_color, (centerX2, centerY2), 10, (255,0,0), 10)
cv.rectangle(img_color, (x,y), (x+width,y+height), (255,0,0))
cv.imshow('img_result', img_result)
cv.imshow('img_color', img_color)
key = cv.waitKey(1) & 0xFF
if key == 27: # esc
break
elif key == 32: # space
if step == 0:
roi = img_color2[cy-20:cy+20, cx-20:cx+20]
roi = cv.medianBlur(roi, 3)
cv.imshow("roi1", roi)
hsv = cv.cvtColor(roi, cv.COLOR_BGR2HSV)
h,s,v = cv.split(hsv)
color1 = h.mean()
print(color1)
step += 1
elif step == 1:
roi = img_color2[cy-20:cy+20, cx-20:cx+20]
roi = cv.medianBlur(roi, 3)
cv.imshow("roi2", roi)
hsv = cv.cvtColor(roi, cv.COLOR_BGR2HSV)
h,s,v = cv.split(hsv)
color2 = h.mean()
set_color = True
nothing(0)
print(color2)
step += 1
cap.release()
cv.destroyAllWindows()
↧