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asked 2019-05-25 03:10:12 -0600

porori0407 gravatar image

i want to output coordinates.

hi all

C:\fakepath\캡처.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()

click to hide/show revision 2
None

updated 2019-05-25 03:29:04 -0600

sturkmen gravatar image

i want to output coordinates.

hi all

C:\fakepath\캡처.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
 
np
color1 = 0
color2 = 0
 
0
ranges = 20
set_color = False
step = 0
 
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
 
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.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)
 
30)
cap = cv.VideoCapture(1)
 
while(True):
 
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()

  
cap.release()
cv.destroyAllWindows()
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