How to determine the length of contours?

asked 2018-05-07 05:50:29 -0500

Norb.ert gravatar image

Hello,

I'm actually working on a webcam software with which I will control a UR co-bot to pick and place objects about forms and colors. I'm working with Python and OpenCV, I managed to recognize the shapes with the findcountor and drawcontour function, and I sort them with the approxPolyDP function. Now I would like to determine for instance the length of each side of a rectangle and then calculate the middle of a side and that point will be sent to the robot to catch it with the gripper. I tried a lot of things and I searched on a lot of forums but I didn't got any usable information, if you have any idea please let me know.

Here is my code: mport cv2 import numpy as np import imutils from shapeDetector import ShapeDetector import shapely.geometry as shapgeo

lower = {'red':(169, 100, 100), 'blue':(108, 100, 100), 'yellow':(18, 100, 100), 'green':(59, 100, 100)}

upper = {'red':(189, 255, 255), 'blue':(128, 255, 255), 'yellow':(54, 255, 255), 'green':(79, 255, 255)}

colors = {'red':(0,0,255), 'blue':(255,0,0), 'yellow':(38, 255, 255), 'green':(0, 255, 0)}

camera = cv2.VideoCapture(0)

def drawContours(frame,c):

(x,y,w,h) = cv2.boundingRect(c)
if w > 50 and h > 50:
    cv2.drawContours(frame, [c], -1, colors[key], 2)
    cv2.circle(frame, (cX, cY), 7, (255, 255, 255), -1)
    cv2.putText(frame, "center", (cX - 20, cY - 20),
        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255),2)
    cv2.putText(frame, shape, (cX + 20, cY + 20), cv2.FONT_HERSHEY_SIMPLEX,
        0.5, (255, 255, 255), 2)
    c = c.astype("float")
    c *= ratio
    c = c.astype("int")

cv2.imshow("Frame", frame)
return frame

while True:

(grabbed, frame) = camera.read()
resized = imutils.resize(frame, width=600)
ratio = frame.shape[0] / float(resized.shape[0])
blurred = cv2.GaussianBlur(frame, (11,11), 0)
hsv = cv2.cvtColor(blurred,  cv2.COLOR_BGR2HSV)


for key, value in upper.items():
    kernel = np.ones((9,9),np.uint8)
    mask = cv2.inRange(hsv, lower[key], upper[key])
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
    #mask = cv2.morphologyEx(mask, cv2.MORPH_GRADIENT, kernel)

    cnts = cv2.findContours(mask.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
    cnts = cnts[0] if imutils.is_cv2() else cnts[1]
    sd = ShapeDetector()

    for c in cnts:
        M = cv2.moments(c)
        cX = int((M["m10"] / M["m00"]) * ratio)
        cY = int((M["m01"] / M["m00"]) * ratio)


        shape = sd.detect(c)
        drawContours(frame, c)

        #cv2.imshow("mask",mask)

key = cv2.waitKey(1) & 0xFF

if key == ord("q"):
    break

camera.release() cv2.destroyAllWindows()

Here is the shape detection: import cv2

class ShapeDetector: def __init__(self): pass

def detect(self, c):
    # initialize the shape name and approximate the contour
    shape = "unidentified"
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.04 * peri, True)
    # if the shape is a triangle, it will have 3 vertices
    if len(approx) == 3:
        shape = "triangle"

    # if the shape has 4 vertices, it is either a square or
    # a rectangle
    elif len ...
(more)
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