detection.py

# import modules
import numpy as np
import argparse
import cv2


# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-b", "--buffer", type=int, default=64,
                help="max buffer size")
args = vars(ap.parse_args())

# define the lower and upper boundaries of the colors in the HSV color space
lower = {'red': (166, 84, 141), 'green': (66, 122, 129), 'blue': (97, 100, 117), 'yellow': (23, 59, 119), 'orange': (0, 50, 80)}  # assign new item lower['blue'] = (93, 10, 0)
upper = {'red': (186, 255, 255), 'green': (86, 255, 255), 'blue': (117, 255, 255), 'yellow': (54, 255, 255), 'orange': (20, 255, 255)}

# define standard colors for circle around the object
colors = {'red': (0, 0, 255), 'green': (0, 255, 0), 'blue': (255, 0, 0), 'yellow': (0, 255, 217), 'orange': (0, 140, 255)}

camera = cv2.VideoCapture(0 + cv2.CAP_DSHOW)

# keep looping
while True:
    # grab the current frame
    (grabbed, frame) = camera.read()

    # resize the frame, blur it, and convert it to the HSV
    # color space
    frame = cv2.resize(frame, (640, 480))

    blurred = cv2.GaussianBlur(frame, (11, 11), 0)
    hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)
    # for each color in dictionary check object in frame
    for key, value in upper.items():
        # construct a mask for the color from dictionary`1, then perform
        # a series of dilations and erosions to remove any small
        # blobs left in the mask
        kernel = np.ones((9, 9), np.uint8)
        mask = cv2.inRange(hsv, lower[key], upper[key])
        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)

        # find contours in the mask and initialize the current
        # (x, y) center of the ball
        cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
                                cv2.CHAIN_APPROX_SIMPLE)[-2]
        center = None

        # only proceed if at least one contour was found
        if len(cnts) > 0:
            # find the largest contour in the mask, then use
            # it to compute the minimum enclosing circle and
            # centroid
            c = max(cnts, key=cv2.contourArea)
            ((x, y), radius) = cv2.minEnclosingCircle(c)
            M = cv2.moments(c)
            center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))

            # only proceed if the radius meets a minimum size. Correct this value for your obect's size
            if radius > 0.5:
                # draw the circle and centroid on the frame,
                # then update the list of tracked points
                cv2.circle(frame, (int(x), int(y)), int(radius), colors[key], 2)
                cv2.putText(frame, key, (int(x - radius), int(y - radius)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, colors[key], 2)

    # show the frame to our screen
    cv2.imshow("Frame", frame)

    key = cv2.waitKey(1) & 0xFF
    # if the 'q' key is pressed, stop the loop
    if key == ord("q"):
        break

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