Discussion :

[Quent91]

Bonjour,

Dans le cadre d'un projet de fin d'année, je dois réaliser une voiture miniature autonome. Cela fait plusieurs jours que je bloque sur la détection de feux tricolores. Suivant la luminosité ambiante, les valeurs HSV de mon feux tricolore changent. J'aimerais donc savoir si il est possible de faire en sorte que peu importe l'endroit ou l'on se situe, les valeurs HSV restent fixes. Mon deuxième problème est que je n'arrive pas à détecter seulement mon feux.

Voici le code :

Code :

Sélectionner tout - Visualiser dans une fenêtre à part

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import numpy as np
import cv2
def nothing (x):
   pass
 
 
cap = cv2.VideoCapture(0)
cv2.namedWindow("Trackbars")
 
 
cap.set(3,320)
cap.set(4,240)
#print (cap.get(5))
 
 
cv2.createTrackbar("L - H","Trackbars",0,255,nothing)
cv2.createTrackbar("L - S","Trackbars",0,255,nothing)
cv2.createTrackbar("L - V","Trackbars",0,255,nothing)
cv2.createTrackbar("U - H","Trackbars",0,255,nothing)
cv2.createTrackbar("U - S","Trackbars",0,255,nothing)
cv2.createTrackbar("U - V","Trackbars",0,255,nothing)
 
while True:
 
 
    _, frame = cap.read()
 
    blurred = cv2.GaussianBlur(frame, (11, 11), 0)
    hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)
 
 
    l_h = cv2.getTrackbarPos("L - H","Trackbars")          
    l_s = cv2.getTrackbarPos("L - S","Trackbars")          
    l_v = cv2.getTrackbarPos("L - V","Trackbars")         
    u_h = cv2.getTrackbarPos("U - H","Trackbars")         
    u_s = cv2.getTrackbarPos("U - S","Trackbars")          
    u_v = cv2.getTrackbarPos("U - V","Trackbars")
 
 
 
    lower = {'red':(1, 220, 163), 'yellow':(l_h, l_s, l_v), 'green':(l_h, l_s, l_v)} #assign new item
    upper = {'red':(60, 255, 255), 'yellow':(u_h,u_s,u_v), 'green':(u_h,u_s,u_v)}
 
    # define standard colors for circle around the object
    colors = {'red':(0,0,255), 'yellow':(0, 255, 217), 'green':(0,255,0)}
 
    #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
        #print(len(cnts))
        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)
            cX = int(M["m10"] / M["m00"])
            cY = int(M["m01"] / M["m00"])
 
            # only proceed if the radius meets a minimum size. Correct this value for your object's size
            #print radius     
            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 + " light", (cX+40, cY), cv2.FONT_HERSHEY_SIMPLEX, 0.6,colors[key],2)
 
 
    # show the frame to our screen
    cv2.imshow("Frame", frame)
    cv2.imshow("Mask", mask)
 
    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()