def refineLabels(out, labels, corners, howmany=3):
# pick up labels on corners
containcorners = []
for corner in corners:
x = int(corner[0][0])
y = int(corner[0][1])
hsv = rgb2hsv(out[y, x])
# accept only if candidate color(yellow or blue)
if checkBlue(hsv) or checkYellow(hsv):
containcorners.append(labels[y][x])
# make table(corners,label)
match = []
for label in np.unique(containcorners):
match.append([(containcorners == label).sum(), label])
match = sorted(match, reverse=True)
match = np.array(match)
# refine howmany
howmany = howmany if howmany < match.shape[0] else match.shape[0]
# select labels with enough corners
pickup = []
for i in range(howmany):
pickup.append(match[i][1])
# mark bg with 0
mark = 0
labels_ = np.zeros(labels.shape)
#
for label in np.unique(pickup):
mark += 1
labels_[labels == label] = mark
# make saturation
return labels_
def estimateColr(img,
rect,
debug=False):
[x, y, w, h] = rect
roi = img[y:y+h, x:x+w]
rgb = cv2.cvtColor(roi,cv2.COLOR_BGR2RGB)
totalcolr = [0,0,0]
for j in range(h):
for i in range(w):
totalcolr += rgb[j,i]
pixelcount = w * h
avergecolr = totalcolr / pixelcount
if debug:
print("estimatecolr,avrgcolr:",avergecolr)
hsv = rgb2hsv(avergecolr)
return checkBlue(hsv) or checkYellow(hsv)
def detectcolr(self):
c_x = self.width / 2
c_y = self.height / 2
roi = self.bgr[c_y - 15:c_y + 15, c_x - 30:c_x + 30, :]
rgb = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
h, w, c = roi.shape
totalcolr = np.zeros(3)
for j in range(h):
for i in range(w):
totalcolr += rgb[j, i]
avergecolr = totalcolr / (w * h)
hsv = rgb2hsv(avergecolr)
if checkBlue(hsv):
self.colr = 1
self.returnImg = 255 - self.laplacian
elif checkYellow(hsv):
self.colr = 2
self.returnImg = self.laplacian.copy()
else:
self.colr = 0