while cap.isOpened():
ret, frame = cap.read()
if ret == True:
out = frame.copy()
rows, cols, _ = frame.shape
binary = np.zeros((rows, cols, 3), np.float32)
for i in range(rows):
for j in range(cols):
individualPixel = frame[i, j]
temp = individualPixel[0]
individualPixel[0] = individualPixel[2]
individualPixel[2] = temp
#print(i,"x",j,":",individualPixel)
if (EM.getProbGMM(individualPixel, 'yellow') >= 3.00e-6):
#if (EM.getProbGMM(individualPixel, 'yellow') >= 3.00e-6):
binary[i, j] = np.array([255, 255, 255])
# else:
# frame[i, j] = np.array([255, 255, 255])
new = cv2.medianBlur(binary, 5)
#new = cv2.GaussianBlur(new,(5,5),5)
edges = cv2.Canny(np.uint8(new), 50, 255)
conts, h = cv2.findContours(edges, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
(conts_sorted,
boundingBoxes) = contours.sort_contours(conts, method="left-to-right")
hull = cv2.convexHull(conts_sorted[0])
(x, y), radius = cv2.minEnclosingCircle(hull)
if radius > 6:
cv2.circle(out, (int(x), int(y)), int(radius), (0, 255, 0), 4)
count = -1
while cap.isOpened():
ret, frame = cap.read()
count += 1
# if count < 52:
# continue
print(count)
out = frame.copy()
rows, cols, _ = frame.shape
binary = np.zeros((rows, cols, 3), np.float32)
for i in range(rows):
for j in range(cols):
individualPixel = frame[i, j, 1]
#print(i,"x",j,":",individualPixel)
if (EM.getProbGMM(individualPixel, 'green') >= 3.00e-6):
binary[i, j] = np.array([255, 255, 255])
# else:
# frame[i, j] = np.array([255, 255, 255])
new = cv2.medianBlur(binary, 5)
new = cv2.GaussianBlur(new, (5, 5), 5)
edges = cv2.Canny(np.uint8(new), 50, 255)
conts, h = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
try:
(conts_sorted,
boundingBoxes) = contours.sort_contours(conts, method="left-to-right")
hull = cv2.convexHull(conts_sorted[0])
(x, y), radius = cv2.minEnclosingCircle(hull)
except:
radius = 0
if radius > 6:
ret, frame = cap.read()
count += 1
# if count < 147:
# continue
if ret == True:
out = frame.copy()
rows, cols, _ = frame.shape
binary = np.zeros((rows, cols, 3), np.float32)
for i in range(rows):
for j in range(cols):
individualPixel = frame[i, j]
temp = individualPixel[0]
individualPixel[0] = individualPixel[2]
individualPixel[2] = temp
#print(i,"x",j,":",individualPixel)
if (EM.getProbGMM(individualPixel, 'orange') >= 4.00e-6):
binary[i, j] = np.array([255, 255, 255])
# else:
# frame[i, j] = np.array([255, 255, 255])
new = cv2.medianBlur(binary, 5)
#new = cv2.GaussianBlur(new,(5,5),5)
edges = cv2.Canny(np.uint8(new), 50, 255)
conts, h = cv2.findContours(edges, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
try:
(conts_sorted,
boundingBoxes) = contours.sort_contours(conts,
method="left-to-right")
hull = cv2.convexHull(conts_sorted[0])
(x, y), radius = cv2.minEnclosingCircle(hull)
except:
