class Segmentation:
def __init__(self):
self.pre_processing = Preprocessing()
self.LOWER_GRAY_2 = np.array([0, 0, 100])
self.UPPER_GRAY_2 = np.array([255, 80, 175])
def segment(self, image):
mask, image_no_background = self.pre_processing.cut_out_backgound(image)
image_hue = self.pre_processing.get_mask_brightness(image_no_background)
#cv2.imshow("img_hue", image_no_background)
mat_points = self.map_out(mask, image_no_background)
image = self.pre_processing.equalize_clahe(image)
return mat_points, image#image_no_background
def map_out(self, img_bin, image):
mat_points = []
contours, inheriters = cv2.findContours(img_bin, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
moments = cv2.moments(c)
if moments['m00'] > 100:
x = []
y = []
for i in c:
for j in i:
x.append(j[0])
y.append(j[1])
max_x, min_x, max_y, min_y = np.argmax(x), np.argmin(x), np.argmax(y), np.argmin(y)
mat_points.append((x[min_x], y[min_y], x[max_x], y[max_y]))
return mat_points
def get_points_min_max(self, array):
x, y = cv2.split(array)
min_x = min(x)[0]
x = list(x)
pos_min_x = x.index(min_x)
min_y = y[pos_min_x][0]
max_x = max(x)[0]
pos_max_x = x.index(max_x)
max_y = y[pos_max_x][0]
return (min_x, min_y),(max_x, max_y)
def highlight_smoke_contours(self, image):
mask = self.pre_processing.enhance_color(self.LOWER_GRAY_2, self.UPPER_GRAY_2, image)
contours, cany = self.pre_processing.border_image(mask, image)
i = 0
sub_mats = []
for c in contours:
extension = cv2.contourArea(c)
if extension > 600:
contour = contours[i]
(x_min, y_min), (x_max, y_max) = self.get_points_min_max(contour)
sub_mats.append((x_min, y_min, x_max, y_max))
self.pre_processing.draw_image(image, 1, [(x_min, y_min), (x_max, y_max)], (255,0,0))
self.pre_processing.draw_image(image, 2, contour, (0,255,0))
self.pre_processing.draw_image(cany, 2, contour,(0,255,0))
i = i + 1
return image, cany, sub_mats
class Segmentation:
def __init__(self):
self.pre_processing = Preprocessing()
self.LOWER_GRAY_2 = np.array([0, 0, 100])
self.UPPER_GRAY_2 = np.array([255, 80, 175])
def segment(self, image):
mask, image_no_background = self.pre_processing.cut_out_backgound(
image)
image_hue = self.pre_processing.get_mask_brightness(
image_no_background)
#cv2.imshow("img_hue", image_no_background)
mat_points = self.map_out(mask, image_no_background)
image = self.pre_processing.equalize_clahe(image)
return mat_points, image #image_no_background
def map_out(self, img_bin, image):
mat_points = []
contours, inheriters = cv2.findContours(img_bin, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
moments = cv2.moments(c)
if moments['m00'] > 100:
x = []
y = []
for i in c:
for j in i:
x.append(j[0])
y.append(j[1])
max_x, min_x, max_y, min_y = np.argmax(x), np.argmin(
x), np.argmax(y), np.argmin(y)
mat_points.append((x[min_x], y[min_y], x[max_x], y[max_y]))
return mat_points
def get_points_min_max(self, array):
x, y = cv2.split(array)
min_x = min(x)[0]
x = list(x)
pos_min_x = x.index(min_x)
min_y = y[pos_min_x][0]
max_x = max(x)[0]
pos_max_x = x.index(max_x)
max_y = y[pos_max_x][0]
return (min_x, min_y), (max_x, max_y)
def highlight_smoke_contours(self, image):
mask = self.pre_processing.enhance_color(self.LOWER_GRAY_2,
self.UPPER_GRAY_2, image)
contours, cany = self.pre_processing.border_image(mask, image)
i = 0
sub_mats = []
for c in contours:
extension = cv2.contourArea(c)
if extension > 600:
contour = contours[i]
(x_min, y_min), (x_max,
y_max) = self.get_points_min_max(contour)
sub_mats.append((x_min, y_min, x_max, y_max))
self.pre_processing.draw_image(image, 1, [(x_min, y_min),
(x_max, y_max)],
(255, 0, 0))
self.pre_processing.draw_image(image, 2, contour, (0, 255, 0))
self.pre_processing.draw_image(cany, 2, contour, (0, 255, 0))
i = i + 1
return image, cany, sub_mats
