def calculate_distance_map(self, img_luv, target_distribution):
"""Calculates a distance map by sliding a window by a given stride"""
distances = np.empty(
dtype=np.uint8,
shape=(int((img_luv.shape[0] - self.window_size) / self.stride),
int((img_luv.shape[1] - self.window_size) / self.stride)))
for x in range(distances.shape[1]):
for y in range(distances.shape[0]):
x_px = x * self.stride
y_px = y * self.stride
local_distribution = Utils.calculate_distribution(
img_luv[y_px:y_px + self.window_size,
x_px:x_px + self.window_size])
distance = Utils.bhattacharyya_distance(
local_distribution, target_distribution)
# clamp & check distance
if np.isnan(distance):
print("Distance calculated was NaN! Setting to 255.")
distance = 255
if distance > 255: distance = 255
if distance < 0: distance = 0
distances[y, x] = int(distance)
# DEBUG
self.distances_img = self.distance_map_to_img(distances, img_luv)
return distances
def segment(self, img_original, target_distribution_rect):
"""Returns a quad or None if segmentation failed"""
# DEBUG
self.img_original = img_original
# norm, preprocess, luv
img_normalized = self.normalize(img_original)
img_preprocessed = self.preprocess(img_normalized)
img_luv = cv2.cvtColor(img_preprocessed, cv2.COLOR_BGR2Luv)
# target distribution
r = target_distribution_rect.scale(self.scale_factor).floor()
target_distribution = Utils.calculate_distribution(
img_luv[r.a[1]:r.b[1], r.a[0]:r.b[0]])
# distance map
distances = self.calculate_distance_map(img_luv, target_distribution)
# region
region = self.get_receipt_pixels(distances)
if region is None:
return None
# quad
quad = self.extract_quad(region)
return quad
def small_distribution(self):
"""Calculates distribution of the small ROI"""
r = self.small_roi_rect().floor()
img = self.img[r.a[1]:r.b[1], r.a[0]:r.b[0]]
img_luv = cv2.cvtColor(img, cv2.COLOR_BGR2Luv)
return Utils.calculate_distribution(img_luv)
def distribution(self):
"""Calculates the distribution of the selected region of interest"""
img_luv = cv2.cvtColor(self.distribution_img, cv2.COLOR_BGR2Luv)
return Utils.calculate_distribution(img_luv)
