def __prepare_for_first_contour_connecting(self):
# cv2.imshow("first", self.cv_image)
dilated_image = Helper.dilate_image(self.cv_image)
# cv2.imshow("first dilate", dilated_image)
# self.steps.append(("con_dilated", dilated_image))
dilated_mask = Helper.convert_image_to_mask(dilated_image)
# cv2.imshow("mask", dilated_mask)
self.steps.append(("con_gray", dilated_mask))
gray_denoised_image = cv2.fastNlMeansDenoising(dilated_mask, None, 5, 7, 21)
self.steps.append(("con_denoised", gray_denoised_image))
# cv2.imshow("denoised", gray_denoised_image)
threshold_image = cv2.adaptiveThreshold(gray_denoised_image,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
self.steps.append(("con_threshold", threshold_image))
# cv2.imshow("threshold", threshold_image)
# ERODED = cv2.erode(threshold_image, (2,2), iterations=1)
# cv2.imshow("ERODED", ERODED)
# DILATED = cv2.dilate(ERODED, (2,2), iterations=1)
# cv2.imshow("DILATED", DILATED)
# DENOISED = cv2.fastNlMeansDenoising(threshold_image, None, 5, 7, 21)
# cv2.imshow("DENOISED", DENOISED)
prepared_mask = cv2.bitwise_and(threshold_image, threshold_image, mask=self.image_masks.topo_mask)
self.steps.append(("con_mask", prepared_mask))
cv2.imshow('prepared', prepared_mask)
return prepared_mask
def __generate_green_mask(self):
green_range = self.__get_image_in_range_from_hsv(
MaskGenerator.low_green, MaskGenerator.high_green)
filled_green_contours = self.__get_filled_contours_from_image(
green_range)
green_mask = Helper.convert_image_to_mask(filled_green_contours)
green_mask_reduced = Helper.reduce_image_contours(
green_mask, 200, line_thickness=cv2.FILLED)
return green_mask_reduced
def __generate_black_mask(self):
black_range = self.__get_image_in_range_from_hsv(
MaskGenerator.low_black, MaskGenerator.high_black)
filled_contours = self.__get_filled_contours_from_image(black_range)
contours_mask = Helper.convert_image_to_mask(filled_contours)
dilated = Helper.dilate_image(contours_mask, array=self.dilate_array)
black_mask = Helper.reduce_image_contours(dilated,
6,
line_thickness=cv2.FILLED)
return black_mask
def __generate_blue_mask(self):
blue_range = self.__get_image_in_range_from_hsv(
MaskGenerator.low_blue, MaskGenerator.high_blue)
self.image_masks.steps.append(("blue_range", blue_range))
filled_blue_contours = self.__get_filled_contours_from_image(
blue_range)
self.image_masks.steps.append(("blue_filled", filled_blue_contours))
blue_mask = Helper.convert_image_to_mask(filled_blue_contours)
dilated = Helper.dilate_image(blue_mask, array=(2, 2))
self.image_masks.steps.append(("blue_dilated", dilated))
blue_mask = Helper.reduce_image_contours(dilated,
15,
line_thickness=cv2.FILLED)
return blue_mask
def __generate_red_mask(self):
red_range = self.__get_image_in_range_from_hsv(MaskGenerator.low_red,
MaskGenerator.high_red)
# cv2.imshow("red range", red_range)
filled_contours = self.__get_filled_contours_from_image(red_range)
# cv2.imshow("filled contours", filled_contours)
contours_mask = Helper.convert_image_to_mask(filled_contours)
# cv2.imshow("contours mask", contours_mask)
dilated = Helper.dilate_image(contours_mask, array=self.dilate_array)
# cv2.imshow("dilated", dilated)
reduced = Helper.reduce_image_contours(dilated,
6,
line_thickness=cv2.FILLED)
# cv2.imshow("red range", red_range)
return reduced
def __get_sub_image(self):
rows, cols, chan = self.image.shape
sub_image = self.image[int(self.__bottom_thresh *
rows):rows, # bottom rows
int(self.__left_thresh *
cols):int(self.__right_thresh *
cols) # middle rows
]
sub_image = cv2.resize(sub_image,
None,
fx=self.__resize_factor,
fy=self.__resize_factor,
interpolation=cv2.INTER_LINEAR)
sub_image = Helper.convert_image_to_mask(sub_image)
gray_denoised_image = cv2.fastNlMeansDenoising(sub_image, None, 5, 7,
21)
threshold_image = cv2.threshold(gray_denoised_image, 225, 255,
cv2.THRESH_BINARY_INV)[1]
return sub_image
