class SudocubeFactory(object):
def __init__(self):
self._init_dependencies()
def _init_dependencies(self):
self._corner_finder = CornerFinder()
self._data_extractor = SudocubeDataExtractor()
self._unwarper = Unwarper()
self._color_extractor = ColorExtractor()
def create_sudocube_from_image(self, bgr_img):
hsv_img = self._convert_bgr_image_to_hsv(bgr_img)
smooth_img = self._smooth_image(hsv_img)
smooth_img = cv2.GaussianBlur(smooth_img, (9, 9), 0.5)
unwarped_img = self._unwarp_image(smooth_img)
sudocube_corners = self._find_corners(unwarped_img)
sudocube_data = self._data_extractor.extract_data_from_image_with_corner_info(unwarped_img, sudocube_corners)
sudocube = Sudocube(sudocube_data)
return sudocube
def _unwarp_image(self, img):
green_frame = self._find_green_frame_around_sudoku(img)
unwarped_img = self._unwarper.unwarp(green_frame, img)
return unwarped_img
def _find_corners(self, img):
sudocube_frame = self._find_sudocube_frame(img)
sudocube_corners = self._corner_finder.find_corners(sudocube_frame)
return sudocube_corners
def _convert_bgr_image_to_hsv(self, bgr_img):
return cv2.cvtColor(bgr_img, cv2.COLOR_RGB2HSV)
def _smooth_image(self, img):
return cv2.GaussianBlur(img, (5, 5), 0.50)
def _find_sudocube_frame(self, img):
return self._color_extractor.extract_blue(img)
def _find_green_frame_around_sudoku(self, img):
return self._color_extractor.extract_green(img)
model = get_model_instance_segmentation(chanel_data.number_of_classes())
print("loading model", cnf.model_name )
model.load_state_dict(torch.load(cnf.path2model+cnf.model_name, map_location=device ))
model.to(device)
model.eval()
cnf.num_tsts= 1000 # inf
for i, item in enumerate(chanel_data):
if i >= cnf.num_tsts: break
image = item[0]
masks, labels = instance_segmentation_api(model, image, device, chanel_data.class_names,
threshold=0.7, rect_th=1, text_size=1, text_th=3)
masked_img = []
for jj in range(len(labels)):
masks[jj] = masks[jj].astype(dtype='uint')
# zz[masks[jj]] = jj+1; # Image.fromarray(zz*255/(jj+1)).show()
img = ImageChops.multiply(image, Image.fromarray(255*masks[jj]).convert('RGB') ); # img.show()
masked_img.append(np.array(img, dtype='uint8'))
one_person_clothing_colors = ColorExtractor(masks, labels, masked_img, cnf,
image_name=item[1][-10:-1])
one_person_clothing_colors.pie_chart(image, fname=item[1][-10:-1], figure_size=(4, 4))
# color_table_obj = ColorTable(dataset.class_names, cnf)
def _init_dependencies(self):
self._corner_finder = CornerFinder()
self._data_extractor = SudocubeDataExtractor()
self._unwarper = Unwarper()
self._color_extractor = ColorExtractor()
