import os import cv2 import numpy as np from src.constants.image_processing import ( DEFAULT_BLACK_COLOR, DEFAULT_BORDER_REMOVE, DEFAULT_GAUSSIAN_BLUR_PARAMS_MARKER, DEFAULT_LINE_WIDTH, DEFAULT_NORMALIZE_PARAMS, DEFAULT_WHITE_COLOR, ERODE_RECT_COLOR, EROSION_PARAMS, MARKER_RECTANGLE_COLOR, NORMAL_RECT_COLOR, QUADRANT_DIVISION, ) from src.logger import logger from src.processors.interfaces.ImagePreprocessor import ImagePreprocessor from src.utils.image import ImageUtils from src.utils.interaction import InteractionUtils class CropOnMarkers(ImagePreprocessor): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) config = self.tuning_config marker_ops = self.options self.threshold_circles = [] # img_utils = ImageUtils() # options with defaults self.marker_path = os.path.join( self.relative_dir, marker_ops.get("relativePath", "omr_marker.jpg") ) self.min_matching_threshold = marker_ops.get("min_matching_threshold", 0.3) self.max_matching_variation = marker_ops.get("max_matching_variation", 0.41) self.marker_rescale_range = tuple( int(r) for r in marker_ops.get("marker_rescale_range", (35, 100)) ) self.marker_rescale_steps = int(marker_ops.get("marker_rescale_steps", 10)) self.apply_erode_subtract = marker_ops.get("apply_erode_subtract", True) self.marker = self.load_marker(marker_ops, config) def __str__(self): return self.marker_path def exclude_files(self): return [self.marker_path] def apply_filter(self, image, file_path): config = self.tuning_config image_instance_ops = self.image_instance_ops image_eroded_sub = ImageUtils.normalize_util( image if self.apply_erode_subtract else ( image - cv2.erode( image, kernel=np.ones(EROSION_PARAMS["kernel_size"]), iterations=EROSION_PARAMS["iterations"], ) ) ) # Quads on warped image quads = {} h1, w1 = image_eroded_sub.shape[:2] midh, midw = ( h1 // QUADRANT_DIVISION["height_factor"], w1 // QUADRANT_DIVISION["width_factor"], ) origins = [[0, 0], [midw, 0], [0, midh], [midw, midh]] quads[0] = image_eroded_sub[0:midh, 0:midw] quads[1] = image_eroded_sub[0:midh, midw:w1] quads[2] = image_eroded_sub[midh:h1, 0:midw] quads[3] = image_eroded_sub[midh:h1, midw:w1] # Draw Quadlines image_eroded_sub[:, midw : midw + 2] = DEFAULT_WHITE_COLOR image_eroded_sub[midh : midh + 2, :] = DEFAULT_WHITE_COLOR best_scale, all_max_t = self.getBestMatch(image_eroded_sub) if best_scale is None: if config.outputs.show_image_level >= 1: InteractionUtils.show("Quads", image_eroded_sub, config=config) return None optimal_marker = ImageUtils.resize_util_h( self.marker, u_height=int(self.marker.shape[0] * best_scale) ) _h, w = optimal_marker.shape[:2] centres = [] sum_t, max_t = 0, 0 quarter_match_log = "Matching Marker: " for k in range(0, 4): res = cv2.matchTemplate(quads[k], optimal_marker, cv2.TM_CCOEFF_NORMED) max_t = res.max() quarter_match_log += f"Quarter{str(k + 1)}: {str(round(max_t, 3))}\t" if ( max_t < self.min_matching_threshold or abs(all_max_t - max_t) >= self.max_matching_variation ): logger.error( file_path, "\nError: No circle found in Quad", k + 1, "\n\t min_matching_threshold", self.min_matching_threshold, "\t max_matching_variation", self.max_matching_variation, "\t max_t", max_t, "\t all_max_t", all_max_t, ) if config.outputs.show_image_level >= 1: InteractionUtils.show( f"No markers: {file_path}", image_eroded_sub, 0, config=config, ) InteractionUtils.show( f"res_Q{str(k + 1)} ({str(max_t)})", res, 1, config=config, ) return None pt = np.argwhere(res == max_t)[0] pt = [pt[1], pt[0]] pt[0] += origins[k][0] pt[1] += origins[k][1] # print(">>",pt) image = cv2.rectangle( image, tuple(pt), (pt[0] + w, pt[1] + _h), MARKER_RECTANGLE_COLOR, DEFAULT_LINE_WIDTH, ) # display: image_eroded_sub = cv2.rectangle( image_eroded_sub, tuple(pt), (pt[0] + w, pt[1] + _h), ERODE_RECT_COLOR if self.apply_erode_subtract else NORMAL_RECT_COLOR, 4, ) centres.append([pt[0] + w / 2, pt[1] + _h / 2]) sum_t += max_t logger.info(quarter_match_log) logger.info(f"Optimal Scale: {best_scale}") # analysis data self.threshold_circles.append(sum_t / 4) image = ImageUtils.four_point_transform(image, np.array(centres)) # appendSaveImg(1,image_eroded_sub) # appendSaveImg(1,image_norm) image_instance_ops.append_save_img(2, image_eroded_sub) # Debugging image - # res = cv2.matchTemplate(image_eroded_sub,optimal_marker,cv2.TM_CCOEFF_NORMED) # res[ : , midw:midw+2] = 255 # res[ midh:midh+2, : ] = 255 # show("Markers Matching",res) if config.outputs.show_image_level >= 2 and config.outputs.show_image_level < 4: image_eroded_sub = ImageUtils.resize_util_h( image_eroded_sub, image.shape[0] ) image_eroded_sub[:, -DEFAULT_BORDER_REMOVE:] = DEFAULT_BLACK_COLOR h_stack = np.hstack((image_eroded_sub, image)) InteractionUtils.show( f"Warped: {file_path}", ImageUtils.resize_util( h_stack, int(config.dimensions.display_width * 1.6) ), 0, 0, [0, 0], config=config, ) # iterations : Tuned to 2. # image_eroded_sub = image_norm - cv2.erode(image_norm, kernel=np.ones((5,5)),iterations=2) return image def load_marker(self, marker_ops, config): if not os.path.exists(self.marker_path): logger.error( "Marker not found at path provided in template:", self.marker_path, ) exit(31) marker = cv2.imread(self.marker_path, cv2.IMREAD_GRAYSCALE) if "sheetToMarkerWidthRatio" in marker_ops: marker = ImageUtils.resize_util( marker, config.dimensions.processing_width / int(marker_ops["sheetToMarkerWidthRatio"]), ) marker = cv2.GaussianBlur( marker, DEFAULT_GAUSSIAN_BLUR_PARAMS_MARKER["kernel_size"], DEFAULT_GAUSSIAN_BLUR_PARAMS_MARKER["sigma_x"], ) marker = cv2.normalize( marker, None, alpha=DEFAULT_NORMALIZE_PARAMS["alpha"], beta=DEFAULT_NORMALIZE_PARAMS["beta"], norm_type=cv2.NORM_MINMAX, ) if self.apply_erode_subtract: marker -= cv2.erode( marker, kernel=np.ones(EROSION_PARAMS["kernel_size"]), iterations=EROSION_PARAMS["iterations"], ) return marker # Resizing the marker within scaleRange at rate of descent_per_step to # find the best match. def getBestMatch(self, image_eroded_sub): config = self.tuning_config descent_per_step = ( self.marker_rescale_range[1] - self.marker_rescale_range[0] ) // self.marker_rescale_steps _h, _w = self.marker.shape[:2] res, best_scale = None, None all_max_t = 0 for r0 in np.arange( self.marker_rescale_range[1], self.marker_rescale_range[0], -1 * descent_per_step, ): # reverse order s = float(r0 * 1 / 100) if s == 0.0: continue rescaled_marker = ImageUtils.resize_util_h( self.marker, u_height=int(_h * s) ) # res is the black image with white dots res = cv2.matchTemplate( image_eroded_sub, rescaled_marker, cv2.TM_CCOEFF_NORMED ) max_t = res.max() if all_max_t < max_t: # print('Scale: '+str(s)+', Circle Match: '+str(round(max_t*100,2))+'%') best_scale, all_max_t = s, max_t if all_max_t < self.min_matching_threshold: logger.warning( "\tTemplate matching too low! Consider rechecking preProcessors applied before this." ) if config.outputs.show_image_level >= 1: InteractionUtils.show("res", res, 1, 0, config=config) if best_scale is None: logger.warning( "No matchings for given scaleRange:", self.marker_rescale_range ) return best_scale, all_max_t