import numpy as np import cv2 from PIL import Image, ImageDraw label_map = { "background": 0, "hat": 1, "hair": 2, "sunglasses": 3, "upper_clothes": 4, "skirt": 5, "pants": 6, "dress": 7, "belt": 8, "left_shoe": 9, "right_shoe": 10, "head": 11, "left_leg": 12, "right_leg": 13, "left_arm": 14, "right_arm": 15, "bag": 16, "scarf": 17, } def extend_arm_mask(wrist, elbow, scale): wrist = elbow + scale * (wrist - elbow) return wrist def hole_fill(img): img = np.pad(img[1:-1, 1:-1], pad_width=1, mode='constant', constant_values=0) img_copy = img.copy() mask = np.zeros((img.shape[0] + 2, img.shape[1] + 2), dtype=np.uint8) cv2.floodFill(img, mask, (0, 0), 255) img_inverse = cv2.bitwise_not(img) dst = cv2.bitwise_or(img_copy, img_inverse) return dst def refine_mask(mask): contours, hierarchy = cv2.findContours(mask.astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1) area = [] for j in range(len(contours)): a_d = cv2.contourArea(contours[j], True) area.append(abs(a_d)) refine_mask = np.zeros_like(mask).astype(np.uint8) if len(area) != 0: i = area.index(max(area)) cv2.drawContours(refine_mask, contours, i, color=255, thickness=-1) return refine_mask def get_mask_location(model_type, category, model_parse: Image.Image, keypoint: dict, width=384, height=512): im_parse = model_parse.resize((width, height), Image.NEAREST) parse_array = np.array(im_parse) if model_type == 'hd': arm_width = 60 elif model_type == 'dc': arm_width = 45 else: raise ValueError("model_type must be 'hd' or 'dc'!") parse_head = (parse_array == 1).astype(np.float32) + \ (parse_array == 3).astype(np.float32) + \ (parse_array == 11).astype(np.float32) parser_mask_fixed = (parse_array == label_map["left_shoe"]).astype(np.float32) + \ (parse_array == label_map["right_shoe"]).astype(np.float32) + \ (parse_array == label_map["hat"]).astype(np.float32) + \ (parse_array == label_map["sunglasses"]).astype(np.float32) + \ (parse_array == label_map["bag"]).astype(np.float32) parser_mask_changeable = (parse_array == label_map["background"]).astype(np.float32) arms_left = (parse_array == 14).astype(np.float32) arms_right = (parse_array == 15).astype(np.float32) if category == 'dresses': # Initial dress mask for the upper body parse_mask_upper = np.logical_or((parse_array == label_map["upper_clothes"]), (parse_array == label_map["dress"])).astype(np.float32) parser_mask_fixed_lower_cloth = (parse_array == label_map["skirt"]).astype(np.float32) + \ (parse_array == label_map["pants"]).astype(np.float32) parser_mask_fixed += parser_mask_fixed_lower_cloth # Create a mask for the legs (including skirts and pants) parse_mask_legs = (parse_array == label_map["skirt"]).astype(np.float32) + \ (parse_array == label_map["pants"]).astype(np.float32) + \ (parse_array == label_map["left_leg"]).astype(np.float32) + \ (parse_array == label_map["right_leg"]).astype(np.float32) # Dilate the leg mask to ensure coverage and fill gaps parse_mask_legs = cv2.dilate(parse_mask_legs.astype(np.uint8), np.ones((6, 6), np.uint8), iterations=6) # Combine the upper body mask with the leg mask parse_mask = np.maximum(parse_mask_upper, parse_mask_legs) elif category == 'upper_body': parse_mask = (parse_array == 4).astype(np.float32) + (parse_array == 7).astype(np.float32) parser_mask_fixed_lower_cloth = (parse_array == label_map["skirt"]).astype(np.float32) + \ (parse_array == label_map["pants"]).astype(np.float32) parser_mask_fixed += parser_mask_fixed_lower_cloth parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed)) elif category == 'lower_body': parse_mask = (parse_array == 6).astype(np.float32) + \ (parse_array == 12).astype(np.float32) + \ (parse_array == 13).astype(np.float32) + \ (parse_array == 5).astype(np.float32) parser_mask_fixed += (parse_array == label_map["upper_clothes"]).astype(np.float32) + \ (parse_array == 14).astype(np.float32) + \ (parse_array == 15).astype(np.float32) parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed)) else: raise NotImplementedError # Load pose points pose_data = keypoint["pose_keypoints_2d"] pose_data = np.array(pose_data) pose_data = pose_data.reshape((-1, 2)) im_arms_left = Image.new('L', (width, height)) im_arms_right = Image.new('L', (width, height)) arms_draw_left = ImageDraw.Draw(im_arms_left) arms_draw_right = ImageDraw.Draw(im_arms_right) if category == 'dresses' or category == 'upper_body': shoulder_right = np.multiply(tuple(pose_data[2][:2]), height / 512.0) shoulder_left = np.multiply(tuple(pose_data[5][:2]), height / 512.0) elbow_right = np.multiply(tuple(pose_data[3][:2]), height / 512.0) elbow_left = np.multiply(tuple(pose_data[6][:2]), height / 512.0) wrist_right = np.multiply(tuple(pose_data[4][:2]), height / 512.0) wrist_left = np.multiply(tuple(pose_data[7][:2]), height / 512.0) ARM_LINE_WIDTH = int(arm_width / 512 * height) size_left = [shoulder_left[0] - ARM_LINE_WIDTH // 2, shoulder_left[1] - ARM_LINE_WIDTH // 2, shoulder_left[0] + ARM_LINE_WIDTH // 2, shoulder_left[1] + ARM_LINE_WIDTH // 2] size_right = [shoulder_right[0] - ARM_LINE_WIDTH // 2, shoulder_right[1] - ARM_LINE_WIDTH // 2, shoulder_right[0] + ARM_LINE_WIDTH // 2, shoulder_right[1] + ARM_LINE_WIDTH // 2] if wrist_right[0] <= 1. and wrist_right[1] <= 1.: im_arms_right = arms_right else: wrist_right = extend_arm_mask(wrist_right, elbow_right, 1.2) arms_draw_right.line(np.concatenate((shoulder_right, elbow_right, wrist_right)).astype(np.uint16).tolist(), 'white', ARM_LINE_WIDTH, 'curve') arms_draw_right.arc(size_right, 0, 360, 'white', ARM_LINE_WIDTH // 2) if wrist_left[0] <= 1. and wrist_left[1] <= 1.: im_arms_left = arms_left else: wrist_left = extend_arm_mask(wrist_left, elbow_left, 1.2) arms_draw_left.line(np.concatenate((wrist_left, elbow_left, shoulder_left)).astype(np.uint16).tolist(), 'white', ARM_LINE_WIDTH, 'curve') arms_draw_left.arc(size_left, 0, 360, 'white', ARM_LINE_WIDTH // 2) hands_left = np.logical_and(np.logical_not(im_arms_left), arms_left) hands_right = np.logical_and(np.logical_not(im_arms_right), arms_right) parser_mask_fixed += hands_left + hands_right parser_mask_fixed = np.logical_or(parser_mask_fixed, parse_head) parse_mask = cv2.dilate(parse_mask, np.ones((5, 5), np.uint16), iterations=5) if category == 'dresses' or category == 'upper_body': neck_mask = (parse_array == 18).astype(np.float32) neck_mask = cv2.dilate(neck_mask, np.ones((5, 5), np.uint16), iterations=1) neck_mask = np.logical_and(neck_mask, np.logical_not(parse_head)) parse_mask = np.logical_or(parse_mask, neck_mask) arm_mask = cv2.dilate(np.logical_or(im_arms_left, im_arms_right).astype('float32'), np.ones((5, 5), np.uint16), iterations=4) parse_mask += np.logical_or(parse_mask, arm_mask) parse_mask = np.logical_and(parser_mask_changeable, np.logical_not(parse_mask)) parse_mask_total = np.logical_or(parse_mask, parser_mask_fixed) inpaint_mask = 1 - parse_mask_total img = np.where(inpaint_mask, 255, 0) dst = hole_fill(img.astype(np.uint8)) dst = refine_mask(dst) inpaint_mask = dst / 255 * 1 mask = Image.fromarray(inpaint_mask.astype(np.uint8) * 255) mask_gray = Image.fromarray(inpaint_mask.astype(np.uint8) * 127) return mask, mask_gray