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| 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': | |
| parse_mask = (parse_array == 7).astype(np.float32) + \ | |
| (parse_array == 4).astype(np.float32) + \ | |
| (parse_array == 5).astype(np.float32) + \ | |
| (parse_array == 6).astype(np.float32) | |
| parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed)) | |
| 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 | |