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