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add humanparsing and openopse for agnostic mask
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,208 @@ | ||
| import os | ||
| import cv2 | ||
| import torch | ||
| import numpy as np | ||
| from numpy.linalg import lstsq | ||
| from PIL import Image, ImageDraw | ||
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| def resize_and_center(image, target_width, target_height): | ||
| img = np.array(image) | ||
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| if img.shape[-1] == 4: | ||
| img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB) | ||
| elif len(img.shape) == 2 or img.shape[-1] == 1: | ||
| img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) | ||
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| original_height, original_width = img.shape[:2] | ||
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| scale = min(target_height / original_height, target_width / original_width) | ||
| new_height = int(original_height * scale) | ||
| new_width = int(original_width * scale) | ||
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| resized_img = cv2.resize(img, (new_width, new_height), | ||
| interpolation=cv2.INTER_CUBIC) | ||
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| padded_img = np.ones((target_height, target_width, 3), | ||
| dtype=np.uint8) * 255 | ||
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| top = (target_height - new_height) // 2 | ||
| left = (target_width - new_width) // 2 | ||
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| padded_img[top:top + new_height, left:left + new_width] = resized_img | ||
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| return Image.fromarray(padded_img) | ||
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| def list_dir(folder_path): | ||
| # Collect all file paths within the directory | ||
| file_paths = [] | ||
| for root, _, files in os.walk(folder_path): | ||
| for file in files: | ||
| file_paths.append(os.path.join(root, file)) | ||
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| file_paths = sorted(file_paths) | ||
| return file_paths | ||
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| 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, | ||
| } | ||
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| def get_agnostic_mask(model_parse, keypoint, category, size=(384, 512)): | ||
| parse_array = np.array(model_parse) | ||
| pose_data = keypoint["pose_keypoints_2d"] | ||
| pose_data = np.array(pose_data) | ||
| pose_data = pose_data.reshape((-1, 2)) | ||
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| parse_shape = (parse_array > 0).astype(np.float32) | ||
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| parse_head = (parse_array == 1).astype(np.float32) + \ | ||
| (parse_array == 2).astype(np.float32) + \ | ||
| (parse_array == 3).astype(np.float32) + \ | ||
| (parse_array == 11).astype(np.float32) | ||
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| parser_mask_fixed = (parse_array == label_map["hair"]).astype(np.float32) + \ | ||
| (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["scarf"]).astype(np.float32) + \ | ||
| (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 = (parse_array == 14).astype(np.float32) + \ | ||
| (parse_array == 15).astype(np.float32) | ||
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| if category == 'dresses': | ||
| label_cat = 7 | ||
| parse_mask = (parse_array == 7).astype(np.float32) + \ | ||
| (parse_array == 12).astype(np.float32) + \ | ||
| (parse_array == 13).astype(np.float32) | ||
| parser_mask_changeable += np.logical_and( | ||
| parse_array, np.logical_not(parser_mask_fixed)) | ||
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| elif category == 'upper_body': | ||
| label_cat = 4 | ||
| parse_mask = (parse_array == 4).astype(np.float32) | ||
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| parser_mask_fixed += (parse_array == label_map["skirt"]).astype(np.float32) + \ | ||
| (parse_array == label_map["pants"]).astype(np.float32) | ||
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| parser_mask_changeable += np.logical_and( | ||
| parse_array, np.logical_not(parser_mask_fixed)) | ||
| elif category == 'lower_body': | ||
| label_cat = 6 | ||
| parse_mask = (parse_array == 6).astype(np.float32) + \ | ||
| (parse_array == 12).astype(np.float32) + \ | ||
| (parse_array == 13).astype(np.float32) | ||
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| 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)) | ||
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| parse_head = torch.from_numpy(parse_head) # [0,1] | ||
| parse_mask = torch.from_numpy(parse_mask) # [0,1] | ||
| parser_mask_fixed = torch.from_numpy(parser_mask_fixed) | ||
| parser_mask_changeable = torch.from_numpy(parser_mask_changeable) | ||
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| # dilation | ||
| parse_without_cloth = np.logical_and( | ||
| parse_shape, np.logical_not(parse_mask)) | ||
| parse_mask = parse_mask.cpu().numpy() | ||
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| width = size[0] | ||
| height = size[1] | ||
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| im_arms = Image.new('L', (width, height)) | ||
| arms_draw = ImageDraw.Draw(im_arms) | ||
| if category == 'dresses' or category == 'upper_body': | ||
| shoulder_right = tuple(np.multiply(pose_data[2, :2], height / 512.0)) | ||
| shoulder_left = tuple(np.multiply(pose_data[5, :2], height / 512.0)) | ||
| elbow_right = tuple(np.multiply(pose_data[3, :2], height / 512.0)) | ||
| elbow_left = tuple(np.multiply(pose_data[6, :2], height / 512.0)) | ||
| wrist_right = tuple(np.multiply(pose_data[4, :2], height / 512.0)) | ||
| wrist_left = tuple(np.multiply(pose_data[7, :2], height / 512.0)) | ||
| if wrist_right[0] <= 1. and wrist_right[1] <= 1.: | ||
| if elbow_right[0] <= 1. and elbow_right[1] <= 1.: | ||
| arms_draw.line( | ||
| [wrist_left, elbow_left, shoulder_left, shoulder_right], 'white', 30, 'curve') | ||
| else: | ||
| arms_draw.line([wrist_left, elbow_left, shoulder_left, shoulder_right, elbow_right], 'white', 30, | ||
| 'curve') | ||
| elif wrist_left[0] <= 1. and wrist_left[1] <= 1.: | ||
| if elbow_left[0] <= 1. and elbow_left[1] <= 1.: | ||
| arms_draw.line([shoulder_left, shoulder_right, | ||
| elbow_right, wrist_right], 'white', 30, 'curve') | ||
| else: | ||
| arms_draw.line([elbow_left, shoulder_left, shoulder_right, elbow_right, wrist_right], 'white', 30, | ||
| 'curve') | ||
| else: | ||
| arms_draw.line([wrist_left, elbow_left, shoulder_left, shoulder_right, elbow_right, wrist_right], 'white', | ||
| 30, 'curve') | ||
|
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| if height > 512: | ||
| im_arms = cv2.dilate(np.float32(im_arms), np.ones( | ||
| (10, 10), np.uint16), iterations=5) | ||
| elif height > 256: | ||
| im_arms = cv2.dilate(np.float32(im_arms), np.ones( | ||
| (5, 5), np.uint16), iterations=5) | ||
| hands = np.logical_and(np.logical_not(im_arms), arms) | ||
| parse_mask += im_arms | ||
| parser_mask_fixed += hands | ||
|
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||
| # delete neck | ||
| parse_head_2 = torch.clone(parse_head) | ||
| if category == 'dresses' or category == 'upper_body': | ||
| points = [] | ||
| points.append(np.multiply(pose_data[2, :2], height / 512.0)) | ||
| points.append(np.multiply(pose_data[5, :2], height / 512.0)) | ||
| x_coords, y_coords = zip(*points) | ||
| A = np.vstack([x_coords, np.ones(len(x_coords))]).T | ||
| m, c = lstsq(A, y_coords, rcond=None)[0] | ||
| for i in range(parse_array.shape[1]): | ||
| y = i * m + c | ||
| parse_head_2[int(y - 20 * (height / 512.0)):, i] = 0 | ||
|
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| parser_mask_fixed = np.logical_or( | ||
| parser_mask_fixed, np.array(parse_head_2, dtype=np.uint16)) | ||
| parse_mask += np.logical_or(parse_mask, np.logical_and(np.array(parse_head, dtype=np.uint16), | ||
| np.logical_not(np.array(parse_head_2, dtype=np.uint16)))) | ||
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| if height > 512: | ||
| parse_mask = cv2.dilate(parse_mask, np.ones( | ||
| (20, 20), np.uint16), iterations=5) | ||
| elif height > 256: | ||
| parse_mask = cv2.dilate(parse_mask, np.ones( | ||
| (10, 10), np.uint16), iterations=5) | ||
| else: | ||
| parse_mask = cv2.dilate(parse_mask, np.ones( | ||
| (5, 5), np.uint16), iterations=5) | ||
| parse_mask = np.logical_and( | ||
| parser_mask_changeable, np.logical_not(parse_mask)) | ||
| parse_mask_total = np.logical_or(parse_mask, parser_mask_fixed) | ||
| mask = 1 - parse_mask_total | ||
| mask = Image.fromarray(torch.stack([mask] * 3, dim=-1).numpy() * 255) | ||
| return mask |
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