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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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def pil_to_binary_mask(pil_image, threshold=0):
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np_image = np.array(pil_image)
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grayscale_image = Image.fromarray(np_image).convert("L")
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binary_mask = np.array(grayscale_image) > threshold
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mask = np.zeros(binary_mask.shape, dtype=np.uint8)
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for i in range(binary_mask.shape[0]):
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for j in range(binary_mask.shape[1]):
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if binary_mask[i,j] == True :
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mask[i,j] = 1
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mask = (mask*255).astype(np.uint8)
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output_mask = Image.fromarray(mask)
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return output_mask |
