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import cv2
import numpy as np
from Graphonomy.human_parser import HumanParser
from Graphonomy.dataset_settings import dataset_settings
cihp_label = dataset_settings['cihp']['label']
import random
def slide_window_garment_mask_refine(human_parser: HumanParser, num_window, raw_image):
trans2roi=np.zeros((2,3),np.float32)
trans2roi[0,0]=1
trans2roi[1, 1] = 1
height = raw_image.shape[0]
width = raw_image.shape[1]
trans_list = []
bias = width * 0.1
txs = np.linspace(int(-bias), int(bias), num_window).tolist()
tys = np.linspace(int(-bias), int(bias), num_window).tolist()
for i in range(num_window):
for j in range(num_window):
tx = int(txs[i])
ty = int(tys[j])
new_trans = trans2roi.copy()
new_trans[0, 2] += tx
new_trans[1, 2] += ty
trans_list.append(new_trans)
raw_mask_list = []
raw_valid_mask_list = []
for i in range(len(trans_list)):
roi_img = cv2.warpAffine(raw_image, trans_list[i], (width, height), flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0, 0, 0))
garment_mask = get_garment_mask(human_parser, roi_img).astype(np.uint8) * 255
raw_mask = cv2.warpAffine(garment_mask, get_inverse_trans(trans_list[i]), (width, height),
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0))
raw_valid_mask = get_trans_mask(trans_list[i], height, width)
raw_mask_list.append((raw_mask > 128).astype(np.uint8))
raw_valid_mask_list.append(raw_valid_mask)
sum_mask = np.zeros((height, width), dtype=np.uint8)
sum_valid_mask = np.zeros((height, width), dtype=np.uint8)
for i in range(num_window*num_window):
sum_mask += raw_mask_list[i]
sum_valid_mask += raw_valid_mask_list[i]
result_mask = sum_mask.astype(np.float32) > (sum_valid_mask.astype(np.float32) * 0.5)
#result_mask = raw_valid_mask_list[-1].astype(np.float32) * 0.5 > 0
return result_mask
def get_trans_mask(trans, height, width):
valid_mask = np.ones((height, width), dtype=np.uint8)
raw_valid_mask = cv2.warpAffine(valid_mask, get_inverse_trans(trans), (width, height), flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0))
return raw_valid_mask
def get_garment_mask(human_parser: HumanParser, roi_img):
garment_mask = human_parser.GetAtrGarmentMask(roi_img,isRGB=False)
return garment_mask
def get_inverse_trans(trans):
full_matrix = np.vstack([trans, [0, 0, 1]])
# Compute the inverse of the 3x3 matrix
inverse_matrix = np.linalg.inv(full_matrix)
# Extract the top 2 rows for use with cv2.warpAffine
inverse_transform_matrix = inverse_matrix[:2, :]
return inverse_transform_matrix
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