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Zero
File size: 8,402 Bytes
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import pdb
from pathlib import Path
import sys
PROJECT_ROOT = Path(__file__).absolute().parents[0].absolute()
sys.path.insert(0, str(PROJECT_ROOT))
import os
import torch
import numpy as np
import cv2
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from datasets.simple_extractor_dataset import SimpleFolderDataset
from utils.transforms import transform_logits
from tqdm import tqdm
from PIL import Image
def get_palette(num_cls):
""" Returns the color map for visualizing the segmentation mask.
Args:
num_cls: Number of classes
Returns:
The color map
"""
n = num_cls
palette = [0] * (n * 3)
for j in range(0, n):
lab = j
palette[j * 3 + 0] = 0
palette[j * 3 + 1] = 0
palette[j * 3 + 2] = 0
i = 0
while lab:
palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
i += 1
lab >>= 3
return palette
def delete_irregular(logits_result):
parsing_result = np.argmax(logits_result, axis=2)
upper_cloth = np.where(parsing_result == 4, 255, 0)
contours, hierarchy = cv2.findContours(upper_cloth.astype(np.uint8),
cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)
area = []
for i in range(len(contours)):
a = cv2.contourArea(contours[i], True)
area.append(abs(a))
if len(area) != 0:
top = area.index(max(area))
M = cv2.moments(contours[top])
cY = int(M["m01"] / M["m00"])
dresses = np.where(parsing_result == 7, 255, 0)
contours_dress, hierarchy_dress = cv2.findContours(dresses.astype(np.uint8),
cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)
area_dress = []
for j in range(len(contours_dress)):
a_d = cv2.contourArea(contours_dress[j], True)
area_dress.append(abs(a_d))
if len(area_dress) != 0:
top_dress = area_dress.index(max(area_dress))
M_dress = cv2.moments(contours_dress[top_dress])
cY_dress = int(M_dress["m01"] / M_dress["m00"])
wear_type = "dresses"
if len(area) != 0:
if len(area_dress) != 0 and cY_dress > cY:
irregular_list = np.array([4, 5, 6])
logits_result[:, :, irregular_list] = -1
else:
irregular_list = np.array([5, 6, 7, 8, 9, 10, 12, 13])
logits_result[:cY, :, irregular_list] = -1
wear_type = "cloth_pant"
parsing_result = np.argmax(logits_result, axis=2)
# pad border
parsing_result = np.pad(parsing_result, pad_width=1, mode='constant', constant_values=0)
return parsing_result, wear_type
def hole_fill(img):
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)
# keep large area in skin case
for j in range(len(area)):
if j != i and area[i] > 2000:
cv2.drawContours(refine_mask, contours, j, color=255, thickness=-1)
return refine_mask
def refine_hole(parsing_result_filled, parsing_result, arm_mask):
filled_hole = cv2.bitwise_and(np.where(parsing_result_filled == 4, 255, 0),
np.where(parsing_result != 4, 255, 0)) - arm_mask * 255
contours, hierarchy = cv2.findContours(filled_hole, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)
refine_hole_mask = np.zeros_like(parsing_result).astype(np.uint8)
for i in range(len(contours)):
a = cv2.contourArea(contours[i], True)
# keep hole > 2000 pixels
if abs(a) > 2000:
cv2.drawContours(refine_hole_mask, contours, i, color=255, thickness=-1)
return refine_hole_mask + arm_mask
def onnx_inference(session, lip_session, input_dir):
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.406, 0.456, 0.485], std=[0.225, 0.224, 0.229])
])
dataset = SimpleFolderDataset(root=input_dir, input_size=[512, 512], transform=transform)
dataloader = DataLoader(dataset)
with torch.no_grad():
for _, batch in enumerate(tqdm(dataloader)):
image, meta = batch
c = meta['center'].numpy()[0]
s = meta['scale'].numpy()[0]
w = meta['width'].numpy()[0]
h = meta['height'].numpy()[0]
output = session.run(None, {"input.1": image.numpy().astype(np.float32)})
upsample = torch.nn.Upsample(size=[512, 512], mode='bilinear', align_corners=True)
upsample_output = upsample(torch.from_numpy(output[1][0]).unsqueeze(0))
upsample_output = upsample_output.squeeze()
upsample_output = upsample_output.permute(1, 2, 0) # CHW -> HWC
logits_result = transform_logits(upsample_output.data.cpu().numpy(), c, s, w, h, input_size=[512, 512])
parsing_result = np.argmax(logits_result, axis=2)
parsing_result = np.pad(parsing_result, pad_width=1, mode='constant', constant_values=0)
# try holefilling the clothes part
arm_mask = (parsing_result == 14).astype(np.float32) \
+ (parsing_result == 15).astype(np.float32)
upper_cloth_mask = (parsing_result == 4).astype(np.float32) + arm_mask
img = np.where(upper_cloth_mask, 255, 0)
dst = hole_fill(img.astype(np.uint8))
parsing_result_filled = dst / 255 * 4
parsing_result_woarm = np.where(parsing_result_filled == 4, parsing_result_filled, parsing_result)
# add back arm and refined hole between arm and cloth
refine_hole_mask = refine_hole(parsing_result_filled.astype(np.uint8), parsing_result.astype(np.uint8),
arm_mask.astype(np.uint8))
parsing_result = np.where(refine_hole_mask, parsing_result, parsing_result_woarm)
# remove padding
parsing_result = parsing_result[1:-1, 1:-1]
dataset_lip = SimpleFolderDataset(root=input_dir, input_size=[473, 473], transform=transform)
dataloader_lip = DataLoader(dataset_lip)
with torch.no_grad():
for _, batch in enumerate(tqdm(dataloader_lip)):
image, meta = batch
c = meta['center'].numpy()[0]
s = meta['scale'].numpy()[0]
w = meta['width'].numpy()[0]
h = meta['height'].numpy()[0]
output_lip = lip_session.run(None, {"input.1": image.numpy().astype(np.float32)})
upsample = torch.nn.Upsample(size=[473, 473], mode='bilinear', align_corners=True)
upsample_output_lip = upsample(torch.from_numpy(output_lip[1][0]).unsqueeze(0))
upsample_output_lip = upsample_output_lip.squeeze()
upsample_output_lip = upsample_output_lip.permute(1, 2, 0) # CHW -> HWC
logits_result_lip = transform_logits(upsample_output_lip.data.cpu().numpy(), c, s, w, h,
input_size=[473, 473])
parsing_result_lip = np.argmax(logits_result_lip, axis=2)
# add neck parsing result
neck_mask = np.logical_and(np.logical_not((parsing_result_lip == 13).astype(np.float32)),
(parsing_result == 11).astype(np.float32))
parsing_result = np.where(neck_mask, 18, parsing_result)
palette = get_palette(19)
output_img = Image.fromarray(np.asarray(parsing_result, dtype=np.uint8))
output_img.putpalette(palette)
face_mask = torch.from_numpy((parsing_result == 11).astype(np.float32))
return output_img, face_mask
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