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#!/usr/bin/python
# -*- encoding: utf-8 -*-
import numpy as np
from model import BiSeNet
import torch
import os
import os.path as osp
from PIL import Image
import torchvision.transforms as transforms
import cv2
from pathlib import Path
import configargparse
import tqdm
# import ttach as tta
def vis_parsing_maps(im, parsing_anno, stride, save_im=False, save_path='vis_results/parsing_map_on_im.jpg',
img_size=(512, 512)):
im = np.array(im)
vis_im = im.copy().astype(np.uint8)
vis_parsing_anno = parsing_anno.copy().astype(np.uint8)
vis_parsing_anno = cv2.resize(
vis_parsing_anno, None, fx=stride, fy=stride, interpolation=cv2.INTER_NEAREST)
vis_parsing_anno_color = np.zeros(
(vis_parsing_anno.shape[0], vis_parsing_anno.shape[1], 3)) + np.array([255, 255, 255]) # + 255
vis_parsing_anno_color_face = np.zeros(
(vis_parsing_anno.shape[0], vis_parsing_anno.shape[1], 3)) + np.array([255, 255, 255]) # + 255
num_of_class = np.max(vis_parsing_anno)
# print(num_of_class)
for pi in range(1, 14):
index = np.where(vis_parsing_anno == pi)
vis_parsing_anno_color[index[0], index[1], :] = np.array([255, 0, 0])
for pi in range(14, 16):
index = np.where(vis_parsing_anno == pi)
vis_parsing_anno_color[index[0], index[1], :] = np.array([0, 255, 0])
for pi in range(16, 17):
index = np.where(vis_parsing_anno == pi)
vis_parsing_anno_color[index[0], index[1], :] = np.array([0, 0, 255])
for pi in range(17, num_of_class+1):
index = np.where(vis_parsing_anno == pi)
vis_parsing_anno_color[index[0], index[1], :] = np.array([255, 0, 0])
vis_parsing_anno_color = vis_parsing_anno_color.astype(np.uint8)
index = np.where(vis_parsing_anno == num_of_class-1)
vis_im = cv2.resize(vis_parsing_anno_color, img_size,
interpolation=cv2.INTER_NEAREST)
if save_im:
cv2.imwrite(save_path, vis_im)
for pi in range(1, 7):
index = np.where(vis_parsing_anno == pi)
vis_parsing_anno_color_face[index[0], index[1], :] = np.array([255, 0, 0])
for pi in range(10, 14):
index = np.where(vis_parsing_anno == pi)
vis_parsing_anno_color_face[index[0], index[1], :] = np.array([255, 0, 0])
pad = 5
vis_parsing_anno_color_face = vis_parsing_anno_color_face.astype(np.uint8)
face_part = (vis_parsing_anno_color_face[..., 0] == 255) & (vis_parsing_anno_color_face[..., 1] == 0) & (vis_parsing_anno_color_face[..., 2] == 0)
face_coords = np.stack(np.nonzero(face_part), axis=-1)
sorted_inds = np.lexsort((-face_coords[:, 0], face_coords[:, 1]))
sorted_face_coords = face_coords[sorted_inds]
u, uid, ucnt = np.unique(sorted_face_coords[:, 1], return_index=True, return_counts=True)
bottom_face_coords = sorted_face_coords[uid] + np.array([pad, 0])
rows, cols, _ = vis_parsing_anno_color_face.shape
# 为了保证新的坐标在图片范围内
bottom_face_coords[:, 0] = np.clip(bottom_face_coords[:, 0], 0, rows - 1)
y_min = np.min(bottom_face_coords[:, 1])
y_max = np.max(bottom_face_coords[:, 1])
# 计算1和2部分的开始和结束位置
y_range = y_max - y_min
height_per_part = y_range // 4
start_y_part1 = y_min + height_per_part
end_y_part1 = start_y_part1 + height_per_part
start_y_part2 = end_y_part1
end_y_part2 = start_y_part2 + height_per_part
for coord in bottom_face_coords:
x, y = coord
start_x = max(x - pad, 0)
end_x = min(x + pad, rows)
if start_y_part1 <= y <= end_y_part1 or start_y_part2 <= y <= end_y_part2:
vis_parsing_anno_color_face[start_x:end_x, y] = [255, 0, 0]
# else:
# start_x = max(x - 2*pad, 0)
# end_x = max(x - pad, 0)
# vis_parsing_anno_color_face[start_x:end_x+1, y] = [255, 255, 255]
vis_im = cv2.GaussianBlur(vis_parsing_anno_color_face, (9, 9), cv2.BORDER_DEFAULT)
vis_im = cv2.resize(vis_im, img_size,
interpolation=cv2.INTER_NEAREST)
cv2.imwrite(save_path.replace('.png', '_face.png'), vis_im)
def evaluate(respth='./res/test_res', dspth='./data', cp='model_final_diss.pth'):
Path(respth).mkdir(parents=True, exist_ok=True)
print(f'[INFO] loading model...')
n_classes = 19
net = BiSeNet(n_classes=n_classes)
net.cuda()
net.load_state_dict(torch.load(cp))
net.eval()
to_tensor = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
image_paths = os.listdir(dspth)
with torch.no_grad():
for image_path in tqdm.tqdm(image_paths):
if image_path.endswith('.jpg') or image_path.endswith('.png'):
img = Image.open(osp.join(dspth, image_path))
ori_size = img.size
image = img.resize((512, 512), Image.BILINEAR)
image = image.convert("RGB")
img = to_tensor(image)
# test-time augmentation.
inputs = torch.unsqueeze(img, 0) # [1, 3, 512, 512]
outputs = net(inputs.cuda())
parsing = outputs.mean(0).cpu().numpy().argmax(0)
image_path = int(image_path[:-4])
image_path = str(image_path) + '.png'
vis_parsing_maps(image, parsing, stride=1, save_im=True, save_path=osp.join(respth, image_path), img_size=ori_size)
if __name__ == "__main__":
parser = configargparse.ArgumentParser()
parser.add_argument('--respath', type=str, default='./result/', help='result path for label')
parser.add_argument('--imgpath', type=str, default='./imgs/', help='path for input images')
parser.add_argument('--modelpath', type=str, default='data_utils/face_parsing/79999_iter.pth')
args = parser.parse_args()
evaluate(respth=args.respath, dspth=args.imgpath, cp=args.modelpath)
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