Spaces:
Runtime error
Runtime error
File size: 2,645 Bytes
e875957 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
import argparse
import os
import sys
import numpy as np
import torch
import torchvision.transforms as transforms
from PIL import Image
from networks.drn_seg import DRNSeg
from utils.tools import *
from utils.visualize import *
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
"--input_path", required=True, help="the model input")
parser.add_argument(
"--dest_folder", required=True, help="folder to store the results")
parser.add_argument(
"--model_path", required=True, help="path to the drn model")
parser.add_argument(
"--gpu_id", default='0', help="the id of the gpu to run model on")
parser.add_argument(
"--no_crop",
action="store_true",
help="do not use a face detector, instead run on the full input image")
args = parser.parse_args()
img_path = args.input_path
dest_folder = args.dest_folder
model_path = args.model_path
gpu_id = args.gpu_id
# Loading the model
if torch.cuda.is_available():
device = 'cuda:{}'.format(gpu_id)
else:
device = 'cpu'
model = DRNSeg(2)
state_dict = torch.load(model_path, map_location=device)
model.load_state_dict(state_dict['model'])
model.to(device)
model.eval()
# Data preprocessing
tf = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
im_w, im_h = Image.open(img_path).size
if args.no_crop:
face = Image.open(img_path).convert('RGB')
else:
faces = face_detection(img_path, verbose=False)
if len(faces) == 0:
print("no face detected by dlib, exiting")
sys.exit()
face, box = faces[0]
face = resize_shorter_side(face, 400)[0]
face_tens = tf(face).to(device)
# Warping field prediction
with torch.no_grad():
flow = model(face_tens.unsqueeze(0))[0].cpu().numpy()
flow = np.transpose(flow, (1, 2, 0))
h, w, _ = flow.shape
# Undoing the warps
modified = face.resize((w, h), Image.BICUBIC)
modified_np = np.asarray(modified)
reverse_np = warp(modified_np, flow)
reverse = Image.fromarray(reverse_np)
# Saving the results
modified.save(
os.path.join(dest_folder, 'cropped_input.jpg'),
quality=90)
reverse.save(
os.path.join(dest_folder, 'warped.jpg'),
quality=90)
flow_magn = np.sqrt(flow[:, :, 0]**2 + flow[:, :, 1]**2)
save_heatmap_cv(
modified_np, flow_magn,
os.path.join(dest_folder, 'heatmap.jpg'))
|