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extract.py
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extract.py
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import numpy as np
import pandas as pd
import os
from tqdm import tqdm
from skimage.transform import resize
from util import frames2array
from imageio import mimsave
def extract_vgg(in_folder, is_video, image_shape, column):
from torchvision.models import vgg
from torchvision import transforms
from torch import nn
import torch
class VggConv(nn.Module):
def __init__(self):
super(VggConv, self).__init__()
self.original_model = vgg.vgg16(pretrained=True)
def forward(self, x):
x = self.original_model.features(x)
return x
net = VggConv().cuda()
out_df = {'file_name': [], 'frame_number': [], 'value': []}
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose([
transforms.ToTensor(),
normalize])
for file in tqdm(sorted(os.listdir(in_folder))):
video = frames2array(os.path.join(in_folder, file), is_video, image_shape, column)
for i, frame in enumerate(video):
with torch.no_grad():
frame = frame.astype('float32') / 255.0
frame = transform(frame)
frame = frame.unsqueeze(0).cuda()
feat = net(frame).data.cpu().numpy()
out_df['file_name'].append(file)
out_df['frame_number'].append(i)
out_df['value'].append(feat)
return pd.DataFrame(out_df)
def extract_face_pose(in_folder, is_video, image_shape, column):
import face_alignment
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False)
out_df = {'file_name': [], 'frame_number': [], 'value': []}
for file in tqdm(os.listdir(in_folder)):
video = frames2array(os.path.join(in_folder, file), is_video, image_shape, column)
for i, frame in enumerate(video):
kp = fa.get_landmarks(frame)
if kp is not None:
kp = kp[0]
out_df['file_name'].append(file)
out_df['frame_number'].append(i)
out_df['value'].append(kp)
return pd.DataFrame(out_df)
def extract_face_id(is_video, in_folder, image_shape, column):
from OpenFacePytorch.loadOpenFace import prepareOpenFace
from torch.autograd import Variable
import torch
from imageio import mimsave
net = prepareOpenFace(useCuda=True, gpuDevice=0, useMultiGPU=False).eval()
out_df = {'file_name': [], 'frame_number': [], 'value': []}
for file in tqdm(os.listdir(in_folder)):
video = frames2array(os.path.join(in_folder, file), is_video, image_shape, column)
for i, frame in enumerate(video):
frame = frame[..., ::-1]
frame = resize(frame, (96, 96))
frame = np.transpose(frame, (2, 0, 1))
with torch.no_grad():
frame = Variable(torch.Tensor(frame)).cuda()
frame = frame.unsqueeze(0)
id_vec = net(frame)[0].data.cpu().numpy()
out_df['file_name'].append(file)
out_df['frame_number'].append(i)
out_df['value'].append(id_vec)
return pd.DataFrame(out_df)
def extract_body_pose(in_folder, is_video, image_shape, column):
from pose_estimation.evaluate.coco_eval import get_multiplier, get_outputs, handle_paf_and_heat
import torch
from pose_estimation.network.rtpose_vgg import get_model
from pose_estimation.network.post import decode_pose
weight_name = 'pose_estimation/network/weight/pose_model.pth'
model = get_model('vgg19')
model.load_state_dict(torch.load(weight_name))
model = torch.nn.DataParallel(model).cuda()
model.float()
model.eval()
out_df = {'file_name': [], 'frame_number': [], 'value': []}
for file in tqdm(os.listdir(in_folder)):
video = frames2array(os.path.join(in_folder, file), is_video, image_shape, column)
for i, frame in enumerate(video):
frame = frame[..., ::-1]# B,G,R order
multiplier = get_multiplier(frame)
with torch.no_grad():
orig_paf, orig_heat = get_outputs(multiplier, frame, model, 'rtpose')
# Get results of flipped image
swapped_img = frame[:, ::-1, :]
flipped_paf, flipped_heat = get_outputs(multiplier, swapped_img, model, 'rtpose')
# compute averaged heatmap and paf
paf, heatmap = handle_paf_and_heat(orig_heat, flipped_heat, orig_paf, flipped_paf)
param = {'thre1': 0.1, 'thre2': 0.05, 'thre3': 0.5}
_, _, joint_list, _ = decode_pose(frame, param, heatmap, paf)
joint_list = np.array(joint_list)
tmp = -np.ones((18, 2))
if len(joint_list) != 0:
tmp[joint_list[:, -1].astype(int)] = joint_list[:, :2]
joint_list = tmp
out_df['file_name'].append(file)
out_df['frame_number'].append(i)
out_df['value'].append(joint_list)
return pd.DataFrame(out_df)
def extract_body_id(in_folder, is_video, image_shape, column):
from reid_baseline.model import ft_net
from torch import nn
from torchvision import transforms
import torch
net = ft_net(751)
net.load_state_dict(torch.load('reid_baseline/reid_model.pth'))
net.model.fc = nn.Sequential()
net.classifier = nn.Sequential()
net.cuda()
data_transforms = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((288,144), interpolation=3),
transforms.ToTensor(),
transforms.Normalize([0., 0., 0.], [255., 255., 255.]),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
out_df = {'file_name': [], 'frame_number': [], 'value': []}
for file in tqdm(os.listdir(in_folder)):
video = frames2array(os.path.join(in_folder, file), is_video, image_shape, column)
for i, frame in enumerate(video):
frame = data_transforms(frame).cuda()
with torch.no_grad():
id_vec = net(frame.unsqueeze(0))
id_vec = id_vec.data.cpu().numpy()
out_df['file_name'].append(file)
out_df['frame_number'].append(i)
out_df['value'].append(id_vec)
return pd.DataFrame(out_df)
if __name__ == "__main__":
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("--in_folder", default="test", help="Folder with images")
parser.add_argument("--out_file", default="test.pkl", help="Extracted values")
parser.add_argument("--is_video", dest='is_video', action='store_true', help="If this is a video.")
parser.add_argument("--column", default=0, type=int, help="Some generation tools stack multiple images together,"
" the index of the comlumn with right images")
parser.add_argument("--image_shape", default=(64, 64), type=lambda x: tuple([int(a) for a in x.split(',')]),
help="Image shape")
parser.add_argument("--type", default='body_id', choices=['face_id', 'face_pose', 'body_id', 'body_pose', 'vgg'],
help="Type of info to extract")
args = parser.parse_args()
func = locals()["extract_" + args.type]
out_file = args.out_file
del args.type, args.out_file
df = func(**vars(args))
df.to_pickle(out_file)