def _process_single_cat_file(file, target_dir):
"""
Processes a single file of the cat dataset
Parameters
----------
file : str
the file to process
target_dir : str
the target directory
"""
file = os.path.abspath(file)
target_dir = os.path.abspath(target_dir)
pd_frame = pd.read_csv(str(file) + ".cat", sep=' ', header=None)
landmarks = (pd_frame.as_matrix()[0][1:-1]).reshape((-1, 2))
# switch xy
landmarks[:, [0, 1]] = landmarks[:, [1, 0]]
target_file = os.path.join(
target_dir,
os.path.split(os.path.split(file)[0])[-1] + "_" +
os.path.split(file)[-1])
# export landmarks
pts_exporter(landmarks, str(target_file.rsplit(".", 1)[0]) + ".pts")
# move image file
shutil.move(file, target_file)
os.remove(file + ".cat")
def test_io():
lmks = np.loadtxt(
os.path.join(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
"example_files", "lenna.txt"))
ljson_exporter(lmks, "./lmks.ljson")
assert os.path.isfile("./lmks.ljson")
pts_exporter(lmks, "./lmks.pts")
assert os.path.isfile("./lmks.pts")
lmks_ljson = ljson_importer("./lmks.ljson")
assert (lmks == lmks_ljson).all()
lmks_pts = pts_importer("./lmks.pts")
assert (lmks == lmks_ljson).all()
os.remove("./lmks.ljson")
os.remove("./lmks.pts")
def predict():
"""
Predicts file directory with network specified by files to output path
"""
import numpy as np
import torch
from tqdm import tqdm
import os
from matplotlib import pyplot as plt
import sys
from shapenet.utils.load_config_file import Config
from shapenet.layer import HomogeneousShapeLayer
from shapenet.networks import SingleShapeNetwork
from shapedata.single_shape import SingleShapeDataProcessing, \
SingleShapeSingleImage2D
from shapedata.io import pts_exporter
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-v",
"--visualize",
action="store_true",
help="If Flag is specified, results will be plotted")
parser.add_argument("-d", "--in_path", type=str, help="Input Data Dir")
parser.add_argument("-s",
"--out_path",
default="./outputs",
type=str,
help="Output Data Dir")
parser.add_argument("-w", "--weight_file", type=str, help="Model Weights")
parser.add_argument("-c", "--config_file", type=str, help="Configuration")
args = parser.parse_args()
config = Config()
config_dict = config(os.path.abspath(args.config_file))
try:
net = torch.jit.load(os.path.abspath(args.weight_file))
net.eval()
net.cpu()
except RuntimeError:
net_layer = HomogeneousShapeLayer
if config_dict["training"].pop("mixed_prec", False):
try:
from apex import amp
amp.init()
except:
pass
shapes = np.load(
os.path.abspath(config_dict["layer"].pop("pca_path"))
)["shapes"][:config_dict["layer"].pop("num_shape_params") + 1]
net = SingleShapeNetwork(net_layer, {
"shapes": shapes,
**config_dict["layer"]
},
img_size=config_dict["data"]["img_size"],
**config_dict["network"])
try:
net.load_state_dict(
torch.load(os.path.abspath(
args.weight_file))["state_dict"]["model"])
except KeyError:
net.load_state_dict(torch.load(os.path.abspath(args.weight_file)))
net = net.to("cpu")
net = net.eval()
data = SingleShapeDataProcessing._get_files(os.path.abspath(args.in_path),
extensions=[".png", ".jpg"])
def process_sample(sample, img_size, net, device, crop=0.1):
lmk_bounds = sample.get_landmark_bounds(sample.lmk)
min_y, min_x, max_y, max_x = lmk_bounds
range_x = max_x - min_x
range_y = max_y - min_y
max_range = max(range_x, range_y) * (1 + crop)
center_x = min_x + range_x / 2
center_y = min_y + range_y / 2
tmp = sample.crop(center_y - max_range / 2, center_x - max_range / 2,
center_y + max_range / 2, center_x + max_range / 2)
img_tensor = torch.from_numpy(tmp.to_grayscale().resize(
(img_size, img_size)).img.transpose(2, 0, 1)).to(
torch.float).unsqueeze(0).to(device)
pred = net(img_tensor).cpu().numpy()[0]
pred = pred * np.array([max_range / img_size, max_range / img_size])
pred = pred + np.asarray(
[center_y - max_range / 2, center_x - max_range / 2])
return pred
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
with torch.no_grad():
if torch.cuda.is_available():
net = net.cuda()
if args.visualize:
pred_path = os.path.join(os.path.abspath(args.out_path), "pred")
vis_path = os.path.join(os.path.abspath(args.out_path),
"visualization")
os.makedirs(vis_path, exist_ok=True)
else:
pred_path = os.path.abspath(args.out_path)
os.makedirs(pred_path, exist_ok=True)
print(data)
for idx, file in enumerate(tqdm(data)):
_data = SingleShapeSingleImage2D.from_files(file)
# pred = process_sample(_data, img_size=net.img_size, net=net,
# device=device)
pred = process_sample(_data, img_size=224, net=net, device=device)
fname = os.path.split(_data.img_file)[-1].rsplit(".", 1)[0]
if args.visualize:
view_kwargs = {}
if _data.is_gray:
view_kwargs["cmap"] = "gray"
fig = _data.view(True, **view_kwargs)
plt.gca().scatter(pred[:, 1], pred[:, 0], s=5, c="C1")
plt.gca().legend(["GT", "Pred"])
# plt.gca().scatter( pred[:, 0], s=5, c="C1")
# plt.gca().legend([ "Pred"])
plt.gcf().savefig(os.path.join(vis_path, fname + ".png"))
plt.close()
_data.save(pred_path, fname, "PTS")
pts_exporter(pred, os.path.join(pred_path, fname + "_pred.pts"))
print('pred:', pred)