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import os
import cv2
import math
import argparse
import numpy as np
from tqdm import tqdm
import torch
# private package
from lib import utility
class GetCropMatrix():
"""
from_shape -> transform_matrix
"""
def __init__(self, image_size, target_face_scale, align_corners=False):
self.image_size = image_size
self.target_face_scale = target_face_scale
self.align_corners = align_corners
def _compose_rotate_and_scale(self, angle, scale, shift_xy, from_center, to_center):
cosv = math.cos(angle)
sinv = math.sin(angle)
fx, fy = from_center
tx, ty = to_center
acos = scale * cosv
asin = scale * sinv
a0 = acos
a1 = -asin
a2 = tx - acos * fx + asin * fy + shift_xy[0]
b0 = asin
b1 = acos
b2 = ty - asin * fx - acos * fy + shift_xy[1]
rot_scale_m = np.array([
[a0, a1, a2],
[b0, b1, b2],
[0.0, 0.0, 1.0]
], np.float32)
return rot_scale_m
def process(self, scale, center_w, center_h):
if self.align_corners:
to_w, to_h = self.image_size - 1, self.image_size - 1
else:
to_w, to_h = self.image_size, self.image_size
rot_mu = 0
scale_mu = self.image_size / (scale * self.target_face_scale * 200.0)
shift_xy_mu = (0, 0)
matrix = self._compose_rotate_and_scale(
rot_mu, scale_mu, shift_xy_mu,
from_center=[center_w, center_h],
to_center=[to_w / 2.0, to_h / 2.0])
return matrix
class TransformPerspective():
"""
image, matrix3x3 -> transformed_image
"""
def __init__(self, image_size):
self.image_size = image_size
def process(self, image, matrix):
return cv2.warpPerspective(
image, matrix, dsize=(self.image_size, self.image_size),
flags=cv2.INTER_LINEAR, borderValue=0)
class TransformPoints2D():
"""
points (nx2), matrix (3x3) -> points (nx2)
"""
def process(self, srcPoints, matrix):
# nx3
desPoints = np.concatenate([srcPoints, np.ones_like(srcPoints[:, [0]])], axis=1)
desPoints = desPoints @ np.transpose(matrix) # nx3
desPoints = desPoints[:, :2] / desPoints[:, [2, 2]]
return desPoints.astype(srcPoints.dtype)
class Alignment:
def __init__(self, args, model_path, dl_framework, device_ids):
self.input_size = 256
self.target_face_scale = 1.0
self.dl_framework = dl_framework
# model
if self.dl_framework == "pytorch":
# conf
self.config = utility.get_config(args)
self.config.device_id = device_ids[0]
# set environment
utility.set_environment(self.config)
self.config.init_instance()
if self.config.logger is not None:
self.config.logger.info("Loaded configure file %s: %s" % (args.config_name, self.config.id))
self.config.logger.info("\n" + "\n".join(["%s: %s" % item for item in self.config.__dict__.items()]))
net = utility.get_net(self.config)
if device_ids == [-1]:
checkpoint = torch.load(model_path, map_location="cpu")
else:
checkpoint = torch.load(model_path)
net.load_state_dict(checkpoint["net"])
net = net.to(self.config.device_id)
net.eval()
self.alignment = net
else:
assert False
self.getCropMatrix = GetCropMatrix(image_size=self.input_size, target_face_scale=self.target_face_scale,
align_corners=True)
self.transformPerspective = TransformPerspective(image_size=self.input_size)
self.transformPoints2D = TransformPoints2D()
def norm_points(self, points, align_corners=False):
if align_corners:
# [0, SIZE-1] -> [-1, +1]
return points / torch.tensor([self.input_size - 1, self.input_size - 1]).to(points).view(1, 1, 2) * 2 - 1
else:
# [-0.5, SIZE-0.5] -> [-1, +1]
return (points * 2 + 1) / torch.tensor([self.input_size, self.input_size]).to(points).view(1, 1, 2) - 1
def denorm_points(self, points, align_corners=False):
if align_corners:
# [-1, +1] -> [0, SIZE-1]
return (points + 1) / 2 * torch.tensor([self.input_size - 1, self.input_size - 1]).to(points).view(1, 1, 2)
else:
# [-1, +1] -> [-0.5, SIZE-0.5]
return ((points + 1) * torch.tensor([self.input_size, self.input_size]).to(points).view(1, 1, 2) - 1) / 2
def preprocess(self, image, scale, center_w, center_h):
matrix = self.getCropMatrix.process(scale, center_w, center_h)
input_tensor = self.transformPerspective.process(image, matrix)
input_tensor = input_tensor[np.newaxis, :]
input_tensor = torch.from_numpy(input_tensor)
input_tensor = input_tensor.float().permute(0, 3, 1, 2)
input_tensor = input_tensor / 255.0 * 2.0 - 1.0
input_tensor = input_tensor.to(self.config.device_id)
return input_tensor, matrix
def postprocess(self, srcPoints, coeff):
# dstPoints = self.transformPoints2D.process(srcPoints, coeff)
# matrix^(-1) * src = dst
# src = matrix * dst
dstPoints = np.zeros(srcPoints.shape, dtype=np.float32)
for i in range(srcPoints.shape[0]):
dstPoints[i][0] = coeff[0][0] * srcPoints[i][0] + coeff[0][1] * srcPoints[i][1] + coeff[0][2]
dstPoints[i][1] = coeff[1][0] * srcPoints[i][0] + coeff[1][1] * srcPoints[i][1] + coeff[1][2]
return dstPoints
def analyze(self, image, scale, center_w, center_h):
input_tensor, matrix = self.preprocess(image, scale, center_w, center_h)
if self.dl_framework == "pytorch":
with torch.no_grad():
output = self.alignment(input_tensor)
landmarks = output[-1][0]
else:
assert False
landmarks = self.denorm_points(landmarks)
landmarks = landmarks.data.cpu().numpy()[0]
landmarks = self.postprocess(landmarks, np.linalg.inv(matrix))
return landmarks
def L2(p1, p2):
return np.linalg.norm(p1 - p2)
def NME(landmarks_gt, landmarks_pv):
pts_num = landmarks_gt.shape[0]
if pts_num == 29:
left_index = 16
right_index = 17
elif pts_num == 68:
left_index = 36
right_index = 45
elif pts_num == 98:
left_index = 60
right_index = 72
nme = 0
eye_span = L2(landmarks_gt[left_index], landmarks_gt[right_index])
for i in range(pts_num):
error = L2(landmarks_pv[i], landmarks_gt[i])
nme += error / eye_span
nme /= pts_num
return nme
def evaluate(args, model_path, metadata_path, device_ids, mode):
alignment = Alignment(args, model_path, dl_framework="pytorch", device_ids=device_ids)
config = alignment.config
nme_sum = 0
with open(metadata_path, 'r') as f:
lines = f.readlines()
for k, line in enumerate(tqdm(lines)):
item = line.strip().split("\t")
image_name, landmarks_5pts, landmarks_gt, scale, center_w, center_h = item[:6]
# image & keypoints alignment
image_name = image_name.replace('\\', '/')
image_name = image_name.replace('//msr-facestore/Workspace/MSRA_EP_Allergan/users/yanghuan/training_data/wflw/rawImages/', '')
image_name = image_name.replace('./rawImages/', '')
image_path = os.path.join(config.image_dir, image_name)
landmarks_gt = np.array(list(map(float, landmarks_gt.split(","))), dtype=np.float32).reshape(-1, 2)
scale, center_w, center_h = float(scale), float(center_w), float(center_h)
image = cv2.imread(image_path)
landmarks_pv = alignment.analyze(image, scale, center_w, center_h)
# NME
if mode == "nme":
nme = NME(landmarks_gt, landmarks_pv)
nme_sum += nme
# print("Current NME(%d): %f" % (k + 1, (nme_sum / (k + 1))))
else:
pass
if mode == "nme":
print("Final NME: %f" % (100*nme_sum / (k + 1)))
else:
pass
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Evaluation script")
parser.add_argument("--config_name", type=str, default="alignment", help="set configure file name")
parser.add_argument("--model_path", type=str, default="./train.pkl", help="the path of model")
parser.add_argument("--data_definition", type=str, default='WFLW', help="COFW/300W/WFLW")
parser.add_argument("--metadata_path", type=str, default="", help="the path of metadata")
parser.add_argument("--image_dir", type=str, default="", help="the path of image")
parser.add_argument("--device_ids", type=str, default="0", help="set device ids, -1 means use cpu device, >= 0 means use gpu device")
parser.add_argument("--mode", type=str, default="nme", help="set the evaluate mode: nme")
args = parser.parse_args()
device_ids = list(map(int, args.device_ids.split(",")))
evaluate(
args,
model_path=args.model_path,
metadata_path=args.metadata_path,
device_ids=device_ids,
mode=args.mode)
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