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import cv2
import math
import copy
import numpy as np
import argparse
import torch
import json
# private package
from lib import utility
from FaceBoxesV2.faceboxes_detector import *
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"])
if self.config.device_id == -1:
net = net.cpu()
else:
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
if self.config.device_id == -1:
input_tensor = input_tensor.cpu()
else:
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
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="inference script")
parser.add_argument('--folder_path', type=str, help='Path to image folder')
args = parser.parse_args()
# args.folder_path = '/media/gyalex/Data/flame/ph_test/head_images/flame/image'
current_path = os.getcwd()
use_gpu = True
########### face detection ############
if use_gpu:
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
current_path = os.getcwd()
det_model_path = os.path.join(current_path, 'preprocess', 'submodules', 'Landmark_detection', 'FaceBoxesV2/weights/FaceBoxesV2.pth')
detector = FaceBoxesDetector('FaceBoxes', det_model_path, use_gpu, device)
########### facial alignment ############
model_path = os.path.join(current_path, 'preprocess', 'submodules', 'Landmark_detection', 'weights/68_keypoints_model.pkl')
if use_gpu:
device_ids = [0]
else:
device_ids = [-1]
args.config_name = 'alignment'
alignment = Alignment(args, model_path, dl_framework="pytorch", device_ids=device_ids)
img_path_list = os.listdir(args.folder_path)
kpts_code = dict()
########### inference ############
for file_name in img_path_list:
abs_path = os.path.join(args.folder_path, file_name)
image = cv2.imread(abs_path)
image_draw = copy.deepcopy(image)
detections, _ = detector.detect(image, 0.6, 1)
for idx in range(len(detections)):
x1_ori = detections[idx][2]
y1_ori = detections[idx][3]
x2_ori = x1_ori + detections[idx][4]
y2_ori = y1_ori + detections[idx][5]
scale = max(x2_ori - x1_ori, y2_ori - y1_ori) / 180
center_w = (x1_ori + x2_ori) / 2
center_h = (y1_ori + y2_ori) / 2
scale, center_w, center_h = float(scale), float(center_w), float(center_h)
landmarks_pv = alignment.analyze(image, scale, center_w, center_h)
landmarks_pv_list = landmarks_pv.tolist()
for num in range(landmarks_pv.shape[0]):
cv2.circle(image_draw, (round(landmarks_pv[num][0]), round(landmarks_pv[num][1])),
2, (0, 255, 0), -1)
kpts_code[file_name] = landmarks_pv_list
save_path = args.folder_path[:-5] + 'landmark'
cv2.imwrite(os.path.join(save_path, file_name), image_draw)
path = args.folder_path[:-5]
json.dump(kpts_code, open(os.path.join(path, 'keypoint.json'), 'w'))
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