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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import numpy as np
import skimage.io as io
# from FaceSDK.face_sdk import FaceDetection
# from face_sdk import FaceDetection
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from skimage.transform import SimilarityTransform
from skimage.transform import warp
from PIL import Image
import torch.nn.functional as F
import torchvision as tv
import torchvision.utils as vutils
import time
import cv2
import os
from skimage import img_as_ubyte
import json
import argparse
import dlib
def _standard_face_pts():
pts = (
np.array([196.0, 226.0, 316.0, 226.0, 256.0, 286.0, 220.0, 360.4, 292.0, 360.4], np.float32) / 256.0
- 1.0
)
return np.reshape(pts, (5, 2))
def _origin_face_pts():
pts = np.array([196.0, 226.0, 316.0, 226.0, 256.0, 286.0, 220.0, 360.4, 292.0, 360.4], np.float32)
return np.reshape(pts, (5, 2))
def get_landmark(face_landmarks, id):
part = face_landmarks.part(id)
x = part.x
y = part.y
return (x, y)
def search(face_landmarks):
x1, y1 = get_landmark(face_landmarks, 36)
x2, y2 = get_landmark(face_landmarks, 39)
x3, y3 = get_landmark(face_landmarks, 42)
x4, y4 = get_landmark(face_landmarks, 45)
x_nose, y_nose = get_landmark(face_landmarks, 30)
x_left_mouth, y_left_mouth = get_landmark(face_landmarks, 48)
x_right_mouth, y_right_mouth = get_landmark(face_landmarks, 54)
x_left_eye = int((x1 + x2) / 2)
y_left_eye = int((y1 + y2) / 2)
x_right_eye = int((x3 + x4) / 2)
y_right_eye = int((y3 + y4) / 2)
results = np.array(
[
[x_left_eye, y_left_eye],
[x_right_eye, y_right_eye],
[x_nose, y_nose],
[x_left_mouth, y_left_mouth],
[x_right_mouth, y_right_mouth],
]
)
return results
def compute_transformation_matrix(img, landmark, normalize, target_face_scale=1.0):
std_pts = _standard_face_pts() # [-1,1]
target_pts = (std_pts * target_face_scale + 1) / 2 * 512.0
# print(target_pts)
h, w, c = img.shape
if normalize == True:
landmark[:, 0] = landmark[:, 0] / h * 2 - 1.0
landmark[:, 1] = landmark[:, 1] / w * 2 - 1.0
# print(landmark)
affine = SimilarityTransform()
affine.estimate(target_pts, landmark)
return affine.params
def show_detection(image, box, landmark):
plt.imshow(image)
print(box[2] - box[0])
plt.gca().add_patch(
Rectangle(
(box[1], box[0]), box[2] - box[0], box[3] - box[1], linewidth=1, edgecolor="r", facecolor="none"
)
)
plt.scatter(landmark[0][0], landmark[0][1])
plt.scatter(landmark[1][0], landmark[1][1])
plt.scatter(landmark[2][0], landmark[2][1])
plt.scatter(landmark[3][0], landmark[3][1])
plt.scatter(landmark[4][0], landmark[4][1])
plt.show()
def affine2theta(affine, input_w, input_h, target_w, target_h):
# param = np.linalg.inv(affine)
param = affine
theta = np.zeros([2, 3])
theta[0, 0] = param[0, 0] * input_h / target_h
theta[0, 1] = param[0, 1] * input_w / target_h
theta[0, 2] = (2 * param[0, 2] + param[0, 0] * input_h + param[0, 1] * input_w) / target_h - 1
theta[1, 0] = param[1, 0] * input_h / target_w
theta[1, 1] = param[1, 1] * input_w / target_w
theta[1, 2] = (2 * param[1, 2] + param[1, 0] * input_h + param[1, 1] * input_w) / target_w - 1
return theta
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--url", type=str, default="/home/jingliao/ziyuwan/celebrities", help="input")
parser.add_argument(
"--save_url", type=str, default="/home/jingliao/ziyuwan/celebrities_detected_face_reid", help="output"
)
opts = parser.parse_args()
url = opts.url
save_url = opts.save_url
### If the origin url is None, then we don't need to reid the origin image
os.makedirs(url, exist_ok=True)
os.makedirs(save_url, exist_ok=True)
face_detector = dlib.get_frontal_face_detector()
landmark_locator = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
count = 0
map_id = {}
for x in os.listdir(url):
img_url = os.path.join(url, x)
pil_img = Image.open(img_url).convert("RGB")
image = np.array(pil_img)
start = time.time()
faces = face_detector(image)
done = time.time()
if len(faces) == 0:
print("Warning: There is no face in %s" % (x))
continue
print(len(faces))
if len(faces) > 0:
for face_id in range(len(faces)):
current_face = faces[face_id]
face_landmarks = landmark_locator(image, current_face)
current_fl = search(face_landmarks)
affine = compute_transformation_matrix(image, current_fl, False, target_face_scale=1.3)
aligned_face = warp(image, affine, output_shape=(512, 512, 3))
img_name = x[:-4] + "_" + str(face_id + 1)
io.imsave(os.path.join(save_url, img_name + ".png"), img_as_ubyte(aligned_face))
count += 1
if count % 1000 == 0:
print("%d have finished ..." % (count))
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