def main_camera():
#network initialize of cnn for stereo faces anti spoofing
modelpath = "./checkpoint_resnet/"
hpr = stereoSpoofingRecognition(modelpath, 0.7)
centerface = CenterFace(DETHEIGHT, DETWIDTH)
test_video(centerface, hpr, 0, calibed=True)
def test_image_tensorrt():
frame = cv2.imread("prj-python/000388.jpg")
# * set height and width
h, w = 480, 640 # must be 480* 640
landmarks = False
centerface = CenterFace(landmarks=landmarks)
if landmarks:
dets, lms = centerface(frame, h, w, threshold=0.35)
else:
dets = centerface(frame, h, w, threshold=0.35)
print("count = ", len(dets))
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(
frame,
(int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])),
(2, 255, 0),
1,
)
if landmarks:
for lm in lms:
for i in range(0, 5):
cv2.circle(frame, (int(lm[i * 2]), int(lm[i * 2 + 1])), 2,
(0, 0, 255), -1)
# Using cv2.imwrite() method
# Saving the image
cv2.imwrite("res_000388.jpg", frame)
# cv2.imshow("out", frame)
cv2.waitKey(0)
def test_image():
frame = cv2.imread('000388.jpg')
h, w = frame.shape[:2]
landmarks = True
centerface = CenterFace(h, w, landmarks=landmarks)
if landmarks:
dets, lms = centerface(frame, threshold=0.35)
else:
dets = centerface(frame, threshold=0.35)
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
if landmarks:
for lm in lms:
cv2.circle(frame, (int(lm[0]), int(lm[1])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[2]), int(lm[3])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[4]), int(lm[5])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[6]), int(lm[7])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[8]), int(lm[9])), 2, (0, 0, 255), -1)
print('python face num:', len(dets))
cv2.imshow('python', frame)
cv2.waitKey(0)
def test_widerface():
Path = 'widerface/WIDER_val/images/'
wider_face_mat = sio.loadmat(
'widerface/wider_face_split/wider_face_val.mat')
event_list = wider_face_mat['event_list']
file_list = wider_face_mat['file_list']
save_path = 'save_out/'
for index, event in enumerate(event_list):
file_list_item = file_list[index][0]
im_dir = event[0][0]
# print(save_path + im_dir)
if not os.path.exists(save_path + im_dir):
os.makedirs(save_path + im_dir)
landmarks = True
centerface = CenterFace(landmarks=landmarks)
for num, file in enumerate(file_list_item):
im_name = file[0][0]
zip_name = '%s/%s.jpg' % (im_dir, im_name)
print(os.path.join(Path, zip_name))
img = cv2.imread(os.path.join(Path, zip_name))
h, w = img.shape[:2]
if landmarks:
dets, lms = centerface(img, h, w, threshold=0.05)
else:
dets = centerface(img, threshold=0.05)
f = open(save_path + im_dir + '/' + im_name + '.txt', 'w')
f.write('{:s}\n'.format('%s/%s.jpg' % (im_dir, im_name)))
f.write('{:d}\n'.format(len(dets)))
for b in dets:
x1, y1, x2, y2, s = b
f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'.format(
x1, y1, (x2 - x1 + 1), (y2 - y1 + 1), s))
f.close()
print('event:%d num:%d' % (index + 1, num + 1))
def test_image(landmarks=False):
frame = cv2.imread('imgs/17.jpg')
# frame = cv2.resize(frame, (640, 640))
h, w = frame.shape[:2]
centerface = CenterFace(h, w, landmarks=landmarks)
if landmarks:
dets, lms = centerface(frame, threshold=0.3)
else:
dets = centerface(frame, threshold=0.3)
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
if landmarks:
for lm in lms:
cv2.circle(frame, (int(lm[0]), int(lm[1])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[2]), int(lm[3])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[4]), int(lm[5])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[6]), int(lm[7])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[8]), int(lm[9])), 2, (0, 0, 255), -1)
cv2.imwrite('centerface.jpg', frame)
cv2.imshow('out', frame)
cv2.waitKey(0)
def camera(count_im):
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
h, w = frame.shape[:2]
centerface = CenterFace()
global org_image
while True:
ret, frame = cap.read()
org_frame = frame.copy()
croped_face = frame.copy()
dets, lms = centerface(frame, h, w, threshold=0.35)
org_image = frame.copy()
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
croped_face = org_frame[int(boxes[1]):int(boxes[3]),
int(boxes[0]):int(boxes[2])]
cv2.imshow('out', frame)
cv2.imshow('face', croped_face)
cv2.imwrite(save_path + 'img_{}.jpg'.format(count_im), croped_face)
count_im += 1
# Press Q on keyboard to stop recording
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
def test_image(img_path):
"""[Face detection]
Args:
img_path ([str]): [path to the image]
"""
frame = cv2.imread(img_path)
h, w = frame.shape[:2]
landmarks = True
centerface = CenterFace(landmarks=landmarks)
if landmarks:
dets, lms = centerface(frame, h, w, threshold=0.35)
else:
dets = centerface(frame, threshold=0.35)
for det in dets:
#boxes, score = det[:4], det[4]
boxes = det[:4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])), (2, 255, 0), 2)
if landmarks:
for lm in lms:
for i in range(0, 5):
cv2.circle(frame, (int(lm[i * 2]), int(lm[i * 2 + 1])), 2,
(0, 0, 255), -1)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(32, 32))
plt.imshow(frame)
#plt.savefig('result_' + img_path, bbox_inches='tight')
plt.show()
def detect_face_by_centerface(img) -> list:
h, w = img.shape[:2]
cf = CenterFace(landmarks=True)
faces, landmarks = cf(img, h, w, threshold=0.5)
boxes = []
for face in faces:
(x1, y1, x2, y2), score = face[:4], face[4]
box = [int(y1), int(x2), int(y2), int(x1)]
boxes.append(box)
return boxes
def test_image():
frame = cv2.imread('000388.jpg')
h, w = frame.shape[:2]
centerface = CenterFace(h, w)
dets = centerface(frame, threshold=0.5)
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
cv2.imshow('out', frame)
cv2.waitKey(0)
def test_image(image_path, model_path):
frame = cv2.imread(image_path)
h, w = frame.shape[:2]
landmarks = True
centerface = CenterFace(model_path=model_path, landmarks=landmarks)
centerface.transform(h, w)
if landmarks:
dets, lms = centerface(frame, threshold=0.35)
else:
dets = centerface(frame, threshold=0.35)
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])), (int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
if landmarks:
for lm in lms:
cv2.circle(frame, (int(lm[0]), int(lm[1])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[2]), int(lm[3])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[4]), int(lm[5])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[6]), int(lm[7])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[8]), int(lm[9])), 2, (0, 0, 255), -1)
cv2.imshow('out', frame)
cv2.waitKey(0)
def test_image():
frame = cv2.imread('000388.jpg')
h, w = frame.shape[:2]
centerface = CenterFace(h, w)
dets, lms = centerface(frame, threshold=0.35)
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])), (int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
for lm in lms:
cv2.circle(frame, (int(lm[0]), int(lm[1])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[2]), int(lm[3])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[4]), int(lm[5])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[6]), int(lm[7])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[8]), int(lm[9])), 2, (0, 0, 255), -1)
cv2.imshow('out', frame)
cv2.waitKey(0)
def camera():
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
h, w = frame.shape[:2]
centerface = CenterFace(h, w)
while True:
ret, frame = cap.read()
dets = centerface(frame, threshold=0.5)
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
cv2.imshow('out', frame)
# Press Q on keyboard to stop recording
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
def video():
cap = cv2.VideoCapture('Manif.mp4')
ret, frame = cap.read()
h, w = frame.shape[:2]
centerface = CenterFace()
fps = 48 # 视频每秒24帧
size = (1920, 1080) # 需要转为视频的图片的尺寸
# 可以使用cv2.resize()进行修改
video = cv2.VideoWriter("result01.mp4",
cv2.VideoWriter_fourcc('m', 'p', '4', 'v'), fps,
size)
cnt_frame = 1
output_path = "./Output/Manifcsv.csv"
file = open(output_path, "w")
writer = csv.writer(file)
writer.writerow(["Frame", "Faces list"])
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
dets, lms = centerface(frame, h, w, threshold=0.35)
face_frame = []
for det in dets:
boxes, score = det[:4], det[4]
face_frame.append(boxes)
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
#for lm in lms:
# for i in range(0, 5):
# cv2.circle(frame, (int(lm[i * 2]), int(lm[i * 2 + 1])), 2, (0, 0, 255), -1)
writer.writerow([cnt_frame, face_frame])
cnt_frame += 1
video.write(frame)
cv2.imshow('out', frame)
# Press Q on keyboard to stop recording
if cv2.waitKey(1) & 0xFF == ord('q'):
break
#dataframe = pd.DataFrame(columns = cnt_frame, data = face_list)
#dataframe.to_csv('./Output/Manifcsv.csv',encoding = 'gbk')
cap.release()
def camera():
cap = cv2.VideoCapture("prj-python/demo.mp4")
ret, frame = cap.read()
h, w = frame.shape[:2]
centerface = CenterFace()
while True:
ret, frame = cap.read()
dets, lms = centerface(frame, h, w, threshold=0.35)
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
for lm in lms:
for i in range(0, 5):
cv2.circle(frame, (int(lm[i * 2]), int(lm[i * 2 + 1])), 2,
(0, 0, 255), -1)
cv2.imshow('out', frame)
# Press Q on keyboard to stop recording
if cv2.waitKey(50) & 0xFF == ord('q'):
break
cap.release()
def test_image():
frame = cv2.imread('000388.jpg')
h, w = frame.shape[:2]
landmarks = True
centerface = CenterFace(landmarks=landmarks)
if landmarks:
dets, lms = centerface(frame, h, w, threshold=0.35)
else:
dets = centerface(frame, threshold=0.35)
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
if landmarks:
for lm in lms:
for i in range(0, 5):
cv2.circle(frame, (int(lm[i * 2]), int(lm[i * 2 + 1])), 2,
(0, 0, 255), -1)
cv2.imshow('out', frame)
cv2.waitKey(0)
def test_image_tensorrt():
frame = cv2.imread('../prj-python/000388.jpg')
h, w = 480, 640 # must be 480* 640
landmarks = True
centerface = CenterFace()
if landmarks:
dets, lms = centerface(frame, h, w, threshold=0.35)
print("count = ", len(dets))
else:
dets = centerface(frame, threshold=0.35)
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
if landmarks:
for lm in lms:
for i in range(0, 5):
cv2.circle(frame, (int(lm[i * 2]), int(lm[i * 2 + 1])), 2,
(0, 0, 255), -1)
cv2.imshow('out', frame)
cv2.waitKey(0)
def camera():
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
h, w = frame.shape[:2]
centerface = CenterFace()
global org_image
while True:
ret, frame = cap.read()
dets, lms = centerface(frame, h, w, threshold=0.35)
org_image = frame.copy()
hsvframe = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)[:, :, 0]
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
for lm in lms:
face_points = []
for i in range(0, 5):
cv2.circle(frame, (int(lm[i * 2]), int(lm[i * 2 + 1])), 2,
(0, 0, 255), -1)
face_points.append([int(lm[i * 2]), int(lm[i * 2 + 1])])
# Extract color of region
avg_top = calculate_avg_color(hsvframe, face_points[0:2])
avg_bot = calculate_avg_color(hsvframe, face_points[2:5])
if avg_top and avg_bot:
diff = np.abs(avg_top - avg_bot) / max(avg_top, avg_bot) * 100
print(diff)
if diff <= 30:
cv2.putText(frame, "Vui long mang khau trang ",
(int(boxes[0]), int(boxes[1])),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 1)
cv2.imshow('out', frame)
# Press Q on keyboard to stop recording
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
def test_images(imglist, output_dir):
num_images = len(imglist)
for i in xrange(num_images):
# print('img{}:{}'.format(i,imglist[i]))
# im = cv2.imread(imglist[i])
frame = cv2.imread(imglist[i])
myboxes = []
assert frame is not None
h, w = frame.shape[:2]
centerface = CenterFace(h, w)
dets, lms = centerface(frame, threshold=0.35)
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
# print( (int(boxes[0]), int(boxes[1])), (int(boxes[2]), int(boxes[3])))
box = [
'Faces', 0,
(int(boxes[0]), int(boxes[1]), int(boxes[2]), int(boxes[3]))
]
myboxes.append(box)
# for lm in lms:
# cv2.circle(frame, (int(lm[0]), int(lm[1])), 2, (0, 0, 255), -1)
# cv2.circle(frame, (int(lm[2]), int(lm[3])), 2, (0, 0, 255), -1)
# cv2.circle(frame, (int(lm[4]), int(lm[5])), 2, (0, 0, 255), -1)
# cv2.circle(frame, (int(lm[6]), int(lm[7])), 2, (0, 0, 255), -1)
# cv2.circle(frame, (int(lm[8]), int(lm[9])), 2, (0, 0, 255), -1)
cv2.imshow('out', frame)
cv2.imwrite(
output_dir + os.path.basename(imglist[i]).split('.')[0] + '.jpg',
frame)
write_xml(imglist[i], w, h, myboxes, output_dir)
cv2.waitKey(500)
def camera():
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
h, w = frame.shape[:2]
centerface = CenterFace(h, w)
while True:
ret, frame = cap.read()
dets, lms = centerface(frame, threshold=0.5)
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])), (int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
for lm in lms:
cv2.circle(frame, (int(lm[0]), int(lm[1])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[2]), int(lm[3])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[4]), int(lm[5])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[6]), int(lm[7])), 2, (0, 0, 255), -1)
cv2.circle(frame, (int(lm[8]), int(lm[9])), 2, (0, 0, 255), -1)
print('rknn face num:', len(dets))
cv2.imshow('rknn', frame)
# Press Q on keyboard to stop recording
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
import cv2
import numpy as np
from PIL import ImageFile, Image
from centerface import CenterFace
net = CenterFace()
def ensure_cv(im):
if type(im) == str:
im = cv2.imread(im)
elif isinstance(im, ImageFile.ImageFile):
im = np.array(im)[:, :, :3]
im = im[:, :, ::-1]
return im
def detect(im):
global net
im = ensure_cv(im)
h, w = im.shape[:2]
dets, lms = net(im, h, w, threshold=0.35)
res = []
for (det, lm) in zip(dets, lms):
det = list(map(lambda x: round(x, 2), det))
lm = list(map(lambda x: round(x, 2), lm))
res.append([det, lm])
return res
def align(im, tsize=128, desiredLeftEye=0.35):
im = ensure_cv(im)
def __init__(self, event_interval=6):
self.controller = Controller(event_interval=event_interval)
self.detector = CenterFace()
self.trackers = []
import cv2
import scipy.io as sio
import os
import numpy as np
from tqdm import tqdm
import glob
from centerface import CenterFace
landmarks = True
centerface = CenterFace(landmarks=landmarks)
#
def test_image(img_path, save_path):
#
img_src = cv2.imread(img_path)
img_src = np.rot90(img_src, axes=(1, 0))
[img_h, img_w, img_c] = img_src.shape
dets, lms = centerface(img_src, img_h, img_w, threshold=0.35)
det = dets[0]
boxes, score = det[:4], det[4]
roi = [boxes[0], boxes[1], boxes[2], boxes[3]]
w = int(roi[2] - roi[0] + 1)
h = int(roi[3] - roi[1] + 1)
if w > h:
roi[1] = roi[1] - (w - h) // 2
roi[3] = roi[3] + (w - h - (w - h) // 2)
elif h > w:
roi[0] = roi[0] - (h - w) // 2
roi[2] = roi[2] + (h - w - (h - w) // 2)
import cv2
from centerface import CenterFace
image = cv2.imread("images/demo.jpg")
h, w = image.shape[:2]
centerface = CenterFace()
dets, lms = centerface(image, h, w, threshold=0.35)
for det in dets:
boxes, score = det[:4], det[4]
cv2.rectangle(
image,
(int(boxes[0]), int(boxes[1])),
(int(boxes[2]), int(boxes[3])),
(2, 255, 0),
1,
)
for lm in lms:
for i in range(0, 5):
cv2.circle(image, (int(lm[i * 2]), int(lm[i * 2 + 1])), 2, (0, 0, 255),
-1)
cv2.imshow("", image)
cv2.waitKey(0)
import cv2
import torch
from centerface import CenterFace
save_folder = "./prediction/"
dataset_folder = "../widerface_val/images/"
if __name__ == '__main__':
torch.set_grad_enabled(False)
testset_folder = dataset_folder
testset_list = dataset_folder[:-7] + "wider_val.txt"
with open(testset_list, 'r') as fr:
test_dataset = fr.read().split()
num_images = len(test_dataset)
centerface = CenterFace(landmarks=True)
for i, img_name in enumerate(test_dataset):
############################# Add face detection here#######################################
image_path = testset_folder + img_name
img = cv2.imread(image_path, cv2.IMREAD_COLOR)
h, w = img.shape[:2]
dets, lms = centerface(img, h, w, threshold=0.35)
############################################################################################
save_name = save_folder + img_name[:-4] + ".txt"
dirname = os.path.dirname(save_name)
if not os.path.isdir(dirname):
os.makedirs(dirname)
with open(save_name, "w") as fd:
bboxs = dets
file_name = os.path.basename(save_name)[:-4] + "\n"