def get_central_face_attributes(full_path):
img = cv.imread(full_path, cv.IMREAD_COLOR)
bboxes, landmarks = detect_faces(img)
if len(landmarks) > 0:
i = select_significant_face(bboxes)
return [bboxes[i]], [landmarks[i]]
return None, None
def get_face_attributes(full_path):
try:
img = Image.open(full_path).convert('RGB')
bounding_boxes, landmarks = detect_faces(img)
if len(landmarks) > 0:
landmarks = [int(round(x)) for x in landmarks[0]]
return True, landmarks
except KeyboardInterrupt:
raise
except:
pass
return False, None
def get_face_attributes(full_path):
try:
img = cv.imread(full_path, cv.IMREAD_COLOR)
bounding_boxes, landmarks = detect_faces(img)
if len(landmarks) > 0:
landmarks = [int(round(x)) for x in landmarks[0]]
return True, landmarks
except KeyboardInterrupt:
raise
except Exception as err:
print(err)
return False, None
def get_central_face_attributes(full_path):
try:
img = Image.open(full_path).convert('RGB')
bounding_boxes, landmarks = detect_faces(img)
if len(landmarks) > 0:
i = select_significant_face(bounding_boxes)
return True, [bounding_boxes[i]], [landmarks[i]]
except KeyboardInterrupt:
raise
except ValueError:
pass
except IOError:
pass
return False, None, None
def get_central_face_attributes(full_path):
from retinaface.detector import detect_faces
from utils import select_central_face
try:
img = Image.open(full_path).convert('RGB')
bounding_boxes, landmarks = detect_faces(img)
if len(landmarks) > 0:
i = select_central_face(img.size, bounding_boxes)
return True, [bounding_boxes[i]], [landmarks[i]]
except KeyboardInterrupt:
raise
except ValueError:
pass
except IOError:
pass
return False, None, None
def detect_face(data):
from retinaface.detector import detect_faces
from utils import select_significant_face, align_face
src_path = data['src_path']
dst_path = data['dst_path']
img_raw = cv.imread(src_path)
if img_raw is not None:
img = resize(img_raw)
bboxes, landmarks = detect_faces(img, top_k=5, keep_top_k=5)
if len(bboxes) > 0:
i = select_significant_face(bboxes)
bbox, landms = bboxes[i], landmarks[i]
img = align_face(img, [landms])
dirname = os.path.dirname(dst_path)
os.makedirs(dirname, exist_ok=True)
cv.imwrite(dst_path, img)
return True
def download(tokens, idx, num):
url = tokens[0].replace('"', '').strip()
left = float(tokens[1].strip())
right = float(tokens[2].strip())
top = float(tokens[3].strip())
bottom = float(tokens[4].strip())
filename = url[url.rfind("/") + 1:].strip()
fullname = os.path.join(download_folder, filename)
# if not os.path.isfile(fullname):
# process = Popen(["wget", '-N', url, "-P", download_folder], stdout=PIPE)
# (output, err) = process.communicate()
# exit_code = process.wait()
filename = '{}_{}.jpg'.format(idx, num)
filename = os.path.join(image_folder, filename)
# print(filename)
if os.path.isfile(filename) and os.path.getsize(filename) > 1000:
img = cv.imread(filename)
if img is not None:
h, w = img.shape[:2]
if h == 224 and w == 224:
return filename
if os.path.isfile(fullname) and os.path.getsize(fullname) > 1000:
img = cv.imread(fullname)
if img is not None:
height, width = img.shape[:2]
left, right = int(round(left * width)), int(round(right * width))
top, bottom = int(round(top * height)), int(round(bottom * height))
img = img[top:bottom, left:right, :]
_, landmarks = detect_faces(img, top_k=1, keep_top_k=1)
if len(landmarks) < 1:
return None
img = align_face(img, landmarks)
cv.imwrite(filename, img)
return filename
return None
import time
import cv2 as cv
from retinaface.detector import detect_faces
if __name__ == '__main__':
# testing begin
image_path = "images/test.jpg"
img_raw = cv.imread(image_path, cv.IMREAD_COLOR)
start = time.time()
bboxes, landmarks = detect_faces(img_raw)
end = time.time()
elapsed = end - start
print('avg time: {:5f} ms'.format(elapsed * 1000))
num_faces = bboxes.shape[0]
# show image
for i in range(num_faces):
b = bboxes[i]
text = "{:.4f}".format(b[4])
b = list(map(int, b))
cv.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
cx = b[0]
cy = b[1] + 12
cv.putText(img_raw, text, (cx, cy), cv.FONT_HERSHEY_DUPLEX, 0.5,
(255, 255, 255))
cv.putText(img_raw, text, (cx, cy),
cv.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
# landms
landms = landmarks[i]
cv.circle(img_raw, (landms[0], landms[5]), 1, (0, 0, 255), 4)
cv.circle(img_raw, (landms[1], landms[6]), 1, (0, 255, 255), 4)
cv.circle(img_raw, (landms[2], landms[7]), 1, (255, 0, 255), 4)
cv.circle(img_raw, (landms[3], landms[8]), 1, (0, 255, 0), 4)
cv.circle(img_raw, (landms[4], landms[9]), 1, (255, 0, 0), 4)
# save image
cv.imwrite('images/result.jpg', img_raw)
cv.imshow('image', img_raw)
cv.waitKey(0)
if __name__ == "__main__":
full_path = 'test/Jason Behr_27968.JPG'
img = Image.open(full_path).convert('RGB')
bboxes, landmarks = mtcnn.detect_faces(img)
print(bboxes)
print(landmarks)
show_bboxes(full_path, bboxes, landmarks)
bboxes, landmarks = retinaface.detect_faces(img)
print(bboxes)
print(landmarks)
show_bboxes(full_path, bboxes, landmarks)
def get_all_face_attributes(full_path):
from retinaface.detector import detect_faces
img = cv.imread(full_path, cv.IMREAD_COLOR)
bounding_boxes, landmarks = detect_faces(img)
return bounding_boxes, landmarks
def get_all_face_attributes(full_path):
img = Image.open(full_path).convert('RGB')
bounding_boxes, landmarks = detect_faces(img)
return bounding_boxes, landmarks
cv.circle(img_raw, (landms[4], landms[9]), 1, (255, 0, 0), 4)
if __name__ == '__main__':
folder = 'data/Aaron Eckhart'
files = [f for f in os.listdir(folder) if f.endswith('.jpg')]
np.random.shuffle(files)
for file in files:
filename = os.path.join(folder, file)
# print(filename)
img = cv.imread(filename)
if img is not None:
img_raw = resize(img)
bboxes, landmarks = detect_faces(img_raw, top_k=5, keep_top_k=5)
i = select_significant_face(img.shape[:2], bboxes)
bbox, landms = bboxes[i], landmarks[i]
img = img_raw.copy()
draw_bbox(img, bbox, landms)
cv.namedWindow(file) # Create a named window
cv.moveWindow(file, 150, 30)
cv.imshow(file, img)
img = align_face(img_raw, [landms])
cv.imshow('aligned', img)
cv.waitKey(0)
print('Completed!')
