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# built-in dependencies
import os
from typing import List
# 3rd party dependencies
import numpy as np
import cv2
import gdown
# project dependencies
from deepface.commons import folder_utils
from deepface.models.Detector import Detector, FacialAreaRegion
from deepface.commons import logger as log
logger = log.get_singletonish_logger()
# pylint: disable=c-extension-no-member
WEIGHTS_URL = "https://github.com/Star-Clouds/CenterFace/raw/master/models/onnx/centerface.onnx"
class CenterFaceClient(Detector):
def __init__(self):
# BUG: model must be flushed for each call
# self.model = self.build_model()
pass
def build_model(self):
"""
Download pre-trained weights of CenterFace model if necessary and load built model
"""
weights_path = f"{folder_utils.get_deepface_home()}/.deepface/weights/centerface.onnx"
if not os.path.isfile(weights_path):
logger.info(f"Downloading CenterFace weights from {WEIGHTS_URL} to {weights_path}...")
try:
gdown.download(WEIGHTS_URL, weights_path, quiet=False)
except Exception as err:
raise ValueError(
f"Exception while downloading CenterFace weights from {WEIGHTS_URL}."
f"You may consider to download it to {weights_path} manually."
) from err
logger.info(f"CenterFace model is just downloaded to {os.path.basename(weights_path)}")
return CenterFace(weight_path=weights_path)
def detect_faces(self, img: np.ndarray) -> List["FacialAreaRegion"]:
"""
Detect and align face with CenterFace
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
resp = []
threshold = float(os.getenv("CENTERFACE_THRESHOLD", "0.80"))
# BUG: model causes problematic results from 2nd call if it is not flushed
# detections, landmarks = self.model.forward(
# img, img.shape[0], img.shape[1], threshold=threshold
# )
detections, landmarks = self.build_model().forward(
img, img.shape[0], img.shape[1], threshold=threshold
)
for i, detection in enumerate(detections):
boxes, confidence = detection[:4], detection[4]
x = boxes[0]
y = boxes[1]
w = boxes[2] - x
h = boxes[3] - y
landmark = landmarks[i]
right_eye = (int(landmark[0]), int(landmark[1]))
left_eye = (int(landmark[2]), int(landmark[3]))
# nose = (int(landmark[4]), int(landmark [5]))
# mouth_right = (int(landmark[6]), int(landmark [7]))
# mouth_left = (int(landmark[8]), int(landmark [9]))
facial_area = FacialAreaRegion(
x=int(x),
y=int(y),
w=int(w),
h=int(h),
left_eye=left_eye,
right_eye=right_eye,
confidence=min(max(0, float(confidence)), 1.0),
)
resp.append(facial_area)
return resp
class CenterFace:
"""
This class is heavily inspired from
github.com/Star-Clouds/CenterFace/blob/master/prj-python/centerface.py
"""
def __init__(self, weight_path: str):
self.net = cv2.dnn.readNetFromONNX(weight_path)
self.img_h_new, self.img_w_new, self.scale_h, self.scale_w = 0, 0, 0, 0
def forward(self, img, height, width, threshold=0.5):
self.img_h_new, self.img_w_new, self.scale_h, self.scale_w = self.transform(height, width)
return self.inference_opencv(img, threshold)
def inference_opencv(self, img, threshold):
blob = cv2.dnn.blobFromImage(
img,
scalefactor=1.0,
size=(self.img_w_new, self.img_h_new),
mean=(0, 0, 0),
swapRB=True,
crop=False,
)
self.net.setInput(blob)
heatmap, scale, offset, lms = self.net.forward(["537", "538", "539", "540"])
return self.postprocess(heatmap, lms, offset, scale, threshold)
def transform(self, h, w):
img_h_new, img_w_new = int(np.ceil(h / 32) * 32), int(np.ceil(w / 32) * 32)
scale_h, scale_w = img_h_new / h, img_w_new / w
return img_h_new, img_w_new, scale_h, scale_w
def postprocess(self, heatmap, lms, offset, scale, threshold):
dets, lms = self.decode(
heatmap, scale, offset, lms, (self.img_h_new, self.img_w_new), threshold=threshold
)
if len(dets) > 0:
dets[:, 0:4:2], dets[:, 1:4:2] = (
dets[:, 0:4:2] / self.scale_w,
dets[:, 1:4:2] / self.scale_h,
)
lms[:, 0:10:2], lms[:, 1:10:2] = (
lms[:, 0:10:2] / self.scale_w,
lms[:, 1:10:2] / self.scale_h,
)
else:
dets = np.empty(shape=[0, 5], dtype=np.float32)
lms = np.empty(shape=[0, 10], dtype=np.float32)
return dets, lms
def decode(self, heatmap, scale, offset, landmark, size, threshold=0.1):
heatmap = np.squeeze(heatmap)
scale0, scale1 = scale[0, 0, :, :], scale[0, 1, :, :]
offset0, offset1 = offset[0, 0, :, :], offset[0, 1, :, :]
c0, c1 = np.where(heatmap > threshold)
boxes, lms = [], []
if len(c0) > 0:
# pylint:disable=consider-using-enumerate
for i in range(len(c0)):
s0, s1 = np.exp(scale0[c0[i], c1[i]]) * 4, np.exp(scale1[c0[i], c1[i]]) * 4
o0, o1 = offset0[c0[i], c1[i]], offset1[c0[i], c1[i]]
s = heatmap[c0[i], c1[i]]
x1, y1 = max(0, (c1[i] + o1 + 0.5) * 4 - s1 / 2), max(
0, (c0[i] + o0 + 0.5) * 4 - s0 / 2
)
x1, y1 = min(x1, size[1]), min(y1, size[0])
boxes.append([x1, y1, min(x1 + s1, size[1]), min(y1 + s0, size[0]), s])
lm = []
for j in range(5):
lm.append(landmark[0, j * 2 + 1, c0[i], c1[i]] * s1 + x1)
lm.append(landmark[0, j * 2, c0[i], c1[i]] * s0 + y1)
lms.append(lm)
boxes = np.asarray(boxes, dtype=np.float32)
keep = self.nms(boxes[:, :4], boxes[:, 4], 0.3)
boxes = boxes[keep, :]
lms = np.asarray(lms, dtype=np.float32)
lms = lms[keep, :]
return boxes, lms
def nms(self, boxes, scores, nms_thresh):
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = np.argsort(scores)[::-1]
num_detections = boxes.shape[0]
suppressed = np.zeros((num_detections,), dtype=bool)
keep = []
for _i in range(num_detections):
i = order[_i]
if suppressed[i]:
continue
keep.append(i)
ix1 = x1[i]
iy1 = y1[i]
ix2 = x2[i]
iy2 = y2[i]
iarea = areas[i]
for _j in range(_i + 1, num_detections):
j = order[_j]
if suppressed[j]:
continue
xx1 = max(ix1, x1[j])
yy1 = max(iy1, y1[j])
xx2 = min(ix2, x2[j])
yy2 = min(iy2, y2[j])
w = max(0, xx2 - xx1 + 1)
h = max(0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (iarea + areas[j] - inter)
if ovr >= nms_thresh:
suppressed[j] = True
return keep
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