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centerface backend added
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Sefik Ilkin Serengil
parent
10b2e18dce
commit
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217
deepface/detectors/CenterFace.py
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217
deepface/detectors/CenterFace.py
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# built-in dependencies
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import os
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from typing import List
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# 3rd party dependencies
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import numpy as np
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import cv2
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import gdown
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# project dependencies
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from deepface.commons import folder_utils
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from deepface.models.Detector import Detector, FacialAreaRegion
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from deepface.commons import logger as log
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logger = log.get_singletonish_logger()
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# pylint: disable=c-extension-no-member
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WEIGHTS_URL = "https://github.com/Star-Clouds/CenterFace/raw/master/models/onnx/centerface.onnx"
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class CenterFaceClient(Detector):
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def __init__(self):
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# BUG: model must be flushed for each call
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# self.model = self.build_model()
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pass
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def build_model(self):
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"""
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Download pre-trained weights of CenterFace model if necessary and load built model
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"""
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weights_path = f"{folder_utils.get_deepface_home()}/.deepface/weights/centerface.onnx"
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if not os.path.isfile(weights_path):
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logger.info(f"Downloading CenterFace weights from {WEIGHTS_URL} to {weights_path}...")
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try:
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gdown.download(WEIGHTS_URL, weights_path, quiet=False)
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except Exception as err:
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raise ValueError(
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f"Exception while downloading CenterFace weights from {WEIGHTS_URL}."
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f"You may consider to download it to {weights_path} manually."
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) from err
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logger.info(f"CenterFace model is just downloaded to {os.path.basename(weights_path)}")
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return CenterFace(weight_path=weights_path)
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def detect_faces(self, img: np.ndarray) -> List["FacialAreaRegion"]:
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"""
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Detect and align face with CenterFace
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Args:
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img (np.ndarray): pre-loaded image as numpy array
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Returns:
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results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
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"""
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resp = []
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threshold = float(os.getenv("CENTERFACE_THRESHOLD", "0.80"))
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# BUG: model causes problematic results from 2nd call if it is not flushed
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# detections, landmarks = self.model.forward(
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# img, img.shape[0], img.shape[1], threshold=threshold
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# )
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detections, landmarks = self.build_model().forward(
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img, img.shape[0], img.shape[1], threshold=threshold
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)
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for i, detection in enumerate(detections):
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boxes, confidence = detection[:4], detection[4]
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x = boxes[0]
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y = boxes[1]
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w = boxes[2] - x
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h = boxes[3] - y
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landmark = landmarks[i]
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right_eye = (int(landmark[0]), int(landmark[1]))
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left_eye = (int(landmark[2]), int(landmark[3]))
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# nose = (int(landmark[4]), int(landmark [5]))
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# mouth_right = (int(landmark[6]), int(landmark [7]))
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# mouth_left = (int(landmark[8]), int(landmark [9]))
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facial_area = FacialAreaRegion(
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x=x,
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y=y,
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w=w,
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h=h,
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left_eye=left_eye,
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right_eye=right_eye,
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confidence=min(max(0, float(confidence)), 1.0),
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)
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resp.append(facial_area)
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return resp
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class CenterFace:
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"""
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This class is heavily inspired from
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github.com/Star-Clouds/CenterFace/blob/master/prj-python/centerface.py
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"""
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def __init__(self, weight_path: str):
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self.net = cv2.dnn.readNetFromONNX(weight_path)
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self.img_h_new, self.img_w_new, self.scale_h, self.scale_w = 0, 0, 0, 0
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def forward(self, img, height, width, threshold=0.5):
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self.img_h_new, self.img_w_new, self.scale_h, self.scale_w = self.transform(height, width)
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return self.inference_opencv(img, threshold)
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def inference_opencv(self, img, threshold):
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blob = cv2.dnn.blobFromImage(
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img,
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scalefactor=1.0,
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size=(self.img_w_new, self.img_h_new),
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mean=(0, 0, 0),
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swapRB=True,
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crop=False,
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)
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self.net.setInput(blob)
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heatmap, scale, offset, lms = self.net.forward(["537", "538", "539", "540"])
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return self.postprocess(heatmap, lms, offset, scale, threshold)
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def transform(self, h, w):
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img_h_new, img_w_new = int(np.ceil(h / 32) * 32), int(np.ceil(w / 32) * 32)
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scale_h, scale_w = img_h_new / h, img_w_new / w
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return img_h_new, img_w_new, scale_h, scale_w
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def postprocess(self, heatmap, lms, offset, scale, threshold):
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dets, lms = self.decode(
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heatmap, scale, offset, lms, (self.img_h_new, self.img_w_new), threshold=threshold
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)
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if len(dets) > 0:
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dets[:, 0:4:2], dets[:, 1:4:2] = (
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dets[:, 0:4:2] / self.scale_w,
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dets[:, 1:4:2] / self.scale_h,
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)
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lms[:, 0:10:2], lms[:, 1:10:2] = (
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lms[:, 0:10:2] / self.scale_w,
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lms[:, 1:10:2] / self.scale_h,
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)
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else:
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dets = np.empty(shape=[0, 5], dtype=np.float32)
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lms = np.empty(shape=[0, 10], dtype=np.float32)
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return dets, lms
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def decode(self, heatmap, scale, offset, landmark, size, threshold=0.1):
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heatmap = np.squeeze(heatmap)
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scale0, scale1 = scale[0, 0, :, :], scale[0, 1, :, :]
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offset0, offset1 = offset[0, 0, :, :], offset[0, 1, :, :]
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c0, c1 = np.where(heatmap > threshold)
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boxes, lms = [], []
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if len(c0) > 0:
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# pylint:disable=consider-using-enumerate
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for i in range(len(c0)):
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s0, s1 = np.exp(scale0[c0[i], c1[i]]) * 4, np.exp(scale1[c0[i], c1[i]]) * 4
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o0, o1 = offset0[c0[i], c1[i]], offset1[c0[i], c1[i]]
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s = heatmap[c0[i], c1[i]]
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x1, y1 = max(0, (c1[i] + o1 + 0.5) * 4 - s1 / 2), max(
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0, (c0[i] + o0 + 0.5) * 4 - s0 / 2
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)
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x1, y1 = min(x1, size[1]), min(y1, size[0])
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boxes.append([x1, y1, min(x1 + s1, size[1]), min(y1 + s0, size[0]), s])
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lm = []
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for j in range(5):
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lm.append(landmark[0, j * 2 + 1, c0[i], c1[i]] * s1 + x1)
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lm.append(landmark[0, j * 2, c0[i], c1[i]] * s0 + y1)
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lms.append(lm)
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boxes = np.asarray(boxes, dtype=np.float32)
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keep = self.nms(boxes[:, :4], boxes[:, 4], 0.3)
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boxes = boxes[keep, :]
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lms = np.asarray(lms, dtype=np.float32)
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lms = lms[keep, :]
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return boxes, lms
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def nms(self, boxes, scores, nms_thresh):
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x1 = boxes[:, 0]
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y1 = boxes[:, 1]
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x2 = boxes[:, 2]
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y2 = boxes[:, 3]
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = np.argsort(scores)[::-1]
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num_detections = boxes.shape[0]
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suppressed = np.zeros((num_detections,), dtype=bool)
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keep = []
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for _i in range(num_detections):
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i = order[_i]
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if suppressed[i]:
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continue
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keep.append(i)
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ix1 = x1[i]
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iy1 = y1[i]
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ix2 = x2[i]
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iy2 = y2[i]
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iarea = areas[i]
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for _j in range(_i + 1, num_detections):
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j = order[_j]
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if suppressed[j]:
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continue
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xx1 = max(ix1, x1[j])
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yy1 = max(iy1, y1[j])
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xx2 = min(ix2, x2[j])
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yy2 = min(iy2, y2[j])
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w = max(0, xx2 - xx1 + 1)
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h = max(0, yy2 - yy1 + 1)
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inter = w * h
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ovr = inter / (iarea + areas[j] - inter)
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if ovr >= nms_thresh:
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suppressed[j] = True
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return keep
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@ -12,6 +12,7 @@ from deepface.detectors import (
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Ssd,
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Yolo,
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YuNet,
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CenterFace,
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)
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from deepface.commons import logger as log
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@ -38,6 +39,7 @@ def build_model(detector_backend: str) -> Any:
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"yolov8": Yolo.YoloClient,
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"yunet": YuNet.YuNetClient,
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"fastmtcnn": FastMtCnn.FastMtCnnClient,
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"centerface": CenterFace.CenterFaceClient,
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}
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if not "face_detector_obj" in globals():
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@ -93,7 +95,7 @@ def detect_faces(
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expand_percentage = 0
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# find facial areas of given image
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facial_areas = face_detector.detect_faces(img=img)
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facial_areas = face_detector.detect_faces(img)
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results = []
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for facial_area in facial_areas:
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