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Update RetinaFace.py
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import warnings
warnings.filterwarnings("ignore")
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
#---------------------------
import numpy as np
import tensorflow as tf
import cv2
import retinaface_model
import preprocess
import postprocess
#---------------------------
import tensorflow as tf
tf_version = int(tf.__version__.split(".")[0])
if tf_version == 2:
import logging
tf.get_logger().setLevel(logging.ERROR)
#---------------------------
def build_model():
global model #singleton design pattern
if not "model" in globals():
model = tf.function(
retinaface_model.build_model(),
input_signature=(tf.TensorSpec(shape=[None, None, None, 3], dtype=np.float32),)
)
return model
def get_image(img_path):
if type(img_path) == str: # Load from file path
if not os.path.isfile(img_path):
raise ValueError("Input image file path (", img_path, ") does not exist.")
img = cv2.imread(img_path)
elif isinstance(img_path, np.ndarray): # Use given NumPy array
img = img_path.copy()
else:
raise ValueError("Invalid image input. Only file paths or a NumPy array accepted.")
# Validate image shape
if len(img.shape) != 3 or np.prod(img.shape) == 0:
raise ValueError("Input image needs to have 3 channels at must not be empty.")
return img
def detect_faces(img_path, threshold=0.9, model = None, allow_upscaling = True):
"""
TODO: add function doc here
"""
img = get_image(img_path)
#---------------------------
if model is None:
model = build_model()
#---------------------------
nms_threshold = 0.4; decay4=0.5
_feat_stride_fpn = [32, 16, 8]
_anchors_fpn = {
'stride32': np.array([[-248., -248., 263., 263.], [-120., -120., 135., 135.]], dtype=np.float32),
'stride16': np.array([[-56., -56., 71., 71.], [-24., -24., 39., 39.]], dtype=np.float32),
'stride8': np.array([[-8., -8., 23., 23.], [ 0., 0., 15., 15.]], dtype=np.float32)
}
_num_anchors = {'stride32': 2, 'stride16': 2, 'stride8': 2}
#---------------------------
proposals_list = []
scores_list = []
landmarks_list = []
im_tensor, im_info, im_scale = preprocess.preprocess_image(img, allow_upscaling)
net_out = model(im_tensor)
net_out = [elt.numpy() for elt in net_out]
sym_idx = 0
for _idx, s in enumerate(_feat_stride_fpn):
_key = 'stride%s'%s
scores = net_out[sym_idx]
scores = scores[:, :, :, _num_anchors['stride%s'%s]:]
bbox_deltas = net_out[sym_idx + 1]
height, width = bbox_deltas.shape[1], bbox_deltas.shape[2]
A = _num_anchors['stride%s'%s]
K = height * width
anchors_fpn = _anchors_fpn['stride%s'%s]
anchors = postprocess.anchors_plane(height, width, s, anchors_fpn)
anchors = anchors.reshape((K * A, 4))
scores = scores.reshape((-1, 1))
bbox_stds = [1.0, 1.0, 1.0, 1.0]
bbox_deltas = bbox_deltas
bbox_pred_len = bbox_deltas.shape[3]//A
bbox_deltas = bbox_deltas.reshape((-1, bbox_pred_len))
bbox_deltas[:, 0::4] = bbox_deltas[:,0::4] * bbox_stds[0]
bbox_deltas[:, 1::4] = bbox_deltas[:,1::4] * bbox_stds[1]
bbox_deltas[:, 2::4] = bbox_deltas[:,2::4] * bbox_stds[2]
bbox_deltas[:, 3::4] = bbox_deltas[:,3::4] * bbox_stds[3]
proposals = postprocess.bbox_pred(anchors, bbox_deltas)
proposals = postprocess.clip_boxes(proposals, im_info[:2])
if s==4 and decay4<1.0:
scores *= decay4
scores_ravel = scores.ravel()
order = np.where(scores_ravel>=threshold)[0]
proposals = proposals[order, :]
scores = scores[order]
proposals[:, 0:4] /= im_scale
proposals_list.append(proposals)
scores_list.append(scores)
landmark_deltas = net_out[sym_idx + 2]
landmark_pred_len = landmark_deltas.shape[3]//A
landmark_deltas = landmark_deltas.reshape((-1, 5, landmark_pred_len//5))
landmarks = postprocess.landmark_pred(anchors, landmark_deltas)
landmarks = landmarks[order, :]
landmarks[:, :, 0:2] /= im_scale
landmarks_list.append(landmarks)
sym_idx += 3
proposals = np.vstack(proposals_list)
if proposals.shape[0]==0:
landmarks = np.zeros( (0,5,2) )
return np.zeros( (0,5) ), landmarks
scores = np.vstack(scores_list)
scores_ravel = scores.ravel()
order = scores_ravel.argsort()[::-1]
proposals = proposals[order, :]
scores = scores[order]
landmarks = np.vstack(landmarks_list)
landmarks = landmarks[order].astype(np.float32, copy=False)
pre_det = np.hstack((proposals[:,0:4], scores)).astype(np.float32, copy=False)
#nms = cpu_nms_wrapper(nms_threshold)
#keep = nms(pre_det)
keep = postprocess.cpu_nms(pre_det, nms_threshold)
det = np.hstack( (pre_det, proposals[:,4:]) )
det = det[keep, :]
landmarks = landmarks[keep]
resp = {}
for idx, face in enumerate(det):
label = 'face_'+str(idx+1)
resp[label] = {}
resp[label]["score"] = face[4]
resp[label]["facial_area"] = list(face[0:4].astype(int))
resp[label]["landmarks"] = {}
resp[label]["landmarks"]["right_eye"] = list(landmarks[idx][0])
resp[label]["landmarks"]["left_eye"] = list(landmarks[idx][1])
resp[label]["landmarks"]["nose"] = list(landmarks[idx][2])
resp[label]["landmarks"]["mouth_right"] = list(landmarks[idx][3])
resp[label]["landmarks"]["mouth_left"] = list(landmarks[idx][4])
return resp
def extract_faces(img_path, threshold=0.9, model = None, align = True, allow_upscaling = True):
resp = []
#---------------------------
img = get_image(img_path)
#---------------------------
obj = detect_faces(img_path = img, threshold = threshold, model = model, allow_upscaling = allow_upscaling)
if type(obj) == dict:
for key in obj:
identity = obj[key]
facial_area = identity["facial_area"]
facial_img = img[facial_area[1]: facial_area[3], facial_area[0]: facial_area[2]]
if align == True:
landmarks = identity["landmarks"]
left_eye = landmarks["left_eye"]
right_eye = landmarks["right_eye"]
nose = landmarks["nose"]
mouth_right = landmarks["mouth_right"]
mouth_left = landmarks["mouth_left"]
facial_img = postprocess.alignment_procedure(facial_img, right_eye, left_eye, nose)
resp.append(facial_img[:, :, ::-1])
#elif type(obj) == tuple:
return resp