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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 | |