Upload RetinaFace.py

RetinaFace.py

@@ -0,0 +1,216 @@
+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
+
+from retinaface.model import retinaface_model
+from retinaface.commons import preprocess, 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