Source code for bob.ip.facedetect.mtcnn

# Example taken from:

import logging

import pkg_resources
from import to_matplotlib
from bob.ip.color import gray_to_rgb

logger = logging.getLogger(__name__)

[docs]class MTCNN: """MTCNN v1 wrapper for Tensorflow 2. See for more details on MTCNN. Attributes ---------- factor : float Factor is a trade-off between performance and speed. min_size : int Minimum face size to be detected. thresholds : list Thresholds are a trade-off between false positives and missed detections. """ def __init__(self, min_size=40, factor=0.709, thresholds=(0.6, 0.7, 0.7), **kwargs): super().__init__(**kwargs) self.min_size = min_size self.factor = factor self.thresholds = thresholds self._graph_path = pkg_resources.resource_filename(__name__, "data/mtcnn.pb") # Avoids loading graph at initilization self._fun = None @property def mtcnn_fun(self): import tensorflow as tf if self._fun is None: # wrap graph function as a callable function self._fun = tf.compat.v1.wrap_function( self._graph_fn, [ tf.TensorSpec(shape=[None, None, 3], dtype=tf.float32), ], ) return self._fun def _graph_fn(self, img): import tensorflow as tf with open(self._graph_path, "rb") as f: graph_def = tf.compat.v1.GraphDef.FromString( prob, landmarks, box = tf.compat.v1.import_graph_def( graph_def, input_map={ "input:0": img, "min_size:0": tf.convert_to_tensor(self.min_size, dtype=float), "thresholds:0": tf.convert_to_tensor(self.thresholds, dtype=float), "factor:0": tf.convert_to_tensor(self.factor, dtype=float), }, return_elements=["prob:0", "landmarks:0", "box:0"], name="", ) return box, prob, landmarks
[docs] def detect(self, image): """Detects all faces in the image. Parameters ---------- image : numpy.ndarray An RGB image in Bob format. Returns ------- tuple A tuple of boxes, probabilities, and landmarks. """ if len(image.shape) == 2: image = gray_to_rgb(image) # Assuming image is Bob format and RGB assert image.shape[0] == 3, image.shape # MTCNN expects BGR opencv format image = to_matplotlib(image) image = image[..., ::-1] boxes, probs, landmarks = self.mtcnn_fun(image) return boxes, probs, landmarks
[docs] def annotations(self, image): """Detects all faces in the image and returns annotations in bob format. Parameters ---------- image : numpy.ndarray An RGB image in Bob format. Returns ------- list A list of annotations. Annotations are dictionaries that contain the following keys: ``topleft``, ``bottomright``, ``reye``, ``leye``, ``nose``, ``mouthright``, ``mouthleft``, and ``quality``. """ boxes, probs, landmarks = self.detect(image) # Iterate over all the detected faces annots = [] for box, prob, lm in zip(boxes, probs, landmarks): topleft = float(box[0]), float(box[1]) bottomright = float(box[2]), float(box[3]) right_eye = float(lm[0]), float(lm[5]) left_eye = float(lm[1]), float(lm[6]) nose = float(lm[2]), float(lm[7]) mouthright = float(lm[3]), float(lm[8]) mouthleft = float(lm[4]), float(lm[9]) annots.append( { "topleft": topleft, "bottomright": bottomright, "reye": right_eye, "leye": left_eye, "nose": nose, "mouthright": mouthright, "mouthleft": mouthleft, "quality": float(prob), } ) return annots
def __call__(self, img): """Wrapper for the annotations method.""" return self.annotations(img)