Mtcnn face detection

demo.py

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        import cv2
        
from detection.mtcnn import MTCNN
        

        

        
#  Detect the face in the picture 
        
def test_image(imgpath):
        
    mtcnn = MTCNN('./mtcnn.pb')
        
    img = cv2.imread(imgpath)
        

        
    bbox, landmarks, scores = mtcnn.detect_faces(img)
        

        
    print('total box:', len(bbox))
        
    for box, pts in zip(bbox, landmarks):
        
        box = box.astype('int32')
        
        img = cv2.rectangle(img, (box[1], box[0]), (box[3], box[2]), (255, 0, 0), 3)
        

        
        pts = pts.astype('int32')
        
        for i in range(5):
        
            img = cv2.circle(img, (pts[i + 5], pts[i]), 1, (0, 255, 0), 2)
        
    cv2.imshow('image', img)
        
    cv2.waitKey()
        

        

        
#  Detect faces in the video 
        
def test_camera():
        
    mtcnn = MTCNN('./mtcnn.pb')
        
    cap = cv2.VideoCapture('rtsp://admin:[email protected]/Streaming/Channels/1')
        
    while True:
        
        ret, img = cap.read()
        
        if not ret:
        
            break
        
        bbox, landmarks, scores = mtcnn.detect_faces(img)
        
        print('total box:', len(bbox), scores)
        
        for box, pts in zip(bbox, landmarks):
        
            box = box.astype('int32')
        
            img = cv2.rectangle(img, (box[1], box[0]), (box[3], box[2]), (255, 0, 0), 3)
        
            pts = pts.astype('int32')
        
            for i in range(5):
        
                img = cv2.circle(img, (pts[i], pts[i + 5]), 1, (0, 255, 0), 2)
        
        cv2.imshow('img', img)
        
        cv2.waitKey(1)
        

        

        
if __name__ == '__main__':
        
    # test_image()
        
    test_camera()
       
       

        
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mtcnn.py

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        import tensorflow as tf
        
from detection.align_trans import get_reference_facial_points, warp_and_crop_face
        
import numpy as np
        
import cv2
        
import detection.face_preprocess as face_preprocess
        

        

        
class MTCNN:
        

        
    def __init__(self, model_path, min_size=40, factor=0.709, thresholds=[0.7, 0.8, 0.8]):
        
        self.min_size = min_size
        
        self.factor = factor
        
        self.thresholds = thresholds
        

        
        graph = tf.Graph()
        
        with graph.as_default():
        
            with open(model_path, 'rb') as f:
        
                graph_def = tf.GraphDef.FromString(f.read())
        
                tf.import_graph_def(graph_def, name='')
        
        self.graph = graph
        
        config = tf.ConfigProto(
        
            allow_soft_placement=True,
        
            intra_op_parallelism_threads=4,
        
            inter_op_parallelism_threads=4)
        
        config.gpu_options.allow_growth = True
        
        self.sess = tf.Session(graph=graph, config=config)
        
        self.refrence = get_reference_facial_points(default_square=True)
        

        
    #  Face detection 
        
    def detect_faces(self, img):
        
        feeds = {
        
            self.graph.get_operation_by_name('input').outputs[0]: img,
        
            self.graph.get_operation_by_name('min_size').outputs[0]: self.min_size,
        
            self.graph.get_operation_by_name('thresholds').outputs[0]: self.thresholds,
        
            self.graph.get_operation_by_name('factor').outputs[0]: self.factor
        
        }
        
        fetches = [self.graph.get_operation_by_name('prob').outputs[0],
        
                  self.graph.get_operation_by_name('landmarks').outputs[0],
        
                  self.graph.get_operation_by_name('box').outputs[0]]
        
        prob, landmarks, box = self.sess.run(fetches, feeds)
        
        return box, landmarks, prob
        

        
    #  Align to get a single face 
        
    def align_face(self, img):
        
        ret = self.detect_faces(img)
        
        if ret is None:
        
            return None
        
        bbox, landmarks, prob = ret
        
        if bbox.shape[0] == 0:
        
            return None
        

        
        landmarks_copy = landmarks.copy()
        
        landmarks[:, 0:5] = landmarks_copy[:, 5:10]
        
        landmarks[:, 5:10] = landmarks_copy[:, 0:5]
        
        # print(landmarks[0, :])
        

        
        bbox = bbox[0, 0:4]
        
        bbox = bbox.astype(int)
        

        
        bbox = bbox[::-1]
        
        bbox_copy = bbox.copy()
        
        bbox[0:2] = bbox_copy[2:4]
        
        bbox[2:4] = bbox_copy[0:2]
        
        # print(bbox)
        

        
        points = landmarks[0, :].reshape((2, 5)).T
        
        # print(points) 
        

        
        '''
        
        face_img = cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 0, 255), 6)
        
        for i in range(5):
        
            pts = points[i, :]
        
            face_img = cv2.circle(face_img, (pts[0], pts[1]), 2, (0, 255, 0), 2)
        
        cv2.imshow('img', face_img)
        
        if cv2.waitKey(100000) & 0xFF == ord('q'):
        
            cv2.destroyAllWindows()   
        
        '''
        

        
        warped_face = face_preprocess.preprocess(img, bbox, points, image_size='112,112')
        
        '''
        
        cv2.imshow('face', warped_face)
        

        
        if cv2.waitKey(100000) & 0xFF == ord('q'):
        
            cv2.destroyAllWindows()
        
        '''
        
        # warped_face = cv2.cvtColor(warped_face, cv2.COLOR_BGR2RGB)
        
        # aligned = np.transpose(warped_face, (2, 0, 1))
        
        # return aligned
        
        return warped_face
        

        
    #  Align to get multiple faces 
        
    def align_multi_faces(self, img, limit=None):
        
        boxes, landmarks, _ = self.detect_faces(img)
        
        if limit:
        
            boxes = boxes[:limit]
        
            landmarks = landmarks[:limit]
        

        
        landmarks_copy = landmarks.copy()
        
        landmarks[:, 0:5] = landmarks_copy[:, 5:10]
        
        landmarks[:, 5:10] = landmarks_copy[:, 0:5]
        

        
        # print('landmarks', landmark)
        
        faces = []
        
        for idx in range(len(landmarks)):
        
            '''
        
            landmark = landmarks[idx, :]
        
            facial5points = [[landmark[j], landmark[j + 5]] for j in range(5)]
        
            warped_face = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112))
        
            faces.append(warped_face)
        
            '''
        
            bbox = boxes[idx, 0:4]
        
            bbox = bbox.astype(int)
        

        
            bbox = bbox[::-1]
        
            bbox_copy = bbox.copy()
        
            bbox[0:2] = bbox_copy[2:4]
        
            bbox[2:4] = bbox_copy[0:2]
        
            # print(bbox)
        

        
            points = landmarks[idx, :].reshape((2, 5)).T
        
            # print(points) 
        
            warped_face = face_preprocess.preprocess(img, bbox, points, image_size='112,112')
        

        
            cv2.imshow('faces', warped_face)
        
            # warped_face = cv2.cvtColor(warped_face, cv2.COLOR_BGR2RGB)
        
            # aligned = np.transpose(warped_face, (2, 0, 1))
        
            faces.append(warped_face)
        

        
        # print('faces',faces)
        
        # print('boxes',boxes)
        
        return faces, boxes, landmarks
       
       

        
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