1
# -*- coding: utf-8 -*-
2
"""
3
Created on Wed Jul 29 17:52:00 2020
4

5
@author: hp
6
"""
7

8
import cv2
9
import numpy as np
10

11
def get_face_detector(modelFile=None,
12
                      configFile=None,
13
                      quantized=False):
14
    """
15
    Get the face detection caffe model of OpenCV's DNN module
16
    
17
    Parameters
18
    ----------
19
    modelFile : string, optional
20
        Path to model file. The default is "models/res10_300x300_ssd_iter_140000.caffemodel" or models/opencv_face_detector_uint8.pb" based on quantization.
21
    configFile : string, optional
22
        Path to config file. The default is "models/deploy.prototxt" or "models/opencv_face_detector.pbtxt" based on quantization.
23
    quantization: bool, optional
24
        Determines whether to use quantized tf model or unquantized caffe model. The default is False.
25
    
26
    Returns
27
    -------
28
    model : dnn_Net
29

30
    """
31
    if quantized:
32
        if modelFile == None:
33
            modelFile = "models/opencv_face_detector_uint8.pb"
34
        if configFile == None:
35
            configFile = "models/opencv_face_detector.pbtxt"
36
        model = cv2.dnn.readNetFromTensorflow(modelFile, configFile)
37
        
38
    else:
39
        if modelFile == None:
40
            modelFile = "models/res10_300x300_ssd_iter_140000.caffemodel"
41
        if configFile == None:
42
            configFile = "models/deploy.prototxt"
43
        model = cv2.dnn.readNetFromCaffe(configFile, modelFile)
44
    return model
45

46
def find_faces(img, model):
47
    """
48
    Find the faces in an image
49
    
50
    Parameters
51
    ----------
52
    img : np.uint8
53
        Image to find faces from
54
    model : dnn_Net
55
        Face detection model
56

57
    Returns
58
    -------
59
    faces : list
60
        List of coordinates of the faces detected in the image
61

62
    """
63
    h, w = img.shape[:2]
64
    blob = cv2.dnn.blobFromImage(cv2.resize(img, (300, 300)), 1.0,
65
	(300, 300), (104.0, 177.0, 123.0))
66
    model.setInput(blob)
67
    res = model.forward()
68
    faces = []
69
    for i in range(res.shape[2]):
70
        confidence = res[0, 0, i, 2]
71
        if confidence > 0.5:
72
            box = res[0, 0, i, 3:7] * np.array([w, h, w, h])
73
            (x, y, x1, y1) = box.astype("int")
74
            faces.append([x, y, x1, y1])
75
    return faces
76

77
def draw_faces(img, faces):
78
    """
79
    Draw faces on image
80

81
    Parameters
82
    ----------
83
    img : np.uint8
84
        Image to draw faces on
85
    faces : List of face coordinates
86
        Coordinates of faces to draw
87

88
    Returns
89
    -------
90
    None.
91

92
    """
93
    for x, y, x1, y1 in faces:
94
        cv2.rectangle(img, (x, y), (x1, y1), (0, 0, 255), 3)
95
        
96