File size: 4,243 Bytes
1173b78
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
import numpy as np
import torch
from torch.autograd import Variable
import sys

sys.path.append("./")
from align.get_nets import PNet, RNet, ONet
from align.box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
from align.first_stage import run_first_stage


def detect_faces(
    image,
    min_face_size=20.0,
    thresholds=[0.6, 0.7, 0.8],
    nms_thresholds=[0.7, 0.7, 0.7],
):
    """
    Arguments:
        image: an instance of PIL.Image.
        min_face_size: a float number.
        thresholds: a list of length 3.
        nms_thresholds: a list of length 3.

    Returns:
        two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
        bounding boxes and facial landmarks.
    """
    # LOAD MODELS
    pnet = PNet()
    rnet = RNet()
    onet = ONet()
    onet.eval()

    # BUILD AN IMAGE PYRAMID
    width, height = image.size
    min_length = min(height, width)

    min_detection_size = 12
    factor = 0.707  # sqrt(0.5)

    # scales for scaling the image
    scales = []

    # scales the image so that
    # minimum size that we can detect equals to
    # minimum face size that we want to detect
    m = min_detection_size / min_face_size
    min_length *= m

    factor_count = 0
    while min_length > min_detection_size:
        scales.append(m * factor**factor_count)
        min_length *= factor
        factor_count += 1

    # STAGE 1

    # it will be returned
    bounding_boxes = []

    # run P-Net on different scales
    for s in scales:
        boxes = run_first_stage(image, pnet, scale=s, threshold=thresholds[0])
        bounding_boxes.append(boxes)

    # collect boxes (and offsets, and scores) from different scales
    bounding_boxes = [i for i in bounding_boxes if i is not None]
    bounding_boxes = np.vstack(bounding_boxes)

    keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
    bounding_boxes = bounding_boxes[keep]

    # use offsets predicted by pnet to transform bounding boxes
    bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
    # shape [n_boxes, 5]

    bounding_boxes = convert_to_square(bounding_boxes)
    bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

    # STAGE 2

    img_boxes = get_image_boxes(bounding_boxes, image, size=24)
    img_boxes = Variable(torch.FloatTensor(img_boxes), volatile=True)
    output = rnet(img_boxes)
    offsets = output[0].data.numpy()  # shape [n_boxes, 4]
    probs = output[1].data.numpy()  # shape [n_boxes, 2]

    keep = np.where(probs[:, 1] > thresholds[1])[0]
    bounding_boxes = bounding_boxes[keep]
    bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
    offsets = offsets[keep]

    keep = nms(bounding_boxes, nms_thresholds[1])
    bounding_boxes = bounding_boxes[keep]
    bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
    bounding_boxes = convert_to_square(bounding_boxes)
    bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

    # STAGE 3

    img_boxes = get_image_boxes(bounding_boxes, image, size=48)
    if len(img_boxes) == 0:
        return [], []
    img_boxes = Variable(torch.FloatTensor(img_boxes), volatile=True)
    output = onet(img_boxes)
    landmarks = output[0].data.numpy()  # shape [n_boxes, 10]
    offsets = output[1].data.numpy()  # shape [n_boxes, 4]
    probs = output[2].data.numpy()  # shape [n_boxes, 2]

    keep = np.where(probs[:, 1] > thresholds[2])[0]
    bounding_boxes = bounding_boxes[keep]
    bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
    offsets = offsets[keep]
    landmarks = landmarks[keep]

    # compute landmark points
    width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0
    height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0
    xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1]
    landmarks[:, 0:5] = (
        np.expand_dims(xmin, 1) + np.expand_dims(width, 1) * landmarks[:, 0:5]
    )
    landmarks[:, 5:10] = (
        np.expand_dims(ymin, 1) + np.expand_dims(height, 1) * landmarks[:, 5:10]
    )

    bounding_boxes = calibrate_box(bounding_boxes, offsets)
    keep = nms(bounding_boxes, nms_thresholds[2], mode="min")
    bounding_boxes = bounding_boxes[keep]
    landmarks = landmarks[keep]

    return bounding_boxes, landmarks