Spaces:
Build error
Build error
File size: 6,220 Bytes
ac4ce84 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
import numpy as np
import torch
from PIL import Image
from models.mtcnn.mtcnn_pytorch.src.get_nets import PNet, RNet, ONet
from models.mtcnn.mtcnn_pytorch.src.box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
from models.mtcnn.mtcnn_pytorch.src.first_stage import run_first_stage
from models.mtcnn.mtcnn_pytorch.src.align_trans import get_reference_facial_points, warp_and_crop_face
device = 'cuda:0'
class MTCNN():
def __init__(self):
print(device)
self.pnet = PNet().to(device)
self.rnet = RNet().to(device)
self.onet = ONet().to(device)
self.pnet.eval()
self.rnet.eval()
self.onet.eval()
self.refrence = get_reference_facial_points(default_square=True)
def align(self, img):
_, landmarks = self.detect_faces(img)
if len(landmarks) == 0:
return None, None
facial5points = [[landmarks[0][j], landmarks[0][j + 5]] for j in range(5)]
warped_face, tfm = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112))
return Image.fromarray(warped_face), tfm
def align_multi(self, img, limit=None, min_face_size=30.0):
boxes, landmarks = self.detect_faces(img, min_face_size)
if limit:
boxes = boxes[:limit]
landmarks = landmarks[:limit]
faces = []
tfms = []
for landmark in landmarks:
facial5points = [[landmark[j], landmark[j + 5]] for j in range(5)]
warped_face, tfm = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112))
faces.append(Image.fromarray(warped_face))
tfms.append(tfm)
return boxes, faces, tfms
def detect_faces(self, image, min_face_size=20.0,
thresholds=[0.15, 0.25, 0.35],
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.
"""
# 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 = []
with torch.no_grad():
# run P-Net on different scales
for s in scales:
boxes = run_first_stage(image, self.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 = torch.FloatTensor(img_boxes).to(device)
output = self.rnet(img_boxes)
offsets = output[0].cpu().data.numpy() # shape [n_boxes, 4]
probs = output[1].cpu().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 = torch.FloatTensor(img_boxes).to(device)
output = self.onet(img_boxes)
landmarks = output[0].cpu().data.numpy() # shape [n_boxes, 10]
offsets = output[1].cpu().data.numpy() # shape [n_boxes, 4]
probs = output[2].cpu().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
|