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import numpy as np
from .box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
from .first_stage import run_first_stage
import onnxruntime
import os
from os.path import exists
import requests
def download_img(img_url, base_dir):
print("Downloading Onnx Model in:",img_url)
r = requests.get(img_url, stream=True)
filename = img_url.split("/")[-1]
# print(r.status_code) # 返回状态码
if r.status_code == 200:
open(f'{base_dir}/{filename}', 'wb').write(r.content) # 将内容写入图片
print(f"Download Finshed -- {filename}")
del r
def detect_faces(image, min_face_size=20.0, thresholds=None, nms_thresholds=None):
"""
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.
"""
if nms_thresholds is None:
nms_thresholds = [0.7, 0.7, 0.7]
if thresholds is None:
thresholds = [0.6, 0.7, 0.8]
base_url = "https://linimages.oss-cn-beijing.aliyuncs.com/"
onnx_filedirs = ["pnet.onnx", "rnet.onnx", "onet.onnx"]
# LOAD MODELS
basedir = os.path.dirname(os.path.realpath(__file__)).split("detector.py")[0]
for onnx_filedir in onnx_filedirs:
if not exists(f"{basedir}/weights"):
os.mkdir(f"{basedir}/weights")
if not exists(f"{basedir}/weights/{onnx_filedir}"):
# download onnx model
download_img(img_url=base_url+onnx_filedir, base_dir=f"{basedir}/weights")
pnet = onnxruntime.InferenceSession(f"{basedir}/weights/pnet.onnx") # Load a ONNX model
input_name_pnet = pnet.get_inputs()[0].name
output_name_pnet1 = pnet.get_outputs()[0].name
output_name_pnet2 = pnet.get_outputs()[1].name
pnet = [pnet, input_name_pnet, [output_name_pnet1, output_name_pnet2]]
rnet = onnxruntime.InferenceSession(f"{basedir}/weights/rnet.onnx") # Load a ONNX model
input_name_rnet = rnet.get_inputs()[0].name
output_name_rnet1 = rnet.get_outputs()[0].name
output_name_rnet2 = rnet.get_outputs()[1].name
rnet = [rnet, input_name_rnet, [output_name_rnet1, output_name_rnet2]]
onet = onnxruntime.InferenceSession(f"{basedir}/weights/onet.onnx") # Load a ONNX model
input_name_onet = onet.get_inputs()[0].name
output_name_onet1 = onet.get_outputs()[0].name
output_name_onet2 = onet.get_outputs()[1].name
output_name_onet3 = onet.get_outputs()[2].name
onet = [onet, input_name_onet, [output_name_onet1, output_name_onet2, output_name_onet3]]
# 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)
output = rnet[0].run([rnet[2][0], rnet[2][1]], {rnet[1]: img_boxes})
offsets = output[0] # shape [n_boxes, 4]
probs = output[1] # 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)
# with torch.no_grad():
# img_boxes = torch.FloatTensor(img_boxes)
# output = onet(img_boxes)
output = onet[0].run([onet[2][0], onet[2][1], onet[2][2]], {rnet[1]: img_boxes})
landmarks = output[0] # shape [n_boxes, 10]
offsets = output[1] # shape [n_boxes, 4]
probs = output[2] # 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
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