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Commit 48c7cb79 authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira
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Removed unecessary file

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1 merge request!16Tinyfacedetect
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doc/plot/detect_code_tinyface.py deleted 100644 → 0
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import mxnet as mx
from mxnet import gluon
from bob.ip.color import gray_to_rgb
import logging
import numpy as np
import cv2 as cv
import pickle
import os, sys
from collections import namedtuple
import time
from bob.io.image import to_matplotlib
import pkg_resources
from bob.extension import rc
from bob.bio.face.embeddings import download_model
logger = logging.getLogger(__name__)
Batch = namedtuple('Batch', ['data'])
class TinyFacesDetector:
"""TinyFace face detector. Original Model is ``ResNet101`` from
https://github.com/peiyunh/tiny. Please check for details. The
model used in this section is the MxNet version from
https://github.com/chinakook/hr101_mxnet.
Attributes
----------
prob_thresh: float
Thresholds are a trade-off between false positives and missed detections.
"""
def __init__(self, prob_thresh=0.5):
internal_path = pkg_resources.resource_filename(
__name__, os.path.join("data", "tinyface_detector/tinyface_detector"),
)
checkpoint_path = (
internal_path
if rc["bob.bio.face.models.tinyface_detector"]
is None
else rc["bob.bio.face.models.tinyface_detector"]
)
urls = ["https://www.idiap.ch/software/bob/data/bob/bob.ip.facedetect/master/tinyface_detector.tar.gz"]
download_model(
checkpoint_path, urls, "tinyface_detector.tar.gz"
)
self.checkpoint_path = checkpoint_path
self.MAX_INPUT_DIM=5000.0
self.prob_thresh = prob_thresh
self.nms_thresh = 0.1
self.model_root = pkg_resources.resource_filename(__name__,self.checkpoint_path)
sym, arg_params, aux_params = mx.model.load_checkpoint(os.path.join(self.checkpoint_path, 'hr101'),0)
all_layers = sym.get_internals()
meta_file = open(os.path.join(self.checkpoint_path, 'meta.pkl'), 'rb')
self.clusters = pickle.load(meta_file)
self.averageImage = pickle.load(meta_file)
meta_file.close()
self.clusters_h = self.clusters[:,3] - self.clusters[:,1] + 1
self.clusters_w = self.clusters[:,2] - self.clusters[:,0] + 1
self.normal_idx = np.where(self.clusters[:,4] == 1)
self.mod = mx.mod.Module(symbol=all_layers['fusex_output'], data_names=['data'], label_names=None)
self.mod.bind(for_training=False, data_shapes=[('data', (1, 3, 224, 224))], label_shapes=None, force_rebind=False)
self.mod.set_params(arg_params=arg_params, aux_params=aux_params, force_init=False)
@staticmethod
def _nms(dets, prob_thresh):
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= prob_thresh)[0]
order = order[inds + 1]
return keep
def detect(self, img):
"""Detects and annotates all faces in the image.
Parameters
----------
image : numpy.ndarray
An RGB image in Bob format.
Returns
-------
list
A list of annotations. Annotations are dictionaries that contain the
following keys: ``topleft``, ``bottomright``, ``reye``, ``leye``.
(``reye`` and ``leye`` are the estimated results, not captured by the
model.)
"""
raw_img = img
if len(raw_img.shape) == 2:
raw_img = gray_to_rgb(raw_img)
assert img.shape[0] == 3, img.shape
raw_img = to_matplotlib(raw_img)
raw_img = raw_img[..., ::-1]
raw_h = raw_img.shape[0]
raw_w = raw_img.shape[1]
raw_img = cv.cvtColor(raw_img, cv.COLOR_BGR2RGB)
raw_img_f = raw_img.astype(np.float32)
min_scale = min(np.floor(np.log2(np.max(self.clusters_w[self.normal_idx]/raw_w))), np.floor(np.log2(np.max(self.clusters_h[self.normal_idx]/raw_h))))
max_scale = min(1.0, -np.log2(max(raw_h, raw_w)/self.MAX_INPUT_DIM))
scales_down = np.arange(min_scale, 0+0.0001, 1.)
scales_up = np.arange(0.5, max_scale+0.0001, 0.5)
scales_pow = np.hstack((scales_down, scales_up))
scales = np.power(2.0, scales_pow)
start = time.time()
bboxes = np.empty(shape=(0,5))
for s in scales[::-1]:
img = cv.resize(raw_img_f, (0,0), fx = s, fy = s)
img = np.transpose(img,(2,0,1))
img = img - self.averageImage
tids = []
if s <= 1. :
tids = list(range(4, 12))
else :
tids = list(range(4, 12)) + list(range(18, 25))
ignoredTids = list(set(range(0,self.clusters.shape[0]))-set(tids))
img_h = img.shape[1]
img_w = img.shape[2]
img = img[np.newaxis, :]
self.mod.reshape(data_shapes=[('data', (1, 3, img_h, img_w))])
self.mod.forward(Batch([mx.nd.array(img)]))
self.mod.get_outputs()[0].wait_to_read()
fusex_res = self.mod.get_outputs()[0]
score_cls = mx.nd.slice_axis(fusex_res, axis=1, begin=0, end=25, name='score_cls')
score_reg = mx.nd.slice_axis(fusex_res, axis=1, begin=25, end=None, name='score_reg')
prob_cls = mx.nd.sigmoid(score_cls)
prob_cls_np = prob_cls.asnumpy()
prob_cls_np[0,ignoredTids,:,:] = 0.
_, fc, fy, fx = np.where(prob_cls_np > self.prob_thresh)
cy = fy * 8 - 1
cx = fx * 8 - 1
ch = self.clusters[fc, 3] - self.clusters[fc,1] + 1
cw = self.clusters[fc, 2] - self.clusters[fc, 0] + 1
Nt = self.clusters.shape[0]
score_reg_np = score_reg.asnumpy()
tx = score_reg_np[0, 0:Nt, :, :]
ty = score_reg_np[0, Nt:2*Nt,:,:]
tw = score_reg_np[0, 2*Nt:3*Nt,:,:]
th = score_reg_np[0,3*Nt:4*Nt,:,:]
dcx = cw * tx[fc, fy, fx]
dcy = ch * ty[fc, fy, fx]
rcx = cx + dcx
rcy = cy + dcy
rcw = cw * np.exp(tw[fc, fy, fx])
rch = ch * np.exp(th[fc, fy, fx])
score_cls_np = score_cls.asnumpy()
scores = score_cls_np[0, fc, fy, fx]
tmp_bboxes = np.vstack((rcx-rcw/2, rcy-rch/2, rcx+rcw/2,rcy+rch/2))
tmp_bboxes = np.vstack((tmp_bboxes/s, scores))
tmp_bboxes = tmp_bboxes.transpose()
bboxes = np.vstack((bboxes, tmp_bboxes))
refind_idx = self._nms(bboxes, self.nms_thresh)
refind_bboxes = bboxes[refind_idx]
refind_bboxes = refind_bboxes.astype(np.int32)
annotations = refind_bboxes
annots = []
for i in range(len(refind_bboxes)):
topleft = float(annotations[i][1]),float(annotations[i][0])
bottomright = float(annotations[i][3]),float(annotations[i][2])
width = float(annotations[i][2]) - float(annotations[i][0])
length = float(annotations[i][3]) - float(annotations[i][1])
right_eye = (0.37) * length + float(annotations[i][1]),(0.3) * width + float(annotations[i][0])
left_eye = (0.37) * length + float(annotations[i][1]),(0.7) * width + float(annotations[i][0])
annots.append(
{
"topleft": topleft,
"bottomright": bottomright,
"reye": right_eye,
"leye": left_eye,
}
)
return annots
\ No newline at end of file
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