diff --git a/bob/ip/facedetect/tests/test_tinyface.py b/bob/ip/facedetect/tests/test_tinyface.py
new file mode 100644
index 0000000000000000000000000000000000000000..7518e03c253a8dfdd4f0b3fc237c610f45283222
--- /dev/null
+++ b/bob/ip/facedetect/tests/test_tinyface.py
@@ -0,0 +1,51 @@
+
+from bob.ip.facedetect.tests.utils import is_library_available
+
+import bob.io.image
+import bob.io.base
+import bob.io.base.test_utils
+
+import numpy
+
+
+# An image with one face
+face_image = bob.io.base.load(
+ bob.io.base.test_utils.datafile(
+ 'testimage.jpg', 'bob.ip.facedetect'
+ )
+)
+
+# An image with 6 faces
+face_image_multiple = bob.io.base.load(
+ bob.io.base.test_utils.datafile(
+ 'test_image_multi_face.png', 'bob.ip.facedetect'
+ )
+)
+
+
+def _assert_tinyface_annotations(annot):
+ """
+ Verifies that TinyFace returns the correct coordinates for ``testimage``.
+ """
+ assert len(annot) == 1, f"len: {len(annot)}; {annot}"
+ face = annot[0]
+ assert [int(x) for x in face['topleft']] == [59, 57], face
+ assert [int(x) for x in face['bottomright']] == [338, 284], face
+# assert [int(x) for x in face['reye']] == [180, 129], face
+# assert [int(x) for x in face['leye']] == [175, 220], face
+
+@is_library_available("mxnet")
+def test_tinyface():
+ """TinyFace should annotate one face correctly."""
+ from bob.ip.facedetect.tinyface import TinyFacesDetector
+ tinyface_annotator = TinyFacesDetector()
+ annot = tinyface_annotator.detect(face_image)
+ _assert_tinyface_annotations(annot)
+
+@is_library_available("mxnet")
+def test_tinyface_multiface():
+ """TinyFace should find multiple faces in an image."""
+ from bob.ip.facedetect.tinyface import TinyFacesDetector
+ tinyface_annotator = TinyFacesDetector()
+ annot = tinyface_annotator.detect(face_image_multiple)
+ assert len(annot) == 6
diff --git a/bob/ip/facedetect/tinyface.py b/bob/ip/facedetect/tinyface.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd46c66541c5b843d8285aff12739e81d7aa5b90
--- /dev/null
+++ b/bob/ip/facedetect/tinyface.py
@@ -0,0 +1,201 @@
+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
+
+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):
+ self.MAX_INPUT_DIM=5000.0
+ self.prob_thresh = prob_thresh
+ self.nms_thresh = 0.1
+ self.model_root = pkg_resources.resource_filename(__name__, "tinyface_detector/")
+
+ sym, arg_params, aux_params = mx.model.load_checkpoint(os.path.join(self.model_root, 'hr101'),0)
+ all_layers = sym.get_internals()
+
+ meta_file = open(os.path.join(self.model_root, '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
diff --git a/bob/ip/facedetect/tinyface_detector/hr101-symbol.json b/bob/ip/facedetect/tinyface_detector/hr101-symbol.json
new file mode 100644
index 0000000000000000000000000000000000000000..5ffba939b62592cecbf3bb124a95224e0b3b9e4b
--- /dev/null
+++ b/bob/ip/facedetect/tinyface_detector/hr101-symbol.json
@@ -0,0 +1,9727 @@
+{
+ "nodes": [
+ {
+ "op": "null",
+ "name": "data",
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "conv1_weight",
+ "attrs": {
+ "kernel": "(7, 7)",
+ "no_bias": "True",
+ "num_filter": "64",
+ "pad": "(3, 3)",
+ "stride": "(2, 2)"
+ },
+ "inputs": []
+ },
+ {
+ "op": "Convolution",
+ "name": "conv1",
+ "attrs": {
+ "kernel": "(7, 7)",
+ "no_bias": "True",
+ "num_filter": "64",
+ "pad": "(3, 3)",
+ "stride": "(2, 2)"
+ },
+ "inputs": [[0, 0, 0], [1, 0, 0]]
+ },
+ {
+ "op": "null",
+ "name": "bn_conv1_gamma",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn_conv1_beta",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn_conv1_moving_mean",
+ "attrs": {
+ "__init__": "[\"zero\", {}]",
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn_conv1_moving_var",
+ "attrs": {
+ "__init__": "[\"one\", {}]",
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "BatchNorm",
+ "name": "bn_conv1",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": [[2, 0, 0], [3, 0, 0], [4, 0, 0], [5, 0, 1], [6, 0, 1]]
+ },
+ {
+ "op": "Activation",
+ "name": "conv1_relu",
+ "attrs": {"act_type": "relu"},
+ "inputs": [[7, 0, 0]]
+ },
+ {
+ "op": "Pooling",
+ "name": "pool1",
+ "attrs": {
+ "kernel": "(3, 3)",
+ "pad": "(0, 0)",
+ "pool_type": "max",
+ "pooling_convention": "full",
+ "stride": "(2, 2)"
+ },
+ "inputs": [[8, 0, 0]]
+ },
+ {
+ "op": "null",
+ "name": "res2a_branch1_weight",
+ "attrs": {
+ "kernel": "(1, 1)",
+ "no_bias": "True",
+ "num_filter": "256",
+ "pad": "(0, 0)",
+ "stride": "(1, 1)"
+ },
+ "inputs": []
+ },
+ {
+ "op": "Convolution",
+ "name": "res2a_branch1",
+ "attrs": {
+ "kernel": "(1, 1)",
+ "no_bias": "True",
+ "num_filter": "256",
+ "pad": "(0, 0)",
+ "stride": "(1, 1)"
+ },
+ "inputs": [[9, 0, 0], [10, 0, 0]]
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch1_gamma",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch1_beta",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch1_moving_mean",
+ "attrs": {
+ "__init__": "[\"zero\", {}]",
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch1_moving_var",
+ "attrs": {
+ "__init__": "[\"one\", {}]",
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "BatchNorm",
+ "name": "bn2a_branch1",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": [[11, 0, 0], [12, 0, 0], [13, 0, 0], [14, 0, 1], [15, 0, 1]]
+ },
+ {
+ "op": "null",
+ "name": "res2a_branch2a_weight",
+ "attrs": {
+ "kernel": "(1, 1)",
+ "no_bias": "True",
+ "num_filter": "64",
+ "pad": "(0, 0)",
+ "stride": "(1, 1)"
+ },
+ "inputs": []
+ },
+ {
+ "op": "Convolution",
+ "name": "res2a_branch2a",
+ "attrs": {
+ "kernel": "(1, 1)",
+ "no_bias": "True",
+ "num_filter": "64",
+ "pad": "(0, 0)",
+ "stride": "(1, 1)"
+ },
+ "inputs": [[9, 0, 0], [17, 0, 0]]
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch2a_gamma",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch2a_beta",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch2a_moving_mean",
+ "attrs": {
+ "__init__": "[\"zero\", {}]",
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch2a_moving_var",
+ "attrs": {
+ "__init__": "[\"one\", {}]",
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "BatchNorm",
+ "name": "bn2a_branch2a",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": [[18, 0, 0], [19, 0, 0], [20, 0, 0], [21, 0, 1], [22, 0, 1]]
+ },
+ {
+ "op": "Activation",
+ "name": "res2a_branch2a_relu",
+ "attrs": {"act_type": "relu"},
+ "inputs": [[23, 0, 0]]
+ },
+ {
+ "op": "null",
+ "name": "res2a_branch2b_weight",
+ "attrs": {
+ "kernel": "(3, 3)",
+ "no_bias": "True",
+ "num_filter": "64",
+ "pad": "(1, 1)",
+ "stride": "(1, 1)"
+ },
+ "inputs": []
+ },
+ {
+ "op": "Convolution",
+ "name": "res2a_branch2b",
+ "attrs": {
+ "kernel": "(3, 3)",
+ "no_bias": "True",
+ "num_filter": "64",
+ "pad": "(1, 1)",
+ "stride": "(1, 1)"
+ },
+ "inputs": [[24, 0, 0], [25, 0, 0]]
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch2b_gamma",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch2b_beta",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch2b_moving_mean",
+ "attrs": {
+ "__init__": "[\"zero\", {}]",
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch2b_moving_var",
+ "attrs": {
+ "__init__": "[\"one\", {}]",
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "BatchNorm",
+ "name": "bn2a_branch2b",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": [[26, 0, 0], [27, 0, 0], [28, 0, 0], [29, 0, 1], [30, 0, 1]]
+ },
+ {
+ "op": "Activation",
+ "name": "res2a_branch2b_relu",
+ "attrs": {"act_type": "relu"},
+ "inputs": [[31, 0, 0]]
+ },
+ {
+ "op": "null",
+ "name": "res2a_branch2c_weight",
+ "attrs": {
+ "kernel": "(1, 1)",
+ "no_bias": "True",
+ "num_filter": "256",
+ "pad": "(0, 0)",
+ "stride": "(1, 1)"
+ },
+ "inputs": []
+ },
+ {
+ "op": "Convolution",
+ "name": "res2a_branch2c",
+ "attrs": {
+ "kernel": "(1, 1)",
+ "no_bias": "True",
+ "num_filter": "256",
+ "pad": "(0, 0)",
+ "stride": "(1, 1)"
+ },
+ "inputs": [[32, 0, 0], [33, 0, 0]]
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch2c_gamma",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch2c_beta",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch2c_moving_mean",
+ "attrs": {
+ "__init__": "[\"zero\", {}]",
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "null",
+ "name": "bn2a_branch2c_moving_var",
+ "attrs": {
+ "__init__": "[\"one\", {}]",
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": []
+ },
+ {
+ "op": "BatchNorm",
+ "name": "bn2a_branch2c",
+ "attrs": {
+ "cudnn_off": "True",
+ "eps": "1e-05",
+ "fix_gamma": "False",
+ "use_global_stats": "True"
+ },
+ "inputs": [[34, 0, 0], [35, 0, 0], [36, 0, 0], [37, 0, 1], [38, 0, 1]]
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+}
\ No newline at end of file
diff --git a/bob/ip/facedetect/tinyface_detector/meta.pkl b/bob/ip/facedetect/tinyface_detector/meta.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..1ff5d9ad913797e9ac8a535974580a5477a9b4a9
Binary files /dev/null and b/bob/ip/facedetect/tinyface_detector/meta.pkl differ
diff --git a/doc/index.rst b/doc/index.rst
index 1bb83ec2800f300bd695c9c14ebd759c14966125..808730a6a8bf61a004bd4b3271adaaab5dae6c32 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -35,6 +35,7 @@ Documentation
guide
mtcnn
+ tinyface
py_api
diff --git a/doc/plot/detect_faces_tinyface.py b/doc/plot/detect_faces_tinyface.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a1f643647ce117b639d3350d3e23c0e88bfa50d
--- /dev/null
+++ b/doc/plot/detect_faces_tinyface.py
@@ -0,0 +1,41 @@
+import matplotlib.pyplot as plt
+from bob.io.base import load
+from bob.io.base.test_utils import datafile
+from bob.io.image import imshow
+from bob.ip.facedetect.tinyface import TinyFacesDetector
+from matplotlib.patches import Rectangle
+
+# load colored test image
+color_image = load(datafile("test_image_multi_face.png", "bob.ip.facedetect"))
+is_tf_available = True
+try:
+ import mxnet
+except Exception:
+ is_tf_available = False
+
+if not is_tf_available:
+ imshow(color_image)
+else:
+
+ # detect all face
+ detector = TinyFacesDetector()
+ detections = detector.detect(color_image)
+
+ imshow(color_image)
+ plt.axis("off")
+
+ for annotations in detections:
+ topleft = annotations["topleft"]
+ bottomright = annotations["bottomright"]
+ size = bottomright[0] - topleft[0], bottomright[1] - topleft[1]
+ # draw bounding boxes
+ plt.gca().add_patch(
+ Rectangle(
+ topleft[::-1],
+ size[1],
+ size[0],
+ edgecolor="b",
+ facecolor="none",
+ linewidth=2,
+ )
+ )
\ No newline at end of file
diff --git a/doc/py_api.rst b/doc/py_api.rst
index 443dcf784c91d7a1ecdd4a58ad1e2609f8ba52e3..5b9f22cb213819d45ac1f630a4b69f2497230e86 100644
--- a/doc/py_api.rst
+++ b/doc/py_api.rst
@@ -41,3 +41,4 @@ Detailed Information
.. automodule:: bob.ip.facedetect
.. automodule:: bob.ip.facedetect.mtcnn
+.. automodule:: bob.ip.facedetect.tinyface
diff --git a/doc/tinyface.rst b/doc/tinyface.rst
new file mode 100644
index 0000000000000000000000000000000000000000..aa30c5308df0de8c0880a64325aa8b019456257b
--- /dev/null
+++ b/doc/tinyface.rst
@@ -0,0 +1,19 @@
+
+.. _bob.ip.facedetect.tinyface:
+
+============================
+ Face detection using TinyFaceN
+============================
+
+This package comes with a TinyFace face detector. The Original Model is ``ResNet101``
+from https://github.com/peiyunh/tiny. Please check for more details on TinyFace. The
+model is converted into MxNet Interface and the code used to implement the model are
+from https://github.com/chinakook/hr101_mxnet.
+
+
+See below for an example on how to use
+:any:`bob.ip.facedetect.mtcnn.MTCNN`:
+
+.. plot:: plot/detect_faces_tinyface.py
+ :include-source: True
+
diff --git a/requirements.txt b/requirements.txt
index c82bea78eb8e733e5aa1cf4ab3f9dc5b361ef38c..2724559859c2d539ada87b60beb9cf1a03f8edb3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,3 +7,4 @@ bob.ip.base
bob.ip.color
scikit-image
scipy
+mxnet