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)): + 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+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