diff options
Diffstat (limited to 'anime-face-detector')
| -rw-r--r-- | anime-face-detector/.gitignore | 110 | ||||
| -rw-r--r-- | anime-face-detector/LICENSE | 21 | ||||
| -rw-r--r-- | anime-face-detector/Makefile | 6 | ||||
| -rw-r--r-- | anime-face-detector/README.md | 89 | ||||
| -rw-r--r-- | anime-face-detector/asset/sample1.png | bin | 1594270 -> 0 bytes | |||
| -rw-r--r-- | anime-face-detector/asset/sample2.png | bin | 1156897 -> 0 bytes | |||
| -rw-r--r-- | anime-face-detector/asset/sample3.png | bin | 1737489 -> 0 bytes | |||
| -rw-r--r-- | anime-face-detector/faster_rcnn_wrapper.py | 204 | ||||
| -rw-r--r-- | anime-face-detector/main.py | 170 | ||||
| -rw-r--r-- | anime-face-detector/make.bat | 20 | ||||
| -rw-r--r-- | anime-face-detector/model/.gitignore | 5 | ||||
| -rw-r--r-- | anime-face-detector/nms/.gitignore | 2 | ||||
| -rw-r--r-- | anime-face-detector/nms/__init__.py | 0 | ||||
| -rw-r--r-- | anime-face-detector/nms/cpu_nms.pyx | 68 | ||||
| -rw-r--r-- | anime-face-detector/nms/gpu_nms.hpp | 2 | ||||
| -rw-r--r-- | anime-face-detector/nms/gpu_nms.pyx | 31 | ||||
| -rw-r--r-- | anime-face-detector/nms/nms_kernel.cu | 144 | ||||
| -rw-r--r-- | anime-face-detector/nms/py_cpu_nms.py | 38 | ||||
| -rw-r--r-- | anime-face-detector/nms_wrapper.py | 29 | ||||
| -rw-r--r-- | anime-face-detector/setup.py | 42 |
20 files changed, 0 insertions, 981 deletions
diff --git a/anime-face-detector/.gitignore b/anime-face-detector/.gitignore deleted file mode 100644 index ff81ae6..0000000 --- a/anime-face-detector/.gitignore +++ /dev/null @@ -1,110 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# pyenv -.python-version - -# celery beat schedule file -celerybeat-schedule - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ - -# idea pycharm data -.idea/ - -# cython build result -build/ diff --git a/anime-face-detector/LICENSE b/anime-face-detector/LICENSE deleted file mode 100644 index 3010384..0000000 --- a/anime-face-detector/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2018 Zhou Xuebin - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/anime-face-detector/Makefile b/anime-face-detector/Makefile deleted file mode 100644 index 1e9e686..0000000 --- a/anime-face-detector/Makefile +++ /dev/null @@ -1,6 +0,0 @@ -all: - python setup.py build_ext --inplace - rm -rf build -clean: - rm -rf */*.pyc - rm -rf */*.so diff --git a/anime-face-detector/README.md b/anime-face-detector/README.md deleted file mode 100644 index 72a242e..0000000 --- a/anime-face-detector/README.md +++ /dev/null @@ -1,89 +0,0 @@ -# Anime-Face-Detector -A Faster-RCNN based anime face detector. - -This detector is trained on 6000 training samples and 641 testing samples, randomly selected from the dataset which is crawled from top 100 [pixiv daily ranking](https://www.pixiv.net/ranking.php?mode=daily). - -Thanks to [OpenCV based Anime face detector](https://github.com/nagadomi/lbpcascade_animeface) written by nagadomi, which helps labelling the data. - -The original implementation of Faster-RCNN using Tensorflow can be found [here](https://github.com/endernewton/tf-faster-rcnn) - -## Dependencies -- Python 3.6 or 3.7 -- `tensorflow` < 2.0 -- `opencv-python` -- `cython` (optional, can be ignored with additional `-nms-type PY_NMS` argument) -- Pre-trained ResNet101 model - -## Usage -1. Clone this repository - ```bash - git clone https://github.com/qhgz2013/anime-face-detector.git - ``` -2. Download the pre-trained model - Google Drive: [here](https://drive.google.com/open?id=1WjBgfOUqp4sdRd9BHs4TkdH2EcBtV5ri) - Baidu Netdisk: [here](https://pan.baidu.com/s/1bvpCp1sbD7t9qnta8IhpmA) -3. Unzip the model file into `model` directory -4. Build the CPU NMS model (skip this step if use PY_NMS with argument: `-nms-type PY_NMS`) - ```bash - make clean - make - ``` - If using Windows Power Shell, type `cmd /C make.bat` to run build script. -5. Run the demo as you want - - Visualize the result (without output path): - ```bash - python main.py -i /path/to/image.jpg - ``` - - Save results to a json file - ```bash - python main.py -i /path/to/image.jpg -o /path/to/output.json - ``` - Format: `{"image_path": [{"score": predicted_probability, "bbox": [min_x, min_y, max_x, max_y]}, ...], ...}` - Sample output file: - ```json - {"/path/to/image.jpg": [{"score": 0.9999708, "bbox": [551.3375, 314.50253, 729.2599, 485.25674]}]} - ``` - - Detecting a whole directory with recursion - ```bash - python main.py -i /path/to/dir -o /path/to/output.json - ``` - - Customize threshold - ```bash - python main.py -i /path/to/image.jpg -nms 0.3 -conf 0.8 - ``` - - Customize model path - ```bash - python main.py -i /path/to/image.jpg -model /path/to/model.ckpt - ``` - - Customize nms type (supports CPU_NMS and PY_NMS, not supports GPU_NMS because of the complicated build process for Windows platform) - ```bash - python main.py -i /path/to/image.jpg -nms-type PY_NMS - ``` - -## Results -**Mean AP for this model: 0.9086** - - -Copyright info: [東方まとめ](https://www.pixiv.net/member_illust.php?mode=medium&illust_id=54275439) by [羽々斬](https://www.pixiv.net/member.php?id=2179695) - - -Copyright info: [【C94】桜と刀](https://www.pixiv.net/member_illust.php?mode=medium&illust_id=69797346) by [幻像黒兎](https://www.pixiv.net/member.php?id=4462245) - - -Copyright info: [アイドルマスター シンデレラガールズ](https://www.pixiv.net/member_illust.php?mode=medium&illust_id=69753772) by [我美蘭＠１日目 東A-40a](https://www.pixiv.net/member.php?id=2003931) - -## About training - -This model is directly trained by [Faster-RCNN](https://github.com/endernewton/tf-faster-rcnn), with following argument: -```bash -python tools/trainval_net.py --weight data/imagenet_weights/res101.ckpt --imdb voc_2007_trainval --imdbval voc_2007_test --iters 60000 --cfg experiments/cfgs/res101.yml --net res101 --set ANCHOR_SCALES "[4,8,16,32]" ANCHOR_RATIOS "[1]" TRAIN.STEPSIZE "[50000]" -``` - -## Dataset - -We've uploaded the dataset to Google drive [here](https://drive.google.com/open?id=1nDPimhiwbAWc2diok-6davhubNVe82pr), dataset structure is similar to VOC2007 (used in original Faster-RCNN implementation). - -## Citation and declaration - -Feel free to cite this repo and dataset. -This work is not related to my research team and lab, just my personal interest. diff --git a/anime-face-detector/asset/sample1.png b/anime-face-detector/asset/sample1.png Binary files differdeleted file mode 100644 index 857ee97..0000000 --- a/anime-face-detector/asset/sample1.png +++ /dev/null diff --git a/anime-face-detector/asset/sample2.png b/anime-face-detector/asset/sample2.png Binary files differdeleted file mode 100644 index eda9ca0..0000000 --- a/anime-face-detector/asset/sample2.png +++ /dev/null diff --git a/anime-face-detector/asset/sample3.png b/anime-face-detector/asset/sample3.png Binary files differdeleted file mode 100644 index 583542d..0000000 --- a/anime-face-detector/asset/sample3.png +++ /dev/null diff --git a/anime-face-detector/faster_rcnn_wrapper.py b/anime-face-detector/faster_rcnn_wrapper.py deleted file mode 100644 index 3ea09ff..0000000 --- a/anime-face-detector/faster_rcnn_wrapper.py +++ /dev/null @@ -1,204 +0,0 @@ -import tensorflow as tf -from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block, resnet_v1 -import tensorflow.contrib.slim as slim -from tensorflow.contrib.slim.python.slim.nets.resnet_utils import arg_scope, conv2d_same -import numpy as np - - -class FasterRCNNSlim: - - def __init__(self): - self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), - resnet_v1_block('block2', base_depth=128, num_units=4, stride=2), - resnet_v1_block('block3', base_depth=256, num_units=23, stride=1), - resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)] - self._image = tf.placeholder(tf.float32, shape=[1, None, None, 3]) - self._im_info = tf.placeholder(tf.float32, shape=[3]) - - self._anchor_scales = [4, 8, 16, 32] - self._num_scales = len(self._anchor_scales) - - self._anchor_ratios = [1] - self._num_ratios = len(self._anchor_ratios) - - self._num_anchors = self._num_scales * self._num_ratios - self._scope = 'resnet_v1_101' - - with arg_scope([slim.conv2d, slim.conv2d_in_plane, slim.conv2d_transpose, slim.separable_conv2d, - slim.fully_connected], - weights_regularizer=tf.contrib.layers.l2_regularizer(0.0001), - biases_regularizer=tf.no_regularizer, - biases_initializer=tf.constant_initializer(0.0)): - # in _build_network - initializer = tf.random_normal_initializer(stddev=0.01) - initializer_bbox = tf.random_normal_initializer(stddev=0.001) - # in _image_to_head - with slim.arg_scope(self._resnet_arg_scope()): - # in _build_base - with tf.variable_scope(self._scope, self._scope): - net_conv = conv2d_same(self._image, 64, 7, stride=2, scope='conv1') - net_conv = tf.pad(net_conv, [[0, 0], [1, 1], [1, 1], [0, 0]]) - net_conv = slim.max_pool2d(net_conv, [3, 3], stride=2, padding='VALID', scope='pool1') - net_conv, _ = resnet_v1(net_conv, self._blocks[:-1], global_pool=False, include_root_block=False, - scope=self._scope) - with tf.variable_scope(self._scope, self._scope): - # in _anchor_component - with tf.variable_scope('ANCHOR-default'): - height = tf.to_int32(tf.ceil(self._im_info[0] / 16.0)) - width = tf.to_int32(tf.ceil(self._im_info[1] / 16.0)) - - shift_x = tf.range(width) * 16 - shift_y = tf.range(height) * 16 - shift_x, shift_y = tf.meshgrid(shift_x, shift_y) - sx = tf.reshape(shift_x, [-1]) - sy = tf.reshape(shift_y, [-1]) - shifts = tf.transpose(tf.stack([sx, sy, sx, sy])) - k = width * height - shifts = tf.transpose(tf.reshape(shifts, [1, k, 4]), perm=[1, 0, 2]) - - anchors = np.array([[-24, -24, 39, 39], [-56, -56, 71, 71], - [-120, -120, 135, 135], [-248, -248, 263, 263]], dtype=np.int32) - - a = anchors.shape[0] - anchor_constant = tf.constant(anchors.reshape([1, a, 4]), dtype=tf.int32) - length = k * a - anchors_tf = tf.reshape(anchor_constant + shifts, shape=[length, 4]) - anchors = tf.cast(anchors_tf, dtype=tf.float32) - self._anchors = anchors - self._anchor_length = length - - # in _region_proposal - rpn = slim.conv2d(net_conv, 512, [3, 3], trainable=False, weights_initializer=initializer, - scope='rpn_conv/3x3') - rpn_cls_score = slim.conv2d(rpn, self._num_anchors * 2, [1, 1], trainable=False, - weights_initializer=initializer, padding='VALID', activation_fn=None, - scope='rpn_cls_score') - rpn_cls_score_reshape = self._reshape(rpn_cls_score, 2, 'rpn_cls_score_reshape') - rpn_cls_prob_reshape = self._softmax(rpn_cls_score_reshape, 'rpn_cls_prob_reshape') - # rpn_cls_pred = tf.argmax(tf.reshape(rpn_cls_score_reshape, [-1, 2]), axis=1, name='rpn_cls_pred') - rpn_cls_prob = self._reshape(rpn_cls_prob_reshape, self._num_anchors * 2, 'rpn_cls_prob') - rpn_bbox_pred = slim.conv2d(rpn, self._num_anchors * 4, [1, 1], trainable=False, - weights_initializer=initializer, padding='VALID', activation_fn=None, - scope='rpn_bbox_pred') - - # in _proposal_layer - with tf.variable_scope('rois'): - post_nms_topn = 300 - nms_thresh = 0.7 - scores = rpn_cls_prob[:, :, :, self._num_anchors:] - scores = tf.reshape(scores, [-1]) - rpn_bbox_pred = tf.reshape(rpn_bbox_pred, [-1, 4]) - - boxes = tf.cast(self._anchors, rpn_bbox_pred.dtype) - widths = boxes[:, 2] - boxes[:, 0] + 1.0 - heights = boxes[:, 3] - boxes[:, 1] + 1.0 - ctr_x = boxes[:, 0] + widths * 0.5 - ctr_y = boxes[:, 1] + heights * 0.5 - - dx = rpn_bbox_pred[:, 0] - dy = rpn_bbox_pred[:, 1] - dw = rpn_bbox_pred[:, 2] - dh = rpn_bbox_pred[:, 3] - - pred_ctr_x = dx * widths + ctr_x - pred_ctr_y = dy * heights + ctr_y - pred_w = tf.exp(dw) * widths - pred_h = tf.exp(dh) * heights - - pred_boxes0 = pred_ctr_x - pred_w * 0.5 - pred_boxes1 = pred_ctr_y - pred_h * 0.5 - pred_boxes2 = pred_ctr_x + pred_w * 0.5 - pred_boxes3 = pred_ctr_y + pred_h * 0.5 - - b0 = tf.clip_by_value(pred_boxes0, 0, self._im_info[1] - 1) - b1 = tf.clip_by_value(pred_boxes1, 0, self._im_info[0] - 1) - b2 = tf.clip_by_value(pred_boxes2, 0, self._im_info[1] - 1) - b3 = tf.clip_by_value(pred_boxes3, 0, self._im_info[0] - 1) - - proposals = tf.stack([b0, b1, b2, b3], axis=1) - indices = tf.image.non_max_suppression(proposals, scores, max_output_size=post_nms_topn, - iou_threshold=nms_thresh) - boxes = tf.to_float(tf.gather(proposals, indices)) - # rpn_scores = tf.reshape(tf.gather(scores, indices), [-1, 1]) - - batch_inds = tf.zeros([tf.shape(indices)[0], 1], dtype=tf.float32) - rois = tf.concat([batch_inds, boxes], 1) - - # in _crop_pool_layer - with tf.variable_scope('pool5'): - batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name='bath_id'), [1]) - bottom_shape = tf.shape(net_conv) - height = (tf.to_float(bottom_shape[1]) - 1) * 16.0 - width = (tf.to_float(bottom_shape[2]) - 1) * 16.0 - x1 = tf.slice(rois, [0, 1], [-1, 1], name='x1') / width - y1 = tf.slice(rois, [0, 2], [-1, 1], name='y1') / height - x2 = tf.slice(rois, [0, 3], [-1, 1], name='x2') / width - y2 = tf.slice(rois, [0, 4], [-1, 1], name='y2') / height - bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], 1)) - pool5 = tf.image.crop_and_resize(net_conv, bboxes, tf.to_int32(batch_ids), [7, 7], name='crops') - # in _head_to_tail - with slim.arg_scope(self._resnet_arg_scope()): - fc7, _ = resnet_v1(pool5, self._blocks[-1:], global_pool=False, include_root_block=False, - scope=self._scope) - fc7 = tf.reduce_mean(fc7, axis=[1, 2]) - with tf.variable_scope(self._scope, self._scope): - # in _region_classification - cls_score = slim.fully_connected(fc7, 2, weights_initializer=initializer, trainable=False, - activation_fn=None, scope='cls_score') - cls_prob = self._softmax(cls_score, 'cls_prob') - # cls_pred = tf.argmax(cls_score, 'cls_pred') - bbox_pred = slim.fully_connected(fc7, 2*4, weights_initializer=initializer_bbox, trainable=False, - activation_fn=None, scope='bbox_pred') - self._cls_score = cls_score - self._cls_prob = cls_prob - self._bbox_pred = bbox_pred - self._rois = rois - - stds = np.tile(np.array([0.1, 0.1, 0.2, 0.2]), 2) - means = np.tile(np.array([0.0, 0.0, 0.0, 0.0]), 2) - self._bbox_pred *= stds - self._bbox_pred += means - - @staticmethod - def _resnet_arg_scope(): - batch_norm_params = { - 'is_training': False, - 'decay': 0.997, - 'epsilon': 1e-5, - 'scale': True, - 'trainable': False, - 'updates_collections': tf.GraphKeys.UPDATE_OPS - } - with arg_scope([slim.conv2d], - weights_regularizer=slim.l2_regularizer(0.0001), - weights_initializer=slim.variance_scaling_initializer(), - trainable=False, - activation_fn=tf.nn.relu, - normalizer_fn=slim.batch_norm, - normalizer_params=batch_norm_params): - with arg_scope([slim.batch_norm], **batch_norm_params) as arg_sc: - return arg_sc - - @staticmethod - def _reshape(bottom, num_dim, name): - input_shape = tf.shape(bottom) - with tf.variable_scope(name): - to_caffe = tf.transpose(bottom, [0, 3, 1, 2]) - reshaped = tf.reshape(to_caffe, [1, num_dim, -1, input_shape[2]]) - to_tf = tf.transpose(reshaped, [0, 2, 3, 1]) - return to_tf - - @staticmethod - def _softmax(bottom, name): - if name.startswith('rpn_cls_prob_reshape'): - input_shape = tf.shape(bottom) - bottom_reshaped = tf.reshape(bottom, [-1, input_shape[-1]]) - reshaped_score = tf.nn.softmax(bottom_reshaped, name=name) - return tf.reshape(reshaped_score, input_shape) - return tf.nn.softmax(bottom, name=name) - - def test_image(self, sess, image, im_info): - return sess.run([self._cls_score, self._cls_prob, self._bbox_pred, self._rois], feed_dict={ - self._image: image, - self._im_info: im_info - }) diff --git a/anime-face-detector/main.py b/anime-face-detector/main.py deleted file mode 100644 index 11f7e4d..0000000 --- a/anime-face-detector/main.py +++ /dev/null @@ -1,170 +0,0 @@ -import numpy as np -import cv2 -from faster_rcnn_wrapper import FasterRCNNSlim -import tensorflow as tf -import argparse -import os -import json -import time -from nms_wrapper import NMSType, NMSWrapper - - -def detect(sess, rcnn_cls, image): - # pre-processing image for Faster-RCNN - img_origin = image.astype(np.float32, copy=True) - img_origin -= np.array([[[102.9801, 115.9465, 112.7717]]]) - - img_shape = img_origin.shape - img_size_min = np.min(img_shape[:2]) - img_size_max = np.max(img_shape[:2]) - - img_scale = 600 / img_size_min - if np.round(img_scale * img_size_max) > 1000: - img_scale = 1000 / img_size_max - img = cv2.resize(img_origin, None, None, img_scale, img_scale, cv2.INTER_LINEAR) - img_info = np.array([img.shape[0], img.shape[1], img_scale], dtype=np.float32) - img = np.expand_dims(img, 0) - - # test image - _, scores, bbox_pred, rois = rcnn_cls.test_image(sess, img, img_info) - - # bbox transform - boxes = rois[:, 1:] / img_scale - - boxes = boxes.astype(bbox_pred.dtype, copy=False) - widths = boxes[:, 2] - boxes[:, 0] + 1 - heights = boxes[:, 3] - boxes[:, 1] + 1 - ctr_x = boxes[:, 0] + 0.5 * widths - ctr_y = boxes[:, 1] + 0.5 * heights - dx = bbox_pred[:, 0::4] - dy = bbox_pred[:, 1::4] - dw = bbox_pred[:, 2::4] - dh = bbox_pred[:, 3::4] - pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis] - pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis] - pred_w = np.exp(dw) * widths[:, np.newaxis] - pred_h = np.exp(dh) * heights[:, np.newaxis] - pred_boxes = np.zeros_like(bbox_pred, dtype=bbox_pred.dtype) - pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w - pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h - pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w - pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h - # clipping edge - pred_boxes[:, 0::4] = np.maximum(pred_boxes[:, 0::4], 0) - pred_boxes[:, 1::4] = np.maximum(pred_boxes[:, 1::4], 0) - pred_boxes[:, 2::4] = np.minimum(pred_boxes[:, 2::4], img_shape[1] - 1) - pred_boxes[:, 3::4] = np.minimum(pred_boxes[:, 3::4], img_shape[0] - 1) - return scores, pred_boxes - - -def load_file_from_dir(dir_path): - ret = [] - for file in os.listdir(dir_path): - path_comb = os.path.join(dir_path, file) - if os.path.isdir(path_comb): - ret += load_file_from_dir(path_comb) - else: - ret.append(path_comb) - return ret - - -def fmt_time(dtime): - if dtime <= 0: - return '0:00.000' - elif dtime < 60: - return '0:%02d.%03d' % (int(dtime), int(dtime * 1000) % 1000) - elif dtime < 3600: - return '%d:%02d.%03d' % (int(dtime / 60), int(dtime) % 60, int(dtime * 1000) % 1000) - else: - return '%d:%02d:%02d.%03d' % (int(dtime / 3600), int((dtime % 3600) / 60), int(dtime) % 60, - int(dtime * 1000) % 1000) - - -def main(): - parser = argparse.ArgumentParser(description='Anime face detector demo') - parser.add_argument('-i', help='The input path of an image or directory', required=True, dest='input', type=str) - parser.add_argument('-o', help='The output json path of the detection result', dest='output') - parser.add_argument('-nms', help='Change the threshold for non maximum suppression', - dest='nms_thresh', default=0.3, type=float) - parser.add_argument('-conf', help='Change the threshold for class regression', dest='conf_thresh', - default=0.8, type=float) - parser.add_argument('-model', help='Specify a new path for model', dest='model', type=str, - default='model/res101_faster_rcnn_iter_60000.ckpt') - parser.add_argument('-nms-type', help='Type of nms', choices=['PY_NMS', 'CPU_NMS', 'GPU_NMS'], dest='nms_type', - default='CPU_NMS') - - args = parser.parse_args() - - assert os.path.exists(args.input), 'The input path does not exists' - - if os.path.isdir(args.input): - files = load_file_from_dir(args.input) - else: - files = [args.input] - file_len = len(files) - - if args.nms_type == 'PY_NMS': - nms_type = NMSType.PY_NMS - elif args.nms_type == 'CPU_NMS': - nms_type = NMSType.CPU_NMS - elif args.nms_type == 'GPU_NMS': - nms_type = NMSType.GPU_NMS - else: - raise ValueError('Incorrect NMS Type, not supported yet') - - nms = NMSWrapper(nms_type) - - cfg = tf.ConfigProto() - cfg.gpu_options.allow_growth = True - sess = tf.Session(config=cfg) - - net = FasterRCNNSlim() - saver = tf.train.Saver() - - saver.restore(sess, args.model) - - result = {} - - time_start = time.time() - - for idx, file in enumerate(files): - elapsed = time.time() - time_start - eta = (file_len - idx) * elapsed / idx if idx > 0 else 0 - print('[%d/%d] Elapsed: %s, ETA: %s >> %s' % (idx+1, file_len, fmt_time(elapsed), fmt_time(eta), file)) - img = cv2.imread(file) - scores, boxes = detect(sess, net, img) - boxes = boxes[:, 4:8] - scores = scores[:, 1] - keep = nms(np.hstack([boxes, scores[:, np.newaxis]]).astype(np.float32), args.nms_thresh) - boxes = boxes[keep, :] - scores = scores[keep] - inds = np.where(scores >= args.conf_thresh)[0] - scores = scores[inds] - boxes = boxes[inds, :] - - result[file] = [] - for i in range(scores.shape[0]): - x1, y1, x2, y2 = boxes[i, :].tolist() - new_result = {'score': float(scores[i]), - 'bbox': [x1, y1, x2, y2]} - result[file].append(new_result) - - if args.output is None: - cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2) - if args.output: - if ((idx+1) % 1000) == 0: - # saving the temporary result - with open(args.output, 'w') as f: - json.dump(result, f) - else: - cv2.imshow(file, img) - - if args.output: - with open(args.output, 'w') as f: - json.dump(result, f) - else: - cv2.waitKey() - - -if __name__ == '__main__': - main() diff --git a/anime-face-detector/make.bat b/anime-face-detector/make.bat deleted file mode 100644 index b0d9bff..0000000 --- a/anime-face-detector/make.bat +++ /dev/null @@ -1,20 +0,0 @@ -@echo off -if /i "%1" == "clean" goto clean -goto all - -:all -python setup.py build_ext --inplace -rd /s /q build - -goto exit - - - -:clean -del /f /s /q *.cpp -del /f /s /q *.c -del /f /s /q *.pyd - -goto exit - -:exit diff --git a/anime-face-detector/model/.gitignore b/anime-face-detector/model/.gitignore deleted file mode 100644 index b1d31d3..0000000 --- a/anime-face-detector/model/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -# all pre-trained models -*.index -*.data-00000-of-00001 -*.meta -*.pkl diff --git a/anime-face-detector/nms/.gitignore b/anime-face-detector/nms/.gitignore deleted file mode 100644 index 40d7cb4..0000000 --- a/anime-face-detector/nms/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -*.c -*.cpp diff --git a/anime-face-detector/nms/__init__.py b/anime-face-detector/nms/__init__.py deleted file mode 100644 index e69de29..0000000 --- a/anime-face-detector/nms/__init__.py +++ /dev/null diff --git a/anime-face-detector/nms/cpu_nms.pyx b/anime-face-detector/nms/cpu_nms.pyx deleted file mode 100644 index 71fbab1..0000000 --- a/anime-face-detector/nms/cpu_nms.pyx +++ /dev/null @@ -1,68 +0,0 @@ -# -------------------------------------------------------- -# Fast R-CNN -# Copyright (c) 2015 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ross Girshick -# -------------------------------------------------------- - -import numpy as np -cimport numpy as np - -cdef inline np.float32_t max(np.float32_t a, np.float32_t b): - return a if a >= b else b - -cdef inline np.float32_t min(np.float32_t a, np.float32_t b): - return a if a <= b else b - -def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): - cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] - cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] - cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] - cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] - cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] - - cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) - cdef np.ndarray[np.int64_t, ndim=1] order = scores.argsort()[::-1] - - cdef int ndets = dets.shape[0] - cdef np.ndarray[np.int_t, ndim=1] suppressed = \ - np.zeros((ndets), dtype=np.int) - - # nominal indices - cdef int _i, _j - # sorted indices - cdef int i, j - # temp variables for box i's (the box currently under consideration) - cdef np.float32_t ix1, iy1, ix2, iy2, iarea - # variables for computing overlap with box j (lower scoring box) - cdef np.float32_t xx1, yy1, xx2, yy2 - cdef np.float32_t w, h - cdef np.float32_t inter, ovr - - keep = [] - for _i in range(ndets): - i = order[_i] - if suppressed[i] == 1: - continue - keep.append(i) - ix1 = x1[i] - iy1 = y1[i] - ix2 = x2[i] - iy2 = y2[i] - iarea = areas[i] - for _j in range(_i + 1, ndets): - j = order[_j] - if suppressed[j] == 1: - continue - xx1 = max(ix1, x1[j]) - yy1 = max(iy1, y1[j]) - xx2 = min(ix2, x2[j]) - yy2 = min(iy2, y2[j]) - w = max(0.0, xx2 - xx1 + 1) - h = max(0.0, yy2 - yy1 + 1) - inter = w * h - ovr = inter / (iarea + areas[j] - inter) - if ovr >= thresh: - suppressed[j] = 1 - - return keep diff --git a/anime-face-detector/nms/gpu_nms.hpp b/anime-face-detector/nms/gpu_nms.hpp deleted file mode 100644 index 68b6d42..0000000 --- a/anime-face-detector/nms/gpu_nms.hpp +++ /dev/null @@ -1,2 +0,0 @@ -void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num, - int boxes_dim, float nms_overlap_thresh, int device_id); diff --git a/anime-face-detector/nms/gpu_nms.pyx b/anime-face-detector/nms/gpu_nms.pyx deleted file mode 100644 index 55878db..0000000 --- a/anime-face-detector/nms/gpu_nms.pyx +++ /dev/null @@ -1,31 +0,0 @@ -# -------------------------------------------------------- -# Faster R-CNN -# Copyright (c) 2015 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ross Girshick -# -------------------------------------------------------- - -import numpy as np -cimport numpy as np - -assert sizeof(int) == sizeof(np.int32_t) - -cdef extern from "gpu_nms.hpp": - void _nms(np.int32_t*, int*, np.float32_t*, int, int, float, int) - -def gpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh, - np.int32_t device_id=0): - cdef int boxes_num = dets.shape[0] - cdef int boxes_dim = dets.shape[1] - cdef int num_out - cdef np.ndarray[np.int32_t, ndim=1] \ - keep = np.zeros(boxes_num, dtype=np.int32) - cdef np.ndarray[np.float32_t, ndim=1] \ - scores = dets[:, 4] - cdef np.ndarray[np.int64_t, ndim=1] \ - order = scores.argsort()[::-1] - cdef np.ndarray[np.float32_t, ndim=2] \ - sorted_dets = dets[order, :] - _nms(&keep[0], &num_out, &sorted_dets[0, 0], boxes_num, boxes_dim, thresh, device_id) - keep = keep[:num_out] - return list(order[keep]) diff --git a/anime-face-detector/nms/nms_kernel.cu b/anime-face-detector/nms/nms_kernel.cu deleted file mode 100644 index 038a590..0000000 --- a/anime-face-detector/nms/nms_kernel.cu +++ /dev/null @@ -1,144 +0,0 @@ -// ------------------------------------------------------------------ -// Faster R-CNN -// Copyright (c) 2015 Microsoft -// Licensed under The MIT License [see fast-rcnn/LICENSE for details] -// Written by Shaoqing Ren -// ------------------------------------------------------------------ - -#include "gpu_nms.hpp" -#include <vector> -#include <iostream> - -#define CUDA_CHECK(condition) \ - /* Code block avoids redefinition of cudaError_t error */ \ - do { \ - cudaError_t error = condition; \ - if (error != cudaSuccess) { \ - std::cout << cudaGetErrorString(error) << std::endl; \ - } \ - } while (0) - -#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) -int const threadsPerBlock = sizeof(unsigned long long) * 8; - -__device__ inline float devIoU(float const * const a, float const * const b) { - float left = max(a[0], b[0]), right = min(a[2], b[2]); - float top = max(a[1], b[1]), bottom = min(a[3], b[3]); - float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f); - float interS = width * height; - float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1); - float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1); - return interS / (Sa + Sb - interS); -} - -__global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh, - const float *dev_boxes, unsigned long long *dev_mask) { - const int row_start = blockIdx.y; - const int col_start = blockIdx.x; - - // if (row_start > col_start) return; - - const int row_size = - min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); - const int col_size = - min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); - - __shared__ float block_boxes[threadsPerBlock * 5]; - if (threadIdx.x < col_size) { - block_boxes[threadIdx.x * 5 + 0] = - dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; - block_boxes[threadIdx.x * 5 + 1] = - dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; - block_boxes[threadIdx.x * 5 + 2] = - dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; - block_boxes[threadIdx.x * 5 + 3] = - dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; - block_boxes[threadIdx.x * 5 + 4] = - dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; - } - __syncthreads(); - - if (threadIdx.x < row_size) { - const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; - const float *cur_box = dev_boxes + cur_box_idx * 5; - int i = 0; - unsigned long long t = 0; - int start = 0; - if (row_start == col_start) { - start = threadIdx.x + 1; - } - for (i = start; i < col_size; i++) { - if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) { - t |= 1ULL << i; - } - } - const int col_blocks = DIVUP(n_boxes, threadsPerBlock); - dev_mask[cur_box_idx * col_blocks + col_start] = t; - } -} - -void _set_device(int device_id) { - int current_device; - CUDA_CHECK(cudaGetDevice(¤t_device)); - if (current_device == device_id) { - return; - } - // The call to cudaSetDevice must come before any calls to Get, which - // may perform initialization using the GPU. - CUDA_CHECK(cudaSetDevice(device_id)); -} - -void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num, - int boxes_dim, float nms_overlap_thresh, int device_id) { - _set_device(device_id); - - float* boxes_dev = NULL; - unsigned long long* mask_dev = NULL; - - const int col_blocks = DIVUP(boxes_num, threadsPerBlock); - - CUDA_CHECK(cudaMalloc(&boxes_dev, - boxes_num * boxes_dim * sizeof(float))); - CUDA_CHECK(cudaMemcpy(boxes_dev, - boxes_host, - boxes_num * boxes_dim * sizeof(float), - cudaMemcpyHostToDevice)); - - CUDA_CHECK(cudaMalloc(&mask_dev, - boxes_num * col_blocks * sizeof(unsigned long long))); - - dim3 blocks(DIVUP(boxes_num, threadsPerBlock), - DIVUP(boxes_num, threadsPerBlock)); - dim3 threads(threadsPerBlock); - nms_kernel<<<blocks, threads>>>(boxes_num, - nms_overlap_thresh, - boxes_dev, - mask_dev); - - std::vector<unsigned long long> mask_host(boxes_num * col_blocks); - CUDA_CHECK(cudaMemcpy(&mask_host[0], - mask_dev, - sizeof(unsigned long long) * boxes_num * col_blocks, - cudaMemcpyDeviceToHost)); - - std::vector<unsigned long long> remv(col_blocks); - memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); - - int num_to_keep = 0; - for (int i = 0; i < boxes_num; i++) { - int nblock = i / threadsPerBlock; - int inblock = i % threadsPerBlock; - - if (!(remv[nblock] & (1ULL << inblock))) { - keep_out[num_to_keep++] = i; - unsigned long long *p = &mask_host[0] + i * col_blocks; - for (int j = nblock; j < col_blocks; j++) { - remv[j] |= p[j]; - } - } - } - *num_out = num_to_keep; - - CUDA_CHECK(cudaFree(boxes_dev)); - CUDA_CHECK(cudaFree(mask_dev)); -} diff --git a/anime-face-detector/nms/py_cpu_nms.py b/anime-face-detector/nms/py_cpu_nms.py deleted file mode 100644 index 54e7b25..0000000 --- a/anime-face-detector/nms/py_cpu_nms.py +++ /dev/null @@ -1,38 +0,0 @@ -# -------------------------------------------------------- -# Fast R-CNN -# Copyright (c) 2015 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ross Girshick -# -------------------------------------------------------- - -import numpy as np - -def py_cpu_nms(dets, thresh): - """Pure Python NMS baseline.""" - 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 <= thresh)[0] - order = order[inds + 1] - - return keep diff --git a/anime-face-detector/nms_wrapper.py b/anime-face-detector/nms_wrapper.py deleted file mode 100644 index ca900e8..0000000 --- a/anime-face-detector/nms_wrapper.py +++ /dev/null @@ -1,29 +0,0 @@ -from enum import Enum - - -class NMSType(Enum): - PY_NMS = 1 - CPU_NMS = 2 - GPU_NMS = 3 - - -default_nms_type = NMSType.PY_NMS - - -class NMSWrapper: - def __init__(self, nms_type=default_nms_type): - assert type(nms_type) == NMSType - if nms_type == NMSType.PY_NMS: - from nms.py_cpu_nms import py_cpu_nms - self._nms = py_cpu_nms - elif nms_type == NMSType.CPU_NMS: - from nms.cpu_nms import cpu_nms - self._nms = cpu_nms - elif nms_type == NMSType.GPU_NMS: - from nms.gpu_nms import gpu_nms - self._nms = gpu_nms - else: - raise ValueError('current nms type is not implemented yet') - - def __call__(self, *args, **kwargs): - return self._nms(*args, **kwargs) diff --git a/anime-face-detector/setup.py b/anime-face-detector/setup.py deleted file mode 100644 index dc634f5..0000000 --- a/anime-face-detector/setup.py +++ /dev/null @@ -1,42 +0,0 @@ -# -------------------------------------------------------- -# Fast R-CNN -# Copyright (c) 2015 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ross Girshick -# -------------------------------------------------------- - -import os -from os.path import join as pjoin -import numpy as np -from distutils.core import setup -from distutils.extension import Extension -from Cython.Distutils import build_ext -import sys - - -# Obtain the numpy include directory. This logic works across numpy versions. -try: - numpy_include = np.get_include() -except AttributeError: - numpy_include = np.get_numpy_include() - -# run the customize_compiler -class custom_build_ext(build_ext): - def build_extensions(self): - build_ext.build_extensions(self) - -ext_modules = [ - Extension( - "nms.cpu_nms", - ["nms/cpu_nms.pyx"], - extra_compile_args=["-Wno-cpp", "-Wno-unused-function"] if sys.platform == 'linux' else [], - include_dirs = [numpy_include] - ) -] - -setup( - name='tf_faster_rcnn', - ext_modules=ext_modules, - # inject our custom trigger - cmdclass={'build_ext': custom_build_ext}, -) |
