Tensorflow で SSD
動作環境
$ cat /etc/os-release NAME="Ubuntu" VERSION="14.04.5 LTS, Trusty Tahr" ID=ubuntu ID_LIKE=debian PRETTY_NAME="Ubuntu 14.04.5 LTS" VERSION_ID="14.04" HOME_URL="http://www.ubuntu.com/" SUPPORT_URL="http://help.ubuntu.com/" BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/" $ nvcc -V nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2016 NVIDIA Corporation Built on Tue_Jan_10_13:22:03_CST_2017 Cuda compilation tools, release 8.0, V8.0.61 $ ls /usr/local/cuda/lib64/libcudnn.so.* /usr/local/cuda/lib64/libcudnn.so.5 /usr/local/cuda/lib64/libcudnn.so.5.1.10 $ pip list | grep tensorflow tensorflow-gpu 1.4.1 tensorflow-tensorboard 0.4.0 $ python Python 3.4.3 (default, Nov 28 2017, 16:41:13) [GCC 4.8.4] on linux Type "help", "copyright", "credits" or "license" for more information.
SSD の取得と、設定?
$ git clone https://github.com/balancap/SSD-Tensorflow.git $ cd SSD-Tensorflow $ cd checkpoints $ unzip ssd_300_vgg.ckpt.zip
サンプルコードを改変して、動かす
$ cd ../notebooks/ $ jupyter nbconvert --to python ssd_notebook.ipynb
matplotlib の設定
$ cat ~/.config/matplotlib/matplotlibrc font.family : IPAexGothic backend : tkagg
サンプルコードを改変
せっかくなので、すべての画像を処理することと、ラベルを設定しました。
./datasets/pascalvoc_common.py
の中身を、使用しやすいように書き換えてます。
SSDによる物体検出を試してみた - TadaoYamaokaの日記
こちら様のデータを使用しています。
ssd_notebook.py
# coding: utf-8 import os import math import random import numpy as np import tensorflow as tf import cv2 slim = tf.contrib.slim #get_ipython().magic('matplotlib inline') import matplotlib.pyplot as plt import matplotlib.image as mpimg import sys sys.path.append('../') from nets import ssd_vgg_300, ssd_common, np_methods from preprocessing import ssd_vgg_preprocessing from notebooks import visualization # TensorFlow session: grow memory when needed. TF, DO NOT USE ALL MY GPU MEMORY!!! gpu_options = tf.GPUOptions(allow_growth=True) config = tf.ConfigProto(log_device_placement=False, gpu_options=gpu_options) isess = tf.InteractiveSession(config=config) # ## SSD 300 Model # # The SSD 300 network takes 300x300 image inputs. In order to feed any image, the latter is resize to this input shape (i.e.`Resize.WARP_RESIZE`). Note that even though it may change the ratio width / height, the SSD model performs well on resized images (and it is the default behaviour in the original Caffe implementation). # # SSD anchors correspond to the default bounding boxes encoded in the network. The SSD net output provides offset on the coordinates and dimensions of these anchors. # Input placeholder. net_shape = (300, 300) data_format = 'NHWC' img_input = tf.placeholder(tf.uint8, shape=(None, None, 3)) # Evaluation pre-processing: resize to SSD net shape. image_pre, labels_pre, bboxes_pre, bbox_img = ssd_vgg_preprocessing.preprocess_for_eval( img_input, None, None, net_shape, data_format, resize=ssd_vgg_preprocessing.Resize.WARP_RESIZE) image_4d = tf.expand_dims(image_pre, 0) # Define the SSD model. reuse = True if 'ssd_net' in locals() else None ssd_net = ssd_vgg_300.SSDNet() with slim.arg_scope(ssd_net.arg_scope(data_format=data_format)): predictions, localisations, _, _ = ssd_net.net(image_4d, is_training=False, reuse=reuse) # Restore SSD model. ckpt_filename = '../checkpoints/ssd_300_vgg.ckpt' # ckpt_filename = '../checkpoints/VGG_VOC0712_SSD_300x300_ft_iter_120000.ckpt' isess.run(tf.global_variables_initializer()) saver = tf.train.Saver() saver.restore(isess, ckpt_filename) # SSD default anchor boxes. ssd_anchors = ssd_net.anchors(net_shape) # ## Post-processing pipeline # # The SSD outputs need to be post-processed to provide proper detections. Namely, we follow these common steps: # # * Select boxes above a classification threshold; # * Clip boxes to the image shape; # * Apply the Non-Maximum-Selection algorithm: fuse together boxes whose Jaccard score > threshold; # * If necessary, resize bounding boxes to original image shape. # Main image processing routine. def process_image(img, select_threshold=0.5, nms_threshold=.45, net_shape=(300, 300)): # Run SSD network. rimg, rpredictions, rlocalisations, rbbox_img = isess.run([image_4d, predictions, localisations, bbox_img], feed_dict={img_input: img}) # Get classes and bboxes from the net outputs. rclasses, rscores, rbboxes = np_methods.ssd_bboxes_select( rpredictions, rlocalisations, ssd_anchors, select_threshold=select_threshold, img_shape=net_shape, num_classes=21, decode=True) rbboxes = np_methods.bboxes_clip(rbbox_img, rbboxes) rclasses, rscores, rbboxes = np_methods.bboxes_sort(rclasses, rscores, rbboxes, top_k=400) rclasses, rscores, rbboxes = np_methods.bboxes_nms(rclasses, rscores, rbboxes, nms_threshold=nms_threshold) # Resize bboxes to original image shape. Note: useless for Resize.WARP! rbboxes = np_methods.bboxes_resize(rbbox_img, rbboxes) return rclasses, rscores, rbboxes # Test on some demo image and visualize output. path = '../demo/' image_names = sorted(os.listdir(path)) for i in range(12): img = mpimg.imread(path + image_names[i]) rclasses, rscores, rbboxes = process_image(img)
visualization.py
# Copyright 2017 Paul Balanca. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== import sys import cv2 import random import matplotlib.pyplot as plt import matplotlib.image as mpimg import matplotlib.cm as mpcm VOC_LABELS = { 0: 'none', 1: 'aeroplane', 2: 'bicycle', 3: 'bird', 4: 'boat', 5: 'bottle', 6: 'bus', 7: 'car', 8: 'cat', 9: 'chair', 10: 'cow', 11: 'diningtable', 12: 'dog', 13: 'horse', 14: 'motorbike', 15: 'person', 16: 'pottedplant', 17: 'sheep', 18: 'sofa', 19: 'train', 20: 'tvmonitor', } # =========================================================================== # # Some colormaps. # =========================================================================== # def colors_subselect(colors, num_classes=21): dt = len(colors) // num_classes sub_colors = [] for i in range(num_classes): color = colors[i*dt] if isinstance(color[0], float): sub_colors.append([int(c * 255) for c in color]) else: sub_colors.append([c for c in color]) return sub_colors colors_plasma = colors_subselect(mpcm.plasma.colors, num_classes=21) colors_tableau = [(255, 255, 255), (31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] # =========================================================================== # # OpenCV drawing. # =========================================================================== # def draw_lines(img, lines, color=[255, 0, 0], thickness=2): """Draw a collection of lines on an image. """ for line in lines: for x1, y1, x2, y2 in line: cv2.line(img, (x1, y1), (x2, y2), color, thickness) def draw_rectangle(img, p1, p2, color=[255, 0, 0], thickness=2): cv2.rectangle(img, p1[::-1], p2[::-1], color, thickness) def draw_bbox(img, bbox, shape, label, color=[255, 0, 0], thickness=2): p1 = (int(bbox[0] * shape[0]), int(bbox[1] * shape[1])) p2 = (int(bbox[2] * shape[0]), int(bbox[3] * shape[1])) cv2.rectangle(img, p1[::-1], p2[::-1], color, thickness) p1 = (p1[0]+15, p1[1]) cv2.putText(img, str(label), p1[::-1], cv2.FONT_HERSHEY_DUPLEX, 0.5, color, 1) def bboxes_draw_on_img(img, classes, scores, bboxes, colors, thickness=2): shape = img.shape for i in range(bboxes.shape[0]): bbox = bboxes[i] color = colors[classes[i]] # Draw bounding box... p1 = (int(bbox[0] * shape[0]), int(bbox[1] * shape[1])) p2 = (int(bbox[2] * shape[0]), int(bbox[3] * shape[1])) cv2.rectangle(img, p1[::-1], p2[::-1], color, thickness) # Draw text... s = '%s/%.3f' % (classes[i], scores[i]) p1 = (p1[0]-5, p1[1]) cv2.putText(img, s, p1[::-1], cv2.FONT_HERSHEY_DUPLEX, 0.4, color, 1) # =========================================================================== # # Matplotlib show... # =========================================================================== # def plt_bboxes(f_name, img, classes, scores, bboxes, figsize=(10,10), linewidth=1.5): """Visualize bounding boxes. Largely inspired by SSD-MXNET! """ fig = plt.figure(figsize=figsize) plt.imshow(img) height = img.shape[0] width = img.shape[1] colors = dict() for i in range(classes.shape[0]): cls_id = int(classes[i]) if cls_id >= 0: score = scores[i] if cls_id not in colors: colors[cls_id] = (random.random(), random.random(), random.random()) ymin = int(bboxes[i, 0] * height) xmin = int(bboxes[i, 1] * width) ymax = int(bboxes[i, 2] * height) xmax = int(bboxes[i, 3] * width) rect = plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, edgecolor=colors[cls_id], linewidth=linewidth) plt.gca().add_patch(rect) class_name = str(cls_id) class_name = VOC_LABELS[cls_id] plt.gca().text(xmin, ymin - 2, '{:s} | {:.3f}'.format(class_name, score), bbox=dict(facecolor=colors[cls_id], alpha=0.5), fontsize=12, color='white') plt.savefig( './results/'+f_name + '.png' ) plt.show()
結果