update readme for the new demo on resnet.

README.md

@@ -101,9 +101,9 @@ If you find it useful, the ``data/cache`` folder created on my side is also shar
   ```Shell
   # at reposistory root
   GPU_ID=0
-  CUDA_VISIBLE_DEVICES=${GPU_ID} ./tools/demo.py 
+  CUDA_VISIBLE_DEVICES=${GPU_ID} ./tools/demo_depre.py 
   ```
-  **Note** VGG16 testing probably requires 4G memory, so if you are using GPUs with a smaller memory capacity, please install it with CPU support only. Refer to [Issue 25](https://github.com/endernewton/tf-faster-rcnn/issues/25).
+  **Note**: VGG16 testing probably requires 4G memory, so if you are using GPUs with a smaller memory capacity, please install it with CPU support only. Refer to [Issue 25](https://github.com/endernewton/tf-faster-rcnn/issues/25).
 
 3. Create a folder and a softlink to use the pretrained model
   ```Shell

tools/demo.py

@@ -121,8 +121,9 @@ def parse_args():
 
 
     if not os.path.isfile(tfmodel + '.meta'):
-        raise IOError(('{:s} not found.\nDid you run ./data/script/'
-                       'fetch_faster_rcnn_models.sh?').format(tfmodel + '.meta'))
+        raise IOError(('{:s} not found.\nDid you download the proper networks from '
+                       'our server and place them properly? If you want something '
+                       'simple and handy, try ./tools/demo_depre.py first.').format(tfmodel + '.meta'))
 
     # set config
     tfconfig = tf.ConfigProto(allow_soft_placement=True)

tools/demo_depre.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python
+
+# --------------------------------------------------------
+# Tensorflow Faster R-CNN
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Xinlei Chen, based on code from Ross Girshick
+# --------------------------------------------------------
+
+"""
+Demo script showing detections in sample images.
+
+See README.md for installation instructions before running.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import _init_paths
+from model.config import cfg
+from model.test_vgg16 import im_detect
+from model.nms_wrapper import nms
+
+from utils.timer import Timer
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import numpy as np
+import os, cv2
+import argparse
+
+from nets.vgg16_depre import vgg16
+
+CLASSES = ('__background__',
+           'aeroplane', 'bicycle', 'bird', 'boat',
+           'bottle', 'bus', 'car', 'cat', 'chair',
+           'cow', 'diningtable', 'dog', 'horse',
+           'motorbike', 'person', 'pottedplant',
+           'sheep', 'sofa', 'train', 'tvmonitor')
+
+NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt', 'vgg16.weights')}
+
+def vis_detections(im, class_name, dets, thresh=0.5):
+    """Draw detected bounding boxes."""
+    inds = np.where(dets[:, -1] >= thresh)[0]
+    if len(inds) == 0:
+        return
+
+    im = im[:, :, (2, 1, 0)]
+    fig, ax = plt.subplots(figsize=(12, 12))
+    ax.imshow(im, aspect='equal')
+    for i in inds:
+        bbox = dets[i, :4]
+        score = dets[i, -1]
+
+        ax.add_patch(
+            plt.Rectangle((bbox[0], bbox[1]),
+                          bbox[2] - bbox[0],
+                          bbox[3] - bbox[1], fill=False,
+                          edgecolor='red', linewidth=3.5)
+            )
+        ax.text(bbox[0], bbox[1] - 2,
+                '{:s} {:.3f}'.format(class_name, score),
+                bbox=dict(facecolor='blue', alpha=0.5),
+                fontsize=14, color='white')
+
+    ax.set_title(('{} detections with '
+                  'p({} | box) >= {:.1f}').format(class_name, class_name,
+                                                  thresh),
+                  fontsize=14)
+    plt.axis('off')
+    plt.tight_layout()
+    plt.draw()
+
+def demo(sess, net, image_name):
+    """Detect object classes in an image using pre-computed object proposals."""
+
+    # Load the demo image
+    im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
+    im = cv2.imread(im_file)
+
+    # Detect all object classes and regress object bounds
+    timer = Timer()
+    timer.tic()
+    scores, boxes = im_detect(sess, net, im)
+    timer.toc()
+    print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]))
+
+    # Visualize detections for each class
+    CONF_THRESH = 0.8
+    NMS_THRESH = 0.3
+    for cls_ind, cls in enumerate(CLASSES[1:]):
+        cls_ind += 1 # because we skipped background
+        cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
+        cls_scores = scores[:, cls_ind]
+        dets = np.hstack((cls_boxes,
+                          cls_scores[:, np.newaxis])).astype(np.float32)
+        keep = nms(dets, NMS_THRESH)
+        dets = dets[keep, :]
+        vis_detections(im, cls, dets, thresh=CONF_THRESH)
+
+def parse_args():
+    """Parse input arguments."""
+    parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo')
+    parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',
+                        choices=NETS.keys(), default='vgg16')
+
+    args = parser.parse_args()
+
+    return args
+
+if __name__ == '__main__':
+    cfg.TEST.HAS_RPN = True  # Use RPN for proposals
+    args = parse_args()
+
+    # model path
+    demonet = args.demo_net
+    tfmodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models', 'voc_2007_trainval', 'default',
+                              NETS[demonet][0])
+    if not os.path.isfile(tfmodel + '.meta'):
+        raise IOError(('{:s} not found.\nDid you run ./data/script/'
+                       'fetch_faster_rcnn_models.sh?').format(tfmodel + '.meta'))
+
+    # weight path
+    tfweight = os.path.join(cfg.DATA_DIR, 'imagenet_weights', NETS[demonet][1])
+    if not os.path.isfile(tfweight):
+        raise IOError(('{:s} not found.\nDid you run ./data/script/'
+                       'fetch_imagenet_weights.sh?').format(tfweight))
+
+    # set config
+    tfconfig = tf.ConfigProto(allow_soft_placement=True)
+    tfconfig.gpu_options.allow_growth=True
+
+    # init session
+    sess = tf.Session(config=tfconfig)
+    # load network
+    if demonet == 'vgg16':
+        net = vgg16(batch_size=1)
+    else:
+        raise NotImplementedError
+
+    net.create_architecture(sess, "TEST", 21, caffe_weight_path=tfweight, 
+                          tag='default', anchor_scales=[8, 16, 32])
+    saver = tf.train.Saver()
+    saver.restore(sess, tfmodel)
+
+    print('Loaded network {:s}'.format(tfmodel))
+
+    im_names = ['000456.jpg', '000542.jpg', '001150.jpg',
+                '001763.jpg', '004545.jpg']
+    for im_name in im_names:
+        print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
+        print('Demo for data/demo/{}'.format(im_name))
+        demo(sess, net, im_name)
+
+    plt.show()