add a demo script

DSOD300_detection_demo.py

@@ -0,0 +1,126 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.font_manager as fm
+import matplotlib.cm as cm
+# %matplotlib inline
+
+plt.rcParams['figure.figsize'] = (10, 10)
+plt.rcParams['image.interpolation'] = 'nearest'
+
+# Make sure that caffe is on the python path:
+import os
+import sys
+sys.path.append('./python')
+
+import caffe
+
+# gpu:
+# caffe.set_device(0)
+# caffe.set_mode_gpu()
+
+# cpu:
+caffe.set_mode_cpu()
+
+from google.protobuf import text_format
+from caffe.proto import caffe_pb2
+
+# load PASCAL VOC labels
+labelmap_file = 'data/VOC0712/labelmap_voc.prototxt'
+file = open(labelmap_file, 'r')
+labelmap = caffe_pb2.LabelMap()
+text_format.Merge(str(file.read()), labelmap)
+
+def get_labelname(labelmap, labels):
+    num_labels = len(labelmap.item)
+    labelnames = []
+    if type(labels) is not list:
+        labels = [labels]
+    for label in labels:
+        found = False
+        for i in xrange(0, num_labels):
+            if label == labelmap.item[i].label:
+                found = True
+                labelnames.append(labelmap.item[i].display_name)
+                break
+        assert found == True
+    return labelnames
+
+#Load the net in the test phase for inference, and configure input preprocessing.
+model_def = 'examples/07+12+coco/deploy.prototxt'
+model_weights = 'examples/07+12+coco/DSOD300_VOC0712+coco.caffemodel'
+
+net = caffe.Net(model_def,      # defines the structure of the model
+                model_weights,  # contains the trained weights
+                caffe.TEST)     # use test mode (e.g., don't perform dropout)
+
+# input preprocessing: 'data' is the name of the input blob == net.inputs[0]
+transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
+transformer.set_transpose('data', (2, 0, 1))
+transformer.set_mean('data', np.array([104,117,123])) # mean pixel
+transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]
+transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB
+
+# DSOD detection
+
+# set net to batch size of 1
+image_resize = 300
+net.blobs['data'].reshape(1,3,image_resize,image_resize)
+
+# set colors
+colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()
+
+#Load an image.
+img = "examples/images/cat.jpg"
+image = caffe.io.load_image(img)
+plt.imshow(image)
+
+#Run the net and examine the top_k results
+
+transformed_image = transformer.preprocess('data', image)
+net.blobs['data'].data[...] = transformed_image
+
+# Forward pass.
+detections = net.forward()['detection_out']
+
+# Parse the outputs.
+det_label = detections[0,0,:,1]
+det_conf = detections[0,0,:,2]
+det_xmin = detections[0,0,:,3]
+det_ymin = detections[0,0,:,4]
+det_xmax = detections[0,0,:,5]
+det_ymax = detections[0,0,:,6]
+
+# Get detections with confidence higher than 0.6.
+top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.6]
+
+top_conf = det_conf[top_indices]
+top_label_indices = det_label[top_indices].tolist()
+top_labels = get_labelname(labelmap, top_label_indices)
+top_xmin = det_xmin[top_indices]
+top_ymin = det_ymin[top_indices]
+top_xmax = det_xmax[top_indices]
+top_ymax = det_ymax[top_indices]
+
+#Plot the boxes
+plt.imshow(image)
+currentAxis = plt.gca()
+
+for i in xrange(top_conf.shape[0]):
+    xmin = int(round(top_xmin[i] * image.shape[1]))
+    ymin = int(round(top_ymin[i] * image.shape[0]))
+    xmax = int(round(top_xmax[i] * image.shape[1]))
+    ymax = int(round(top_ymax[i] * image.shape[0]))
+    score = top_conf[i]
+    label = int(top_label_indices[i])
+    label_name = top_labels[i]
+    display_txt = '%s: %.2f'%(label_name, score)
+    coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1
+    color = colors[label]
+    currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=6))
+    currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5}, fontproperties=fm.FontProperties(size=22))
+
+currentAxis.axes.get_xaxis().set_visible(False)
+currentAxis.axes.get_yaxis().set_visible(False)
+
+plt.show()
+

README.md

@@ -7,7 +7,6 @@ The code is based on the SSD framework (https://github.com/weiliu89/caffe/tree/s
 
 If you use these models or find this helps your research, please cite:
 
-
 	@inproceedings{Shen2017DSOD,
 		title = {DSOD: Learning Deeply Supervised Object Detectors from Scratch},
 		author = {Shen, Zhiqiang and Liu, Zhuang and Li, Jianguo and Jiang, Yu-Gang and Chen, Yurong and Xue, Xiangyang},
@@ -58,14 +57,12 @@ COCO test-dev 2015 result (COCO has more object categories than VOC dataset, so
 |:-------|:-----:|:-----:|
 | DSOD300 (COCO trainval) | 29.3 | [Download (87.2M)](https://drive.google.com/open?id=0B4cvsEOB5eUCYXoxcGRCbVFMNms) |
 
-
 ## Preparation 
 
 0. Install SSD (https://github.com/weiliu89/caffe/tree/ssd) following the instructions there, including: (1) Install SSD caffe; (2) Download PASCAL VOC 2007 and 2012 datasets; and (3) Create LMDB file. Make sure you can run it without any errors.
-1. Create a subfolder `dsod` under `example/`, add files `DSOD300_pascal.py`, `DSOD300_pascal++.py`, `DSOD300_coco.py` and `score_DSOD300_pascal.py` to the folder `example/dsod/`.
+1. Create a subfolder `dsod` under `example/`, add files `DSOD300_pascal.py`, `DSOD300_pascal++.py`, `DSOD300_coco.py`, `score_DSOD300_pascal.py` and `DSOD300_detection_demo.py` to the folder `example/dsod/`.
 2. Replace the file `model_libs.py` in the folder `python/caffe/` with ours.
 
-
 ## Training & Testing
 
 - Train a DSOD model on VOC 07+12:
@@ -86,12 +83,16 @@ COCO test-dev 2015 result (COCO has more object categories than VOC dataset, so
   python examples/dsod/DSOD300_coco.py
   ```
 
-
 - Evaluate the model:
 
-
   ```shell
   python examples/dsod/score_DSOD300_pascal.py
+  ```
+
+- Run a demo:
+
+  ```shell
+  python examples/dsod/DSOD300_detection_demo.py
   ```
 
  **Note**: You can modify the file `model_lib.py` to design your own network structure as you like.
@@ -109,4 +110,3 @@ Zhiqiang Shen (zhiqiangshen13 at fudan.edu.cn)
 Zhuang Liu (liuzhuangthu at gmail.com)
 
 Any comments or suggestions are welcome!
-