This approach follows ideas described in Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. Arxiv 2013.
First of all, we'll need a little Python script to run the Matlab Selective Search code.
Let's run detection on an image of a couple of cats frolicking (one of the ImageNet detection challenge pictures), which we will download from the web. You'll need a prototxt specifying the network, and a trained model.
We will use examples/imagenet_deploy.prototxt and the caffe_reference_imagenet_model.
You'll need to download the model for yourself, and put it in examples/caffe_reference_imagenet_model.
In [1]:
!mkdir _temp
!curl http://farm1.static.flickr.com/220/512450093_7717fb8ce8.jpg > _temp/cat.jpg
!echo `pwd`/_temp/cat.jpg > _temp/cat.txt
!python ../python/caffe/detection/detector.py --crop_mode=selective_search --pretrained_model=caffe_reference_imagenet_model --model_def=imagenet_deploy.prototxt _temp/cat.txt _temp/cat.h5
Running this outputs a DataFrame with the filenames, selected windows, and their ImageNet scores to an HDF5 file. (We only ran on one image, so the filenames will all be the same.)
In [2]:
import pandas as pd
df = pd.read_hdf('_temp/cat.h5', 'df')
print(df.shape)
print(df.iloc[0])
In general, detector.py is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.
Simply list an image per line in the images_file, and it will process all of them.
Although this guide gives an example of ImageNet detection, detector.py is clever enough to adapt to different Caffe models’ input dimensions, batch size, and output categories.
Refer to python detector.py --help and the images_dim and images_mean_file parameters to describe your data set.
No need for hardcoding.
Anyway, let's now load ImageNet class names and make a DataFrame of the features.
In [3]:
with open('../python/caffe/imagenet/ilsvrc_2012_synset_words.txt') as f:
labels_df = pd.DataFrame([
{
'synset_id': l.strip().split(' ')[0],
'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]
}
for l in f.readlines()
])
labels_df.sort('synset_id')
feats_df = pd.DataFrame(np.vstack(df.feat.values), columns=labels_df['name'])
print(feats_df.iloc[0])
Let's look at the activations.
In [4]:
gray()
matshow(feats_df.values)
xlabel('Classes')
ylabel('Windows')
Out[4]:
Now let's take max across all windows and plot the top classes.
In [5]:
max_s = feats_df.max(0)
max_s.sort(ascending=False)
print(max_s[:10])
Okay, there are indeed cats in there (and some nonsense). Picking good localizations is work in progress; manually, we see that the third and thirteenth top detections correspond to the two cats.
In [6]:
# Find, print, and display max detection.
window_order = pd.Series(feats_df.values.max(1)).order(ascending=False)
i = window_order.index[3]
j = window_order.index[13]
# Show top predictions for top detection.
f = pd.Series(df['feat'].iloc[i], index=labels_df['name'])
print('Top detection:')
print(f.order(ascending=False)[:5])
print('')
# Show top predictions for 10th top detection.
f = pd.Series(df['feat'].iloc[j], index=labels_df['name'])
print('10th detection:')
print(f.order(ascending=False)[:5])
# Show top detection in red, 10th top detection in blue.
im = imread('_temp/cat.jpg')
imshow(im)
currentAxis = plt.gca()
det = df.iloc[i]
coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']
currentAxis.add_patch(Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))
det = df.iloc[j]
coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']
currentAxis.add_patch(Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))
Out[6]:
That's cool. Both of these detections are tiger cats. Let's take all 'tiger cat' detections and NMS them to get rid of overlapping windows.
In [7]:
def nms_detections(dets, overlap=0.5):
"""
Non-maximum suppression: Greedily select high-scoring detections and
skip detections that are significantly covered by a previously
selected detection.
This version is translated from Matlab code by Tomasz Malisiewicz,
who sped up Pedro Felzenszwalb's code.
Parameters
----------
dets: ndarray
each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']
overlap: float
minimum overlap ratio (0.5 default)
Output
------
dets: ndarray
remaining after suppression.
"""
if np.shape(dets)[0] < 1:
return dets
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
w = x2 - x1
h = y2 - y1
area = w * h
s = dets[:, 4]
ind = np.argsort(s)
pick = []
counter = 0
while len(ind) > 0:
last = len(ind) - 1
i = ind[last]
pick.append(i)
counter += 1
xx1 = np.maximum(x1[i], x1[ind[:last]])
yy1 = np.maximum(y1[i], y1[ind[:last]])
xx2 = np.minimum(x2[i], x2[ind[:last]])
yy2 = np.minimum(y2[i], y2[ind[:last]])
w = np.maximum(0., xx2 - xx1 + 1)
h = np.maximum(0., yy2 - yy1 + 1)
o = w * h / area[ind[:last]]
to_delete = np.concatenate(
(np.nonzero(o > overlap)[0], np.array([last])))
ind = np.delete(ind, to_delete)
return dets[pick, :]
In [8]:
scores = feats_df['tiger cat']
windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values
dets = np.hstack((windows, scores[:, np.newaxis]))
nms_dets = nms_detections(dets)
Show top 3 NMS'd detections for 'tiger cat' in the image.
In [9]:
imshow(im)
currentAxis = plt.gca()
colors = ['r', 'b', 'y']
for c, det in zip(colors, nms_dets[:3]):
currentAxis.add_patch(
Rectangle((det[0], det[1]), det[2], det[3],
fill=False, edgecolor=c, linewidth=5)
)
Remove the temp directory to clean up.
In [10]:
import shutil
shutil.rmtree('_temp')