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#!/usr/bin/env python
"""
Do windowed detection by classifying a number of images/crops at once,
optionally using the selective search window proposal method.
This implementation follows ideas in
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik.
Rich feature hierarchies for accurate object detection and semantic
segmentation.
http://arxiv.org/abs/1311.2524
The selective_search_ijcv_with_python code required for the selective search
proposal mode is available at
https://github.com/sergeyk/selective_search_ijcv_with_python
TODO
- R-CNN crop mode / crop with context.
- Bundle with R-CNN model for example.
"""
import numpy as np
import os
import caffe
class Detector(caffe.Net):
"""
Detector extends Net for windowed detection by a list of crops or
selective search proposals.
"""
def __init__(self, model_file, pretrained_file, gpu=False, mean_file=None,
input_scale=None, channel_swap=None):
"""
Take
gpu, mean_file, input_scale, channel_swap: convenience params for
setting mode, mean, input scale, and channel order.
"""
caffe.Net.__init__(self, model_file, pretrained_file)
self.set_phase_test()
if gpu:
self.set_mode_gpu()
else:
self.set_mode_cpu()
if mean_file:
self.set_mean(self.inputs[0], mean_file)
if input_scale:
self.set_input_scale(self.inputs[0], input_scale)
if channel_swap:
self.set_channel_swap(self.inputs[0], channel_swap)
def detect_windows(self, images_windows):
"""
Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net.
Take
images_windows: (image filename, window list) iterable.
Give
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts.
"""
# Extract windows.
window_inputs = []
for image_fname, windows in images_windows:
image = caffe.io.load_image(image_fname).astype(np.float32)
for window in windows:
window_inputs.append(image[window[0]:window[2],
window[1]:window[3]])
# Run through the net (warping windows to input dimensions).
caffe_in = self.preprocess(self.inputs[0], window_inputs)
out = self.forward_all(**{self.inputs[0]: caffe_in})
predictions = out[self.outputs[0]].squeeze(axis=(2,3))
# Package predictions with images and windows.
detections = []
ix = 0
for image_fname, windows in images_windows:
for window in windows:
detections.append({
'window': window,
'prediction': predictions[ix],
'filename': image_fname
})
ix += 1
return detections
def detect_selective_search(self, image_fnames):
"""
Do windowed detection over Selective Search proposals by extracting
the crop and warping to the input dimensions of the net.
Take
image_fnames: list
Give
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts.
"""
import selective_search_ijcv_with_python as selective_search
# Make absolute paths so MATLAB can find the files.
image_fnames = [os.path.abspath(f) for f in image_fnames]
windows_list = selective_search.get_windows(image_fnames)
# Run windowed detection on the selective search list.
return self.detect_windows(zip(image_fnames, windows_list))
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