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https://github.com/opencv/opencv_contrib.git
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41a5a5eaf5
GSoC'17 Learning compact models for object detection (#1253) * Final solver and model for SqueezeNet model * update README * update dependencies and CMakeLists * add global pooling * Add training scripts * fix typo * fix dependency of caffe * fix whitespace * Add squeezedet architecture * Pascal pre process script * Adding pre process scripts * Generate the graph of the model * more readable * fix some bugs in the graph * Post process class implementation * Complete minimal post processing and standalone running * Complete the base class * remove c++11 features and fix bugs * Complete example * fix bugs * Adding final scripts * Classification scripts * Update README.md * Add example code and results * Update README.md * Re-order and fix some bugs * fix build failure * Document classes and functions * Add instructions on how to use samples * update instructionos * fix docs failure * fix conversion types * fix type conversion warning * Change examples to sample directoryu * restructure directories * add more references * fix whitespace * retain aspect ratio * Add more examples * fix docs warnings * update with links to trained weights * threshold update * png -> jpg * fix tutorial * model files * precomp.hpp , fix readme links, module dependencies * copyrights - no copyright in samples - use new style OpenCV copyright header - precomp.hpp
48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
from skimage import io, transform
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from multiprocessing.dummy import Pool as ThreadPool
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def rescale(root_new, root_old, img_path, ann_path, out_shape):
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try:
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img = io.imread(root_old+"/"+img_path)
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except Exception as E:
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print E
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h, w, _ = img.shape
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f_h, f_w = float(out_shape)/h, float(out_shape)/w
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trans_img = transform.rescale(img, (f_h, f_w))
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num_objs = 0
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with open(root_old+"/"+ann_path, 'r') as f:
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ann = f.readline()
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ann = ann.rstrip()
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ann = ann.split(' ')
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ann = [float(i) for i in ann]
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num_objs = len(ann) / 5
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for idx in xrange(num_objs):
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ann[idx * 5 + 0] = int(f_w * ann[idx * 5 + 0])
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ann[idx * 5 + 1] = int(f_h * ann[idx * 5 + 1])
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ann[idx * 5 + 2] = int(f_w * ann[idx * 5 + 2])
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ann[idx * 5 + 3] = int(f_h * ann[idx * 5 + 3])
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# Write the new annotations to file
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with open(root_new+"/"+ann_path, 'w') as f_new:
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for val in ann:
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f_new.write(str(val)+' ')
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# Save the new image
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io.imwrite(root_new+"/"+img_path, trans_img)
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def preprocess():
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source = '/users2/Datasets/PASCAL_VOC/VOCdevkit/VOC2012_Resize/source.txt'
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root_old = '/users2/Datasets/PASCAL_VOC/VOCdevkit/VOC2012'
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root_new = '/users2/Datasets/PASCAL_VOC/VOCdevkit/VOC2012_Resize'
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out_shape = 416
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with open(source, 'r') as src:
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lines = src.readlines()
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print 'Processing {} images and annotations'.format(len(lines))
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for line in lines:
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line = line.rstrip()
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line = line.split(' ')
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img_path = line[0]
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ann_path = line[1]
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rescale(root_new, root_old, img_path, ann_path, out_shape)
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if __name__ == '__main__':
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preprocess()
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