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%load_ext autoreload
%autoreload 2

import daimler
import os


    

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basepath="/media/data/datasets/pedestrian/daimler_mono/DaimlerBenchmark/"
print("Loading dataset...")
train_pedestrian,train_non_pedestrian,metadata,object_types=daimler.get_dataset(basepath)
# train_pedestrian=train_pedestrian[::2]
# train_non_pedestrian=train_non_pedestrian[::2]
print("Loaded")


    

    




Loading dataset...
Loaded

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import experiment
print("Generating hogs for pedestrians")
train_pedestrian_hogs=experiment.hogs_from_images(train_pedestrian)


    

    




Generating hogs for pedestrians
image shape (96, 48)
resulting hog size = 3240
Generating hogs for 15660 images...
  0.000000 ..
  10.000000 ..
  20.000000 ..
  30.000000 ..
  40.000000 ..
  50.000000 ..
  60.000000 ..
  70.000000 ..
  80.000000 ..
  90.000000 ..
Done

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image_size=train_pedestrian[0].shape
crop_size=image_size
crop_grid_size=(4,4)
crops_per_image=crop_grid_size[0]*crop_grid_size[1]
print("Generating %d crops of size %s per image " % (crops_per_image, str(image_size)))
train_non_pedestrian_crops=experiment.crop_images_uniform(train_non_pedestrian,crop_grid_size,crop_size)
print("Generating hogs for non pedestrians")
train_non_pedestrian_hogs=experiment.hogs_from_images(train_non_pedestrian_crops)


    

    




Generating 16 crops of size (96, 48) per image 






    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-4-2d0104ba3f7a> in <module>()
      4 crops_per_image=crop_grid_size[0]*crop_grid_size[1]
      5 print("Generating %d crops of size %s per image " % (crops_per_image, str(image_size)))
----> 6 train_non_pedestrian_crops=experiment.crop_images_uniform(train_non_pedestrian,crop_grid_size,crop_size)
      7 print("Generating hogs for non pedestrians")
      8 train_non_pedestrian_hogs=experiment.hogs_from_images(train_non_pedestrian_crops)

~/dev/sld/pedestrian_test/experiment.py in crop_images_uniform(images, crop_grid_size, crop_size)
     86     j=0
     87     for i in range(n):
---> 88         image=images[i]
     89         h,w=image.shape
     90         assert ch+gh<=h and cw+gw<=w

~/dev/sld/.env/lib/python3.6/site-packages/skimage/io/collection.py in __getitem__(self, n)
    257                         kwargs['img_num'] = img_num
    258                     try:
--> 259                         self.data[idx] = self.load_func(fname, **kwargs)
    260                     # Account for functions that do not accept an img_num kwarg
    261                     except TypeError as e:

~/dev/sld/.env/lib/python3.6/site-packages/skimage/io/_plugins/pil_plugin.py in imread(fname, dtype, img_num, **kwargs)
     34     if isinstance(fname, string_types):
     35         with open(fname, 'rb') as f:
---> 36             im = Image.open(f)
     37             return pil_to_ndarray(im, dtype=dtype, img_num=img_num)
     38     else:

~/dev/sld/.env/lib/python3.6/site-packages/PIL/Image.py in open(fp, mode)
   2537         exclusive_fp = True
   2538 
-> 2539     prefix = fp.read(16)
   2540 
   2541     preinit()

KeyboardInterrupt: 

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import h5py
print("Storing hogs..")
filepath='/media/data/models/daimler_hogs.h5'
with h5py.File(filepath, 'w') as h5f:
    h5f.create_dataset('pedestrians', data=train_pedestrian_hogs)
    h5f.create_dataset('non_pedestrians', data=train_non_pedestrian_hogs)

print("Done")


    

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