def load_image_file(filename):
if not isinstance(filename, str):
return Error(2, 'Filename should be a string', 0), None
fd = filename.split('.')[-1]
if fd == 'npy':
try:
img = np.load(filename)
return None, img
except Exception as e:
return Error(1, "Error in loading image file", str(e)), None
if fd in supported_file_types:
try:
img = scipy.imread(filename)
return None, img
except Exception as e:
return Error(1, "Error in loading image file", str(e)), None
else:
try:
img = pickle_load(filename)
return None, img
except Exception as e:
return Error(1, "Error in loading image file", str(e)), None
def make_cub_anno_files(config):
#correct them annotation files for the CUB 2011
print("Making cub annotation files...\n")
CUB_DIR = config.cub_root
out_dir = os.path.dirname(config.train_image_fname)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
done_fname = os.path.join(out_dir, 'cub.done')
#Keypoints, bounding boxes, paths, classes, split for every images
im_part_fname = os.path.join(CUB_DIR, 'parts', 'part_locs.txt')
im_bbox_fname = os.path.join(CUB_DIR, 'bounding_boxes.txt')
im_path_fname = os.path.join(CUB_DIR, 'images.txt')
im_split_fname = os.path.join(CUB_DIR, 'train_test_split.txt')
im_label_fname = os.path.join(CUB_DIR, 'image_class_labels.txt')
path_data = open(im_path_fname, 'r').read().split('\n')[:-1]
path_data = [im.split(' ') for im in path_data]
im_ids = [int(im[0]) for im in path_data]
rel_paths = [str(im[1]) for im in path_data]
images = []
for i in range(len(im_ids)): #might half it depending on time
image_id = im_ids[i]
rel_path = rel_paths[i]
images[image_id] = {}
images[image_id]['id'] = image_id
images[image_id]['rel_path'] = rel_path
#image sizes
all_ims = []
for i in range(len(im_ids)):
if i % 256 == 0:
print('im size {}/{}'.format(i, len(im_ids)))
im_path = os.path.join(CUB_DIR, 'images', images[i]['rel_path'])
im = sp.imread(im_path)
if np.size(im, axis=2) == 1:
im = np.stack([im, im, im], axis=2)
all_ims.append(im)
images[i]['width'] = np.size(im, axis=1)
images[i]['height'] = np.size(im, axis=0)
#Splits
split_data = open(im_split_fname, 'r').read().split('\n')[:-1]
split_data = [im.split(' ') for im in split_data]
im_ids = [int(im[0]) for im in split_data]
is_training = [int(im[1]) for im in split_data]
for i in range(len(im_ids)):
images[im_ids[i]]['train'] = is_training[i] == 1
images[im_ids[i]]['test'] = is_training[i] == 0
#classes
class_data = open(im_label_fname, 'r').read().split('\n')[:-1]
class_data = [im.split(' ') for im in class_data]
im_ids = [int(im[0]) for im in class_data]
labels = [int(im[1]) for im in class_data]
for i in range(len(im_ids)):
images[im_ids[i]]['class'] = labels[i]
#bounding_boxes
bbox_data = open(im_bbox_fname, 'r').read().split('\n')[:-1]
#possible chance to make this more efficient
bbox_data = [im.split(' ') for im in bbox_data]
im_ids = [int(im[0]) for im in bbox_data]
xs = [float(im[1]) for im in bbox_data]
ys = [float(im[2]) for im in bbox_data]
widths = [float(im[3]) for im in bbox_data]
heights = [float(im[4]) for im in bbox_data]
for i in range(len(im_ids)):
bbx = Bbox()
bbx.x1 = int(xs[i])
bbx.y1 = int(ys[i])
bbx.x2 = int(x1 + widths[i] - 1)
bbx.y2 = int(y1 + heights[i] - 1)
images[im_ids]['bbox'] = bbx
im_save_fname = config.image_fname
with open(im_save_fname, 'wb') as f:
pickle.dump(images, f)
f.close()
all_images = images
#Make testing, training fileparts
print('Saving ...')
images = [i for i in all_images if i['test'] == True]
with open(config.test_image_fname, 'wb') as f:
pickle.dump(images, f)
f.close()
images = [i for i in all_images if i['train'] == True]
with open(config.train_image_fname, 'wb') as f:
pickle.dump(images, f)
f.close()
#Save files including images
images = [i for i in all_images if i['test'] == True]
ims = [
all_ims[i] for i in range(len(all_images))
if all_images[i]['test'] == True
]
with open(config.test_imagedata_fname, 'wb') as f:
pickle.dump(images, f)
pickle.dump(ims, f)
f.close()
images = [i for i in all_images if i['train'] == True]
ims = [
all_ims[i] for i in range(len(all_images))
if all_images[i]['train'] == True
]
with open(config.train_imagedata_fname, 'wb') as f:
pickle.dump(images, f)
pickle.dump(ims, f)
f.close()
print('done!')
import matplotlib.pyplot as plt
import scipy
import numpy
bug = scipy.imread('stinkbug1.png')
# if you want to inspect the shape of the loaded image
# uncomment the following line
# print bug.shape
# the original image is RBG having values for all three
# channels separately. For simplicity, we convert that to greyscale image
# by picking up just one channel
# convert to gray
bug = bug[:,:,0]
plt.figure()
plt.gray()
plt.subplot(121)
plt.imshow(bug)
# show 'zoomed' region
zbug = bug[100:350, 140:350]
plt.subplot(122)
plt.imshow(zbug)
plt.show()