def datagen_adapt(X, y, cg, augment=True, batch_size=25, pool=None, cross_image=False):
listlength = X.shape[0]
inp_img = np.zeros((batch_size, ) + cg.imgdims)
ex_patch = np.zeros((batch_size, ) + cg.patchdims)
output_map = np.zeros((batch_size, ) + cg.outputdims)
while True:
sample = np.random.choice(listlength, batch_size)
imglist = np.array([os.path.join(cg.datapath, X[t]) for t in sample])
lblist = np.array([os.path.join(cg.datapath, y[t]) for t in sample])
imgs = ut.multiprocess_fn(pool, ut.load_data, imglist, [cg.imgdims,cg.pad])
lbs = ut.multiprocess_fn(pool, ut.load_dotlabel, lblist, [cg.imgdims,cg.pad])
# Sample exemplar patch from full image and augment.
results = ut.multiprocess_fn(pool, ut.sample_exemplar, list(zip(imgs, lbs)), [cg.patchdims, augment])
ex_patch, output_map = list(zip(*results))
if cross_image:
if random.random() < 0.9:
# shuffle patches so that they can be sampled from different images
ex_patch = list(ex_patch)
random.shuffle(ex_patch)
inp_img = preprocess_input(np.array(imgs, dtype='float32'))
ex_patch = preprocess_input(np.array(ex_patch, dtype='float32'))
output_map = np.array(output_map)
output_map = np.expand_dims(output_map, -1)
inputs = {'image_patch': ex_patch,
'image': inp_img,
}
outputs = {'output': output_map}
yield (inputs, outputs)
def datagen_gmn(X, y, cg, augment=True, batch_size=32, pool=None):
'''
Data has been preprocessed following the precedure:
https://github.com/bertinetto/siamese-fc/tree/master/ILSVRC15-curation
Process ImageNet video data for training the GMN.
Args:
X: numpy array with 1 entry per class (30 total for Imagenet video)
each entry is a list of tuples that represent a single object in a video
each tuple looks like: ( video_dir_path, object_id, [valid frame numbers] )
'''
listlength = X.shape[0]
inp_img = np.zeros((batch_size, ) + cg.imgdims)
ex_patch = np.zeros((batch_size, ) + cg.patchdims)
output_map = np.zeros((batch_size, ) + cg.outputdims)
# generate positive sample heatmap
positive = np.zeros(cg.outputdims)
positive[cg.outputdims[0] // 2, cg.outputdims[1] // 2, 0] = 1
positive[:, :, 0] = 100 * scipy.ndimage.gaussian_filter(
positive[:, :, 0], sigma=(2, 2), mode='constant')
while True:
# select random classes
sample = np.random.choice(listlength, batch_size)
inp_img_paths = np.empty(batch_size, dtype='S120')
ex_patch_paths = np.empty(batch_size, dtype='S120')
output_map = np.zeros((batch_size, ) + cg.outputdims)
for k, class_i in enumerate(sample):
# first select a random (vid,obj) pair from each class
obj = np.random.choice(X[class_i])
if random.random() < 0.5:
# positive pair
# input image and patch are same object
inp_obj = obj
patch_obj = obj
# choose 2 frames at most 100 frames apart
start = np.random.randint(max(1, len(obj[2]) - 100))
inp_frame, ex_frame = np.random.choice(
obj[2][start:start + 100], 2)
# update output heatmap
output_map[k] = positive
else:
# sample negative pair
# input image and patch are different objects
inp_obj = obj
# choose random other class
class_other = np.random.choice(
np.concatenate(
(np.arange(class_i), np.arange(class_i + 1,
listlength))))
# random object from other class
patch_obj = np.random.choice(X[class_other])
# random frame for each object
inp_frame = np.random.choice(inp_obj[2])
ex_frame = np.random.choice(patch_obj[2])
inp_img_paths[k] = os.path.join(
cg.datapath, '%s/%06d.%02d.crop.x.jpg' %
(inp_obj[0], inp_frame, inp_obj[1]))
if augment:
# need to read full image for later augmentation
ex_patch_paths[k] = os.path.join(
cg.datapath, '%s/%06d.%02d.crop.x.jpg' %
(patch_obj[0], ex_frame, patch_obj[1]))
else:
# read patch directly
ex_patch_paths[k] = os.path.join(
cg.datapath, '%s/%06d.%02d.crop.z.jpg' %
(patch_obj[0], ex_frame, patch_obj[1]))
inp_img = ut.multiprocess_fn(pool, ut.load_data, inp_img_paths, [
cg.imgdims,
cg.pad,
])
if augment:
# read full image, augment, crop patch
ex_patch = ut.multiprocess_fn(pool, ut.load_data, ex_patch_paths,
[cg.imgdims, cg.pad])
ex_patch = ut.multiprocess_fn(pool, ut.augment_data, ex_patch)
ex_patch = np.array(ex_patch)
# crop center patch from full image
ex_patch = ex_patch[:, cg.patch_start:cg.patch_end,
cg.patch_start:cg.patch_end, :]
else:
ex_patch = ut.multiprocess_fn(pool, ut.load_data, ex_patch_paths,
[cg.patchdims])
inp_img = preprocess_input(np.array(inp_img, dtype='float32'))
ex_patch = preprocess_input(np.array(ex_patch, dtype='float32'))
inputs = {
'image_patch': ex_patch,
'image': inp_img,
}
outputs = {'output': output_map}
yield (inputs, outputs)