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ACT_datalayer.py
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ACT_datalayer.py
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import sys
import os
import random
import numpy as np
import cv2
from ACT_utils import iou2d
from Dataset import GetDataset
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'python'))
import caffe
distort_params = {
'brightness_prob': 0.5,
'brightness_delta': 32,
'contrast_prob': 0.5,
'contrast_lower': 0.5,
'contrast_upper': 1.5,
'hue_prob': 0.5,
'hue_delta': 18,
'saturation_prob': 0.5,
'saturation_lower': 0.5,
'saturation_upper': 1.5,
'random_order_prob': 0.0,
}
expand_params = {
'expand_prob': 0.5,
'max_expand_ratio': 4.0,
}
batch_samplers = [{
'sampler': {},
'max_trials': 1,
'max_sample': 1,
}, {
'sampler': {'min_scale': 0.3, 'max_scale': 1.0, 'min_aspect_ratio': 0.5, 'max_aspect_ratio': 2.0,},
'sample_constraint': {'min_jaccard_overlap': 0.1, },
'max_trials': 50,
'max_sample': 1,
}, {
'sampler': {'min_scale': 0.3, 'max_scale': 1.0, 'min_aspect_ratio': 0.5, 'max_aspect_ratio': 2.0,},
'sample_constraint': {'min_jaccard_overlap': 0.3,},
'max_trials': 50,
'max_sample': 1,
},{
'sampler': {'min_scale': 0.3, 'max_scale': 1.0, 'min_aspect_ratio': 0.5, 'max_aspect_ratio': 2.0,},
'sample_constraint': {'min_jaccard_overlap': 0.5,},
'max_trials': 50,
'max_sample': 1,
},{
'sampler': {'min_scale': 0.3, 'max_scale': 1.0, 'min_aspect_ratio': 0.5, 'max_aspect_ratio': 2.0,},
'sample_constraint': {'min_jaccard_overlap': 0.7,},
'max_trials': 50,
'max_sample': 1,
},{
'sampler': {'min_scale': 0.3, 'max_scale': 1.0, 'min_aspect_ratio': 0.5, 'max_aspect_ratio': 2.0,},
'sample_constraint': {'min_jaccard_overlap': 0.9,},
'max_trials': 50,
'max_sample': 1,
},{
'sampler': {'min_scale': 0.3, 'max_scale': 1.0, 'min_aspect_ratio': 0.5, 'max_aspect_ratio': 2.0,},
'sample_constraint': {'max_jaccard_overlap': 1.0,},
'max_trials': 50,
'max_sample': 1,
},]
def random_brightness(imglist, brightness_prob, brightness_delta):
if random.random() < brightness_prob:
brig = random.uniform(-brightness_delta, brightness_delta)
for i in xrange(len(imglist)):
imglist[i] += brig
return imglist
def random_contrast(imglist, contrast_prob, contrast_lower, contrast_upper):
if random.random() < contrast_prob:
cont = random.uniform(contrast_lower, contrast_upper)
for i in xrange(len(imglist)):
imglist[i] *= cont
return imglist
def random_saturation(imglist, saturation_prob, saturation_lower, saturation_upper):
if random.random() < saturation_prob:
satu = random.uniform(saturation_lower, saturation_upper)
for i in xrange(len(imglist)):
hsv = cv2.cvtColor(imglist[i], cv2.COLOR_BGR2HSV)
hsv[:, :, 1] *= satu
imglist[i] = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return imglist
def random_hue(imglist, hue_prob, hue_delta):
if random.random() < hue_prob:
hue = random.uniform(-hue_delta, hue_delta)
for i in xrange(len(imglist)):
hsv = cv2.cvtColor(imglist[i], cv2.COLOR_BGR2HSV)
hsv[:, :, 0] += hue
imglist[i] = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return imglist
def apply_distort(imglist, distort_param):
out_imglist = imglist
if distort_param['random_order_prob'] != 0: raise NotImplementedError
if random.random() > 0.5:
out_imglist = random_brightness(out_imglist, distort_param['brightness_prob'], distort_param['brightness_delta'])
out_imglist = random_contrast(out_imglist, distort_param['contrast_prob'], distort_param['contrast_lower'], distort_param['contrast_upper'])
out_imglist = random_saturation(out_imglist, distort_param['saturation_prob'], distort_param['saturation_lower'], distort_param['saturation_upper'])
out_imglist = random_hue(out_imglist, distort_param['hue_prob'], distort_param['hue_delta'])
else:
out_imglist = random_brightness(out_imglist, distort_param['brightness_prob'], distort_param['brightness_delta'])
out_imglist = random_saturation(out_imglist, distort_param['saturation_prob'], distort_param['saturation_lower'], distort_param['saturation_upper'])
out_imglist = random_hue(out_imglist, distort_param['hue_prob'], distort_param['hue_delta'])
out_imglist = random_contrast(out_imglist, distort_param['contrast_prob'], distort_param['contrast_lower'], distort_param['contrast_upper'])
return out_imglist
def apply_expand(imglist, tubes, expand_param, mean_values=None):
# Tubes: dict of label -> list of tubes with tubes being <x1> <y1> <x2> <y2>
out_imglist = imglist
out_tubes = tubes
if random.random() < expand_param['expand_prob']:
expand_ratio = random.uniform(1, expand_param['max_expand_ratio'])
oh,ow = imglist[0].shape[:2]
h = int(oh * expand_ratio)
w = int(ow * expand_ratio)
out_imglist = [np.zeros((h, w, 3), dtype=np.float32) for i in xrange(len(imglist))]
h_off = int(np.floor(h - oh))
w_off = int(np.floor(w - ow))
if mean_values is not None:
for i in xrange(len(imglist)):
out_imglist[i] += np.array(mean_values).reshape(1, 1, 3)
for i in xrange(len(imglist)):
out_imglist[i][h_off:h_off+oh, w_off:w_off+ow, :] = imglist[i]
# project boxes
for ilabel in tubes:
for itube in xrange(len(tubes[ilabel])):
out_tubes[ilabel][itube] += np.array([[w_off, h_off, w_off, h_off]], dtype=np.float32)
return out_imglist, out_tubes
def sample_cuboids(tubes, batch_samplers, imheight, imwidth):
sampled_cuboids = []
for batch_sampler in batch_samplers:
max_trials = batch_sampler['max_trials']
max_sample = batch_sampler['max_sample']
itrial = 0
isample = 0
sampler = batch_sampler['sampler']
min_scale = sampler['min_scale'] if 'min_scale' in sampler else 1
max_scale = sampler['max_scale'] if 'max_scale' in sampler else 1
min_aspect = sampler['min_aspect_ratio'] if 'min_aspect_ratio' in sampler else 1
max_aspect = sampler['max_aspect_ratio'] if 'max_aspect_ratio' in sampler else 1
while itrial < max_trials and isample < max_sample:
# sample a normalized box
scale = random.uniform(min_scale, max_scale)
aspect = random.uniform(min_aspect, max_aspect)
width = scale * np.sqrt(aspect)
height = scale / np.sqrt(aspect)
x = random.uniform(0, 1 - width)
y = random.uniform(0, 1 - height)
# rescale the box
sampled_cuboid = np.array([x*imwidth, y*imheight, (x+width)*imwidth, (y+height)*imheight], dtype=np.float32)
# check constraint
itrial += 1
if not 'sample_constraint' in batch_sampler:
sampled_cuboids.append(sampled_cuboid)
isample += 1
continue
constraints = batch_sampler['sample_constraint']
ious = np.array([np.mean(iou2d(t, sampled_cuboid)) for t in sum(tubes.values(),[])])
if ious.size == 0: # empty gt
isample += 1
continue
if 'min_jaccard_overlap' in constraints and ious.max() >= constraints['min_jaccard_overlap']:
sampled_cuboids.append( sampled_cuboid )
isample += 1
continue
if 'max_jaccard_overlap' in constraints and ious.min() >= constraints['max_jaccard_overlap']:
sampled_cuboids.append( sampled_cuboid )
isample += 1
continue
return sampled_cuboids
def crop_image(imglist, tubes, batch_samplers):
candidate_cuboids = sample_cuboids(tubes, batch_samplers, imglist[0].shape[0], imglist[0].shape[1])
if not candidate_cuboids:
return imglist, tubes
crop_cuboid = random.choice(candidate_cuboids)
x1, y1, x2, y2 = map(int, crop_cuboid.tolist())
for i in xrange(len(imglist)):
imglist[i] = imglist[i][y1:y2+1, x1:x2+1, :]
out_tubes = {}
wi = x2 - x1
hi = y2 - y1
for ilabel in tubes:
for itube in xrange(len(tubes[ilabel])):
t = tubes[ilabel][itube]
t -= np.array([[x1, y1, x1, y1]], dtype=np.float32)
# check if valid
cx = 0.5 * (t[:, 0] + t[:, 2])
cy = 0.5 * (t[:, 1] + t[:, 3])
if np.any(cx < 0) or np.any(cy < 0) or np.any(cx > wi) or np.any(cy > hi):
continue
if not ilabel in out_tubes:
out_tubes[ilabel] = []
# clip box
t[:, 0] = np.maximum(0, t[:, 0])
t[:, 1] = np.maximum(0, t[:, 1])
t[:, 2] = np.minimum(wi, t[:, 2])
t[:, 3] = np.minimum(hi, t[:, 3])
out_tubes[ilabel].append(t)
return imglist, out_tubes
# Assisting function for finding a good/bad tubelet
def tubelet_in_tube(tube, i, K):
# True if all frames from i to (i + K - 1) are inside tube
# it's sufficient to just check the first and last frame.
return (i in tube[: ,0] and i + K - 1 in tube[:, 0])
def tubelet_out_tube(tube, i, K):
# True if all frames between i and (i + K - 1) are outside of tube
return all([not j in tube[:, 0] for j in xrange(i, i + K)])
def tubelet_in_out_tubes(tube_list, i, K):
# Given a list of tubes: tube_list, return True if
# all frames from i to (i + K - 1) are either inside (tubelet_in_tube)
# or outside (tubelet_out_tube) the tubes.
return all([tubelet_in_tube(tube, i, K) or tubelet_out_tube(tube, i, K) for tube in tube_list])
def tubelet_has_gt(tube_list, i, K):
# Given a list of tubes: tube_list, return True if
# the tubelet starting spanning from [i to (i + K - 1)]
# is inside (tubelet_in_tube) at least a tube in tube_list.
return any([tubelet_in_tube(tube, i, K) for tube in tube_list])
class MultiframesLayer(caffe.Layer):
def shuffle(self): # shuffle the list of possible starting frames
self._order = range(self._nseqs)
if self._shuffle:
# set seed like that to have exactly the same shuffle even if we restart from a caffemodel
random.seed(self._rand_seed + self._nshuffles)
random.shuffle(self._order)
self._nshuffles += 1
self._next = 0
def setup(self, bottom, top):
layer_params = eval(self.param_str)
assert 'dataset_name' in layer_params
dataset_name = layer_params['dataset_name']
self._dataset = GetDataset(dataset_name)
assert 'K' in layer_params
self._K = layer_params['K']
assert self._K > 0
# parse optional argument
default_values = {
'rand_seed': 0,
'shuffle': True,
'batch_size': 32 // self._K,
'mean_values': [104, 117, 123],
'resize_height': 300,
'resize_width': 300,
'restart_iter': 0,
'flow': False,
'ninput': 1,
}
for k in default_values.keys():
if k in layer_params:
lay_param = layer_params[k]
else:
lay_param = default_values[k]
setattr(self, '_' + k, lay_param)
if not self._flow and self._ninput > 1:
raise NotImplementedError("ACT-detector: Not implemented: ninput > 1 with rgb frames")
d = self._dataset
K = self._K
# build index (v,i) of valid starting chunk
self._indices = []
for v in d.train_vlist():
vtubes = sum(d.gttubes(v).values(), [])
self._indices += [(v,i) for i in range(1, d.nframes(v)+2-K) if tubelet_in_out_tubes(vtubes,i,K) and tubelet_has_gt(vtubes,i,K)]
# self._indices += [(v,i) for i in range(1, d.nframes(v)+2-K) if all([ (i in t[:,0] and i+K-1 in t[:,0]) or all([not j in t[:,0] for j in xrange(i,i+K)]) for t in vtubes]) and any([ (i in t[:,0] and i+K-1 in t[:,0]) for t in vtubes]) ]
self._nseqs = len(self._indices)
self._iter = 0
self._nshuffles = 0
self.shuffle()
if self._restart_iter > 0:
assert self._next == 0
self._iter = self._restart_iter
iimages = self._restart_iter * self._batch_size
while iimages > self._nseqs:
self.shuffle()
iimages -= self._nseqs
self._next = iimages
for i in xrange(K):
top[i].reshape(self._batch_size, 3 * self._ninput, self._resize_height, self._resize_width)
top[K].reshape(1, 1, 1, 8)
def prepare_blob(self):
d = self._dataset
K = self._K
# Have the same data augmentation, even if restarted
random.seed(self._rand_seed + self._iter)
data = [np.empty((self._batch_size, 3 * self._ninput, self._resize_height, self._resize_width), dtype=np.float32) for ii in range(K)]
alltubes = []
for i in xrange(self._batch_size):
if self._next == self._nseqs:
self.shuffle()
v,frame = self._indices[self._order[self._next]]
# flipping with probability 0.5
do_mirror = random.getrandbits(1) == 1
# load images and tubes and apply mirror
images = []
if self._flow:
images = [cv2.imread(d.flowfile(v, min(frame+ii, d.nframes(v)))).astype(np.float32) for ii in range(K + self._ninput - 1)]
else:
images = [cv2.imread(d.imfile(v, frame+ii)).astype(np.float32) for ii in range(K)]
if do_mirror:
images = [im[:, ::-1, :] for im in images]
# reverse the x component of the flow
if self._flow:
for ii in xrange(K + self._ninput - 1):
images[ii][:, :, 2] = 255 - images[ii][:, :, 2]
h, w = d.resolution(v)
TT = {}
for ilabel, tubes in d.gttubes(v).iteritems():
for t in tubes:
if frame not in t[:, 0]:
continue
assert frame + K - 1 in t[:, 0]
if do_mirror:
# copy otherwise it will change the gt of the dataset also
t = t.copy()
xmin = w - t[:, 3]
t[:, 3] = w - t[:, 1]
t[:, 1] = xmin
boxes = t[(t[:, 0] >= frame) * (t[:, 0] < frame + K) ,1:5]
assert boxes.shape[0] == K
if ilabel not in TT:
TT[ilabel] = []
TT[ilabel].append( boxes)
# apply data augmentation
images = apply_distort(images, distort_params)
images, TT = apply_expand(images, TT, expand_params, mean_values=self._mean_values)
images, TT = crop_image(images, TT, batch_samplers)
hi,wi = images[0].shape[:2]
# resize
images = [cv2.resize(im, (self._resize_width, self._resize_height), interpolation=cv2.INTER_LINEAR) for im in images]
for ii in range(K):
for iii in xrange(self._ninput):
data[ii][i, 3*iii:3*iii + 3, :, :] = np.transpose( images[ii + iii], (2, 0, 1))
idxtube = 0
for ilabel in TT:
for itube in xrange(len(TT[ilabel])):
for b in TT[ilabel][itube]:
alltubes.append([i, ilabel+1, idxtube, b[0]/wi, b[1]/hi, b[2]/wi, b[3]/hi, 0])
idxtube += 1
self._next += 1
self._iter += 1
for ii in range(K):
data[ii] -= np.tile(np.array(self._mean_values, dtype=np.float32)[None, :, None, None], (1, self._ninput, 1, 1))
label = np.array(alltubes, dtype=np.float32)
# label shape 1x1x1x8; if no boxes, then -1
if label.size == 0:
label = -np.ones((1, 1, 1, 8), dtype=np.float32)
else:
label = label.reshape(1, 1, -1, 8)
return data + [label]
def forward(self, bottom, top):
blobs = self.prepare_blob()
for ii in xrange(len(top) - 1):
top[ii].data[...] = blobs[ii].astype(np.float32, copy=False)
top[len(top) - 1].reshape(*(blobs[len(top) - 1].shape))
top[len(top) - 1].data[...] = blobs[len(top) - 1].astype(np.float32, copy=False)
def backward(self, bottom, propagate_down, top):
pass
def reshape(self, bottom, top):
# done in the forward
pass