def create_patches_at_center(img_basenames, annotation_dir, image_dir, size=50, step=40, grayscale=True, progressbar=True,downsample=1, objectclass=None, negative_discard_rate=.9):
'''Extract a set of image patches with labels, from the supplied list of
annotated images. Positive-labelled patches are extracted centered on the
annotated bounding box; negative-lablled patches are extracted at random
from any part of the image which does not overlap an annotated bounding box.'''
if progressbar:
pb = ProgressBar(len(img_basenames))
if not annotation_dir[-1] == os.path.sep:
annotation_dir = annotation_dir + os.path.sep
if not image_dir[-1] == os.path.sep:
image_dir = image_dir + os.path.sep
color_type = 0
if grayscale:
channels=1
else:
channels =3
pos = []
neg = []
s = 1
for img_filename in img_basenames:
if progressbar:
pb.step(s)
s +=1
annotation_filename = annotation_dir + img_filename[:-3] + 'xml'
boundingboxes = get_bounding_boxes_for_single_image(annotation_filename, objectclass)
colortype = cv2.IMREAD_COLOR
img = cv2.imread(image_dir + img_filename, colortype)
height,width,channels=img.shape
img = img.reshape((height, width,channels))
img = np.rollaxis(img,2)
image_pos = get_image_positives(img,boundingboxes,size,downsample=downsample)
pos.append(image_pos)
image_neg = get_image_negatives(img,boundingboxes,size,step,downsample=downsample,discard_rate=negative_discard_rate)
neg.append(image_neg)
pos = [item for sublist in pos for item in sublist]
neg = [item for sublist in neg for item in sublist]
patches = pos+neg
index = np.arange(len(patches))
np.random.seed(0)
np.random.shuffle(index)
np_patches = np.empty((len(patches),channels,size/downsample,size/downsample),dtype=np.uint8)
np_labels = np.empty(len(patches),dtype=int)
max_pos=len(pos)
for i,j in zip(index,xrange(len(index))):
if i < max_pos:
np_patches[j,] = pos[i]
np_labels[j] = 1
else:
np_patches[j,] = neg[i-max_pos]
np_labels[j] = 0
np_labels = np_labels.astype(np.uint8)
return np_labels,np_patches
def create_patches(img_basenames,
annotation_dir,
image_dir,
size,
step,
grayscale=True,
progressbar=True,
downsample=1,
objectclass=None,
negative_discard_rate=.9):
'''Extract a set of image patches with labels, from the supplied list of
annotated images. Positive-labelled patches are extracted centered on the
annotated bounding box; negative-labelled patches are extracted at random
from any part of the image which does not overlap an annotated bounding box.'''
if progressbar:
pb = ProgressBar(len(img_basenames))
if not annotation_dir[-1] == os.path.sep:
annotation_dir = annotation_dir + os.path.sep
if not image_dir[-1] == os.path.sep:
image_dir = image_dir + os.path.sep
color_type = 0
if grayscale:
channels = 1
else:
channels = 3
pos = []
neg = []
s = 1
for img_filename in img_basenames:
if progressbar:
pb.step(s)
s += 1
annotation_filename = annotation_dir + img_filename[:-3] + 'xml'
boundingboxes = get_bounding_boxes_for_single_image(
annotation_filename, objectclass)
#colortype = cv2.IMREAD_COLOR
#img = cv2.imread(image_dir + img_filename, colortype)
img = misc.imread(image_dir + img_filename)
height, width, channels = img.shape
img = img.reshape((height, width, channels))
img = np.rollaxis(img, 2)
image_pos = get_image_positives(img,
boundingboxes,
size,
downsample=downsample)
pos.append(image_pos)
image_neg = get_image_negatives(img,
boundingboxes,
size,
step,
downsample=downsample,
discard_rate=negative_discard_rate)
neg.append(image_neg)
pos = [item for sublist in pos for item in sublist]
neg = [item for sublist in neg for item in sublist]
patches = pos + neg
index = np.arange(len(patches))
np.random.seed(0)
np.random.shuffle(index)
np_patches = np.empty(
(len(patches), channels, size / downsample, size / downsample),
dtype=np.uint8)
np_labels = np.empty(len(patches), dtype=int)
max_pos = len(pos)
for i, j in zip(index, range(len(index))):
if i < max_pos:
np_patches[j, ] = pos[i]
np_labels[j] = 1
else:
np_patches[j, ] = neg[i - max_pos]
np_labels[j] = 0
np_labels = np_labels.astype(np.uint8)
return np_labels, np_patches
def create_patches_at_center(img_basenames, annotation_dir, img_dir, size=50, step=40, grayscale=True, progressbar=True):
if progressbar:
pb = ProgressBar(len(img_basenames))
if not annotation_dir[-1] == os.path.sep:
annotation_dir = annotation_dir + os.path.sep
if not img_dir[-1] == os.path.sep:
img_dir = img_dir + os.path.sep
color_type = 0
if grayscale:
channels=1
else:
channels =3
pos = []
neg = []
s = 1
for img_filename in img_basenames:
if progressbar:
pb.step(s)
s +=1
annotation_filename = annotation_dir + img_filename[:-3] + 'xml'
boundingboxes = get_bounding_boxes_for_single_image(annotation_filename)
#print '%d objects in %s' % (len(boundingboxes), img_filename)
colortype = cv2.IMREAD_COLOR
if grayscale:
colortype = cv2.IMREAD_GRAYSCALE
img = cv2.imread(img_dir + img_filename, colortype)
height,width=img.shape
img = img.reshape((height, width,channels))
img = np.rollaxis(img,2)
image_pos = get_image_positives(img,boundingboxes, size)
pos.append(image_pos)
image_neg = get_image_negatives(img, boundingboxes,size,step)
neg.append(image_neg)
else:
img = cv2.imread(img_dir + img_filename, colortype)
height,width,channels=img.shape
img = img.reshape((height, width,channels))
img = np.rollaxis(img,2)
image_pos = get_image_positives(img,boundingboxes, size)
pos.append(image_pos)
#print len(pos)
image_neg = get_image_negatives(img, boundingboxes,size,step)
neg.append(image_neg)
#print len(neg)
pos = [item for sublist in pos for item in sublist]
print len(pos)
neg = [item for sublist in neg for item in sublist]
print len(neg)
patches = pos+neg
print len(patches)
index = np.arange(len(patches))
np.random.seed(0)
np.random.shuffle(index)
np_patches = np.empty((len(patches),channels,size,size),dtype=np.uint8)
np_labels = np.empty(len(patches),dtype=int)
max_pos=len(pos)
for i,j in zip(index,xrange(len(index))):
if i < max_pos:
np_patches[j,] = pos[i]
np_labels[j] = 1
else:
np_patches[j,] = neg[i-max_pos]
np_labels[j] =0
#print '\nSplit each of ' + str(N) + ' images in ' + str(len(patches)) + \
#' ' + str(size) + 'x' + str(size) + ' patches'
#print 'Total number of patches: ' + str(N*(len(patches)))
#return np.array(np_labels).flatten(), np_patches
return np_labels,np_patches
