def __init__(self,
data_dir,
data_type,
input_dir,
positive_output_dir,
negative_output_dir,
debug=False):
self.coco_utils = CocoUtils(data_dir, data_type)
self.images_dir = '%s/annotations/%s/' % (data_dir, input_dir)
self.window_size = input_pic_size
self.max_object_size = max_centered_object_dimension
self.debug = debug
self.positive_output_dir = positive_output_dir
self.negative_output_dir = negative_output_dir
self.empty_mask = Image.new('L', (input_pic_size, input_pic_size),
mask_pic_true_color)
def __init__(self, data_dir, data_type, input_dir, positive_output_dir, negative_output_dir, debug=False):
self.coco_utils = CocoUtils(data_dir, data_type)
self.images_dir = "%s/annotations/%s/" % (data_dir, input_dir)
self.window_size = input_pic_size
self.max_object_size = max_centered_object_dimension
self.debug = debug
self.positive_output_dir = positive_output_dir
self.negative_output_dir = negative_output_dir
self.empty_mask = Image.new("L", (input_pic_size, input_pic_size), mask_pic_true_color)
class ExamplesGenerator(object):
def __init__(self, data_dir, data_type, input_dir, positive_output_dir, negative_output_dir, debug=False):
self.coco_utils = CocoUtils(data_dir, data_type)
# ../annotations/images_train
# ../annotations/images_val
self.images_dir = '%s/annotations/%s/' % (data_dir, input_dir)
self.window_size = input_pic_size
self.max_object_size = max_centered_object_dimension
self.debug = debug
self.positive_output_dir = positive_output_dir
self.negative_output_dir = negative_output_dir
self.empty_mask = Image.new('L', (input_pic_size, input_pic_size), mask_pic_true_color)
def generate_examples(self, examples_to_generate=None):
stats = ExampleGeneratorStats()
image_ids_and_names = self.coco_utils.get_images_data()
for pic_data in image_ids_and_names:
pic_id = pic_data['id']
pic_path = self.images_dir + pic_data['file_name']
if not os.path.isfile(pic_path):
stats.img_not_found += 1
if self.debug:
print 'image %d does not exist' % pic_id
continue # img does not exist
stats.img_exists += 1
annotations = self.coco_utils.get_img_annotations(pic_id)
if not self.coco_utils.are_legal_anotations(annotations):
if self.debug:
print 'illegal annotations for picture %s' % pic_id
stats.img_with_illegal_annotations += 1
continue
stats.img_with_legal_annotations += 1
pic_patch = Patch(x_min=0, y_min=0, height=pic_data['height'], width=pic_data['width'])
im_arr = io.imread(pic_path)
canonical_seg_patches = []
for segmentation in annotations:
new_can_patch = self.create_positive_examples_from_segmentation(segmentation, pic_id,
pic_patch, im_arr, stats)
if new_can_patch is not None:
canonical_seg_patches.append(new_can_patch)
if not len(canonical_seg_patches) == 0:
negatives = self.create_negative_examples_from_picture(canonical_seg_patches, pic_id, pic_patch, im_arr,
stats)
stats.negatives_generated += negatives
if (examples_to_generate is not None
and examples_to_generate <= stats.positives_generated + stats.negatives_generated):
print 'Generated enough examples- stopping'
break
return stats
def create_positive_examples_from_segmentation(self, segmentation, pic_id, pic_patch, im_arr, stats):
seg_id = segmentation['id']
# bbs - [x y w h]
bbox = segmentation['bbox']
bbox_patch = Patch(x_min=bbox[0], width=bbox[2], y_min=bbox[1], height=bbox[3])
[seg_width, seg_height] = bbox_patch.size()
if self.segment_size_not_right(seg_width, seg_height, seg_id, pic_id, stats):
return None
[seg_center_x, seg_center_y] = bbox_patch.center()
seg_patch = Patch(x_min=seg_center_x - self.window_size / 2, y_min=seg_center_y - self.window_size / 2,
height=self.window_size, width=self.window_size)
if self.patch_exceeds_pic(seg_patch, pic_patch):
if self.debug:
print 'segment %d in picture %d cannot be centered (too close to the edges)' % (seg_id, pic_id)
stats.seg_too_close_to_edges += 1
return None
[pic_width, pic_height] = pic_patch.size()
seg_im = self.coco_utils.get_annotation_image(segmentation, pic_width, pic_height)
positives = self.create_positive_canonical_and_noisy_examples_from_mask(im_arr, seg_im, seg_patch, pic_patch,
bbox_patch, pic_id, seg_id, stats)
stats.seg_success += 1
stats.positives_generated += positives
return seg_patch
def create_positive_canonical_and_noisy_examples_from_mask(self, im_arr, full_seg_im, orig_seg_patch, pic_patch,
bbox_patch, pic_id, seg_id, stats):
created_examples = 0
offsets = [-translation_shift, 0, translation_shift]
scales = [pow(2.0, scale_deformation), 1, pow(2.0, -scale_deformation)]
[orig_patch_center_x, orig_patch_center_y] = orig_seg_patch.center()
[orig_patch_width, orig_patch_height] = orig_seg_patch.size()
for scale_i in range(len(scales)):
for x_offset_i in range(len(offsets)):
for y_offset_i in range(len(offsets)):
new_patch_width = orig_patch_width * scales[scale_i]
new_patch_height = orig_patch_height * scales[scale_i]
new_patch_min_x = orig_patch_center_x - new_patch_width / 2 + offsets[x_offset_i]
new_patch_min_y = orig_patch_center_y - new_patch_height / 2 + offsets[y_offset_i]
new_patch = Patch(new_patch_min_x, new_patch_width, new_patch_min_y, new_patch_height)
if self.patch_exceeds_pic(new_patch, pic_patch):
stats.pos_noisy_seg_too_close_to_edges += 1
continue
if self.patch_exceeds_seg(new_patch, bbox_patch):
# this will not happen with the default constants (input size, max object dimension)
stats.pos_noisy_seg_cuts_seg += 1
continue
img_path = self.create_path(self.positive_output_dir, 'pos', 'im', pic_id, seg_id, x_offset_i,
y_offset_i, scale_i)
patch_im = self.create_and_save_image_patch(im_arr, new_patch, img_path)
mask_path = self.create_path(self.positive_output_dir, 'pos', 'mask', pic_id, seg_id,
x_offset_i, y_offset_i, scale_i)
patch_seg_im = self.create_and_save_mask(full_seg_im, new_patch, mask_path)
self.create_and_save_mirror(self.positive_output_dir, 'pos', patch_seg_im, patch_im, pic_id,
seg_id, x_offset_i, y_offset_i, scale_i)
created_examples += 2 # example and mirror
return created_examples
def create_and_save_mirror(self, base_dir, ex_type, mask, im_patch, pic_id, seg_id, x_offset, y_offset, scale):
mir_im = ImageOps.mirror(im_patch)
mir_im.save(self.create_path(base_dir, ex_type, 'mir-im', pic_id, seg_id, x_offset, y_offset, scale))
mir_mask = ImageOps.mirror(mask)
mir_mask.save(self.create_path(base_dir, ex_type, 'mir-mask', pic_id, seg_id, x_offset, y_offset, scale))
def create_path(self, base_dir, ex_type, im_type, pic_id, seg_id, offset_x, offset_y, scale):
return str('%s/%s-%d-%d-%d-%d-%d-%s.png' % (base_dir, ex_type, pic_id, seg_id, offset_x, offset_y,
scale, im_type))
def patch_exceeds_pic(self, seg_patch, pic_patch):
return not pic_patch.contains(seg_patch)
def patch_exceeds_seg(self, seg_patch, bbox_patch):
return not seg_patch.contains(bbox_patch)
def segment_size_not_right(self, seg_width, seg_height, seg_id, pic_id, stats):
max_dim = max(seg_height, seg_width)
if max_dim > self.max_object_size:
if self.debug:
print 'segment %d in picture %d is too big' % (seg_id, pic_id)
stats.seg_too_big += 1
return True
if max_dim < self.max_object_size:
if self.debug:
print 'segment %d in picture %d is too small' % (seg_id, pic_id)
stats.seg_too_small += 1
return True
return False
def create_and_save_image_patch(self, im_arr, new_patch, img_path):
new_patch_x_max = new_patch.x_min + new_patch.width # not inclusive
new_patch_y_max = new_patch.y_min + new_patch.height
patch_im_arr = im_arr[new_patch.y_min:new_patch_y_max, new_patch.x_min:new_patch_x_max]
patch_im = Image.fromarray(patch_im_arr)
patch_im = patch_im.resize((self.window_size, self.window_size), Image.ANTIALIAS)
patch_im.save(img_path)
return patch_im
def create_and_save_mask(self, full_seg_im, new_patch, mask_path):
new_patch_x_max = new_patch.x_min + new_patch.width # not inclusive
new_patch_y_max = new_patch.y_min + new_patch.height
patch_seg_im = full_seg_im.crop((new_patch.x_min, new_patch.y_min, new_patch_x_max, new_patch_y_max))
patch_seg_im = patch_seg_im.resize((self.window_size, self.window_size), Image.ANTIALIAS)
patch_seg_im.save(mask_path)
return patch_seg_im
def create_negative_examples_from_picture(self, canonical_seg_patches, pic_id, pic_patch, im_arr, stats):
curr_ex_id = 0
examples_generated = 0
can_seg_patch_centers = [seg.center() for seg in canonical_seg_patches]
# including neutral translation to allow one dimension noise
offsets = [-negative_ex_min_offset * 1.5, -negative_ex_min_offset, 0,
negative_ex_min_offset, negative_ex_min_offset * 1.5]
scales = [pow(2, -negative_ex_min_scale-0.5), pow(2, -negative_ex_min_scale),
pow(2, negative_ex_min_scale), pow(2, negative_ex_min_scale+0.5)]
for can_seg_patch in canonical_seg_patches:
[can_patch_center_x, can_patch_center_y] = can_seg_patch.center()
for xoi, x_offset in enumerate(offsets):
for yoi, y_offset in enumerate(offsets):
if x_offset == 0 and y_offset == 0:
continue # no alteration
neg_patch = Patch(x_min=can_seg_patch.x_min + x_offset,
y_min=can_seg_patch.y_min + y_offset,
width=can_seg_patch.width, height=can_seg_patch.height)
if self.is_close_to_other_patches(neg_patch.center(), can_seg_patch_centers):
stats.neg_seg_too_close_to_other_segs += 1
continue
if self.patch_exceeds_pic(neg_patch, pic_patch):
stats.neg_seg_too_close_to_edges += 1
continue
neg_path = self.create_path(self.negative_output_dir, 'neg', 'im', pic_id, curr_ex_id,
xoi, yoi, 0)
im_patch = self.create_and_save_image_patch(im_arr, neg_patch, neg_path)
mask_path = self.create_path(self.negative_output_dir, 'neg', 'mask', pic_id, curr_ex_id,
xoi, yoi, 0)
self.empty_mask.save(mask_path)
self.create_and_save_mirror(self.negative_output_dir, 'neg', self.empty_mask, im_patch,
pic_id, curr_ex_id, xoi, yoi, 0)
curr_ex_id += 1
examples_generated += 2 # pic + mirror
# scale with aspect ratio kept- otherwise we might scale the lesser dimension, and the mask is legal
for scale_i, scale in enumerate(scales):
neg_patch_width = can_seg_patch.width * scale
neg_patch_height = can_seg_patch.height * scale
neg_patch_min_x = can_patch_center_x - neg_patch_width / 2
neg_patch_min_y = can_patch_center_y - neg_patch_height / 2
neg_patch = Patch(x_min=neg_patch_min_x, width=neg_patch_width,
y_min=neg_patch_min_y, height=neg_patch_height)
# if we only scale, we do allow the center to be close to another seg
if self.patch_exceeds_pic(neg_patch, pic_patch):
stats.neg_seg_too_close_to_edges += 1
continue
neg_path = self.create_path(self.negative_output_dir, 'neg', 'im', pic_id, curr_ex_id, 0, 0, scale_i)
im_patch = self.create_and_save_image_patch(im_arr, neg_patch, neg_path)
mask_path = self.create_path(self.negative_output_dir, 'neg', 'mask', pic_id, curr_ex_id, 0, 0, scale_i)
self.empty_mask.save(mask_path)
self.create_and_save_mirror(self.negative_output_dir, 'neg', self.empty_mask, im_patch,
pic_id, curr_ex_id, 0, 0, scale_i)
curr_ex_id += 1
examples_generated += 2 # pic + mirror
return examples_generated
def is_close_to_other_patches(self, seg_center, other_seg_centers):
# check seg_center x and y distance from the other centers.
# each pair must have a minimal distance in at least on dimension
for other_seg_center in other_seg_centers:
if(abs(seg_center[0] - other_seg_center[0]) < negative_ex_min_offset
and abs(seg_center[1] - other_seg_center[1]) < negative_ex_min_offset):
return True
return False
class ExamplesGenerator(object):
def __init__(self,
data_dir,
data_type,
input_dir,
positive_output_dir,
negative_output_dir,
debug=False):
self.coco_utils = CocoUtils(data_dir, data_type)
self.images_dir = '%s/annotations/%s/' % (data_dir, input_dir)
self.window_size = input_pic_size
self.max_object_size = max_centered_object_dimension
self.debug = debug
self.positive_output_dir = positive_output_dir
self.negative_output_dir = negative_output_dir
self.empty_mask = Image.new('L', (input_pic_size, input_pic_size),
mask_pic_true_color)
def generate_examples(self, examples_to_generate=None):
stats = ExampleGeneratorStats()
image_ids_and_names = self.coco_utils.get_images_data()
for pic_data in image_ids_and_names:
pic_id = pic_data['id']
pic_path = self.images_dir + pic_data['file_name']
if not os.path.isfile(pic_path):
stats.img_not_found += 1
if self.debug:
print 'image %d does not exist' % pic_id
continue # img does not exist
stats.img_exists += 1
annotations = self.coco_utils.get_img_annotations(pic_id)
if not self.coco_utils.are_legal_anotations(annotations):
if self.debug:
print 'illegal annotations for picture %s' % pic_id
stats.img_with_illegal_annotations += 1
continue
stats.img_with_legal_annotations += 1
pic_patch = Patch(x_min=0,
y_min=0,
height=pic_data['height'],
width=pic_data['width'])
im_arr = io.imread(pic_path)
canonical_seg_patches = []
for segmentation in annotations:
new_can_patch = self.create_positive_examples_from_segmentation(
segmentation, pic_id, pic_patch, im_arr, stats)
if new_can_patch is not None:
canonical_seg_patches.append(new_can_patch)
if not len(canonical_seg_patches) == 0:
negatives = self.create_negative_examples_from_picture(
canonical_seg_patches, pic_id, pic_patch, im_arr, stats)
stats.negatives_generated += negatives
if (examples_to_generate is not None and examples_to_generate <=
stats.positives_generated + stats.negatives_generated):
print 'Generated enough examples- stopping'
break
return stats
def create_positive_examples_from_segmentation(self, segmentation, pic_id,
pic_patch, im_arr, stats):
seg_id = segmentation['id']
# bbs - [x y w h]
bbox = segmentation['bbox']
bbox_patch = Patch(x_min=bbox[0],
width=bbox[2],
y_min=bbox[1],
height=bbox[3])
[seg_width, seg_height] = bbox_patch.size()
if self.segment_size_not_right(seg_width, seg_height, seg_id, pic_id,
stats):
return None
[seg_center_x, seg_center_y] = bbox_patch.center()
seg_patch = Patch(x_min=seg_center_x - self.window_size / 2,
y_min=seg_center_y - self.window_size / 2,
height=self.window_size,
width=self.window_size)
if self.patch_exceeds_pic(seg_patch, pic_patch):
if self.debug:
print 'segment %d in picture %d cannot be centered (too close to the edges)' % (
seg_id, pic_id)
stats.seg_too_close_to_edges += 1
return None
[pic_width, pic_height] = pic_patch.size()
seg_im = self.coco_utils.get_annotation_image(segmentation, pic_width,
pic_height)
positives = self.create_positive_canonical_and_noisy_examples_from_mask(
im_arr, seg_im, seg_patch, pic_patch, bbox_patch, pic_id, seg_id,
stats)
stats.seg_success += 1
stats.positives_generated += positives
return seg_patch
def create_positive_canonical_and_noisy_examples_from_mask(
self, im_arr, full_seg_im, orig_seg_patch, pic_patch, bbox_patch,
pic_id, seg_id, stats):
created_examples = 0
offsets = [-translation_shift, 0, translation_shift]
scales = [pow(2.0, scale_deformation), 1, pow(2.0, -scale_deformation)]
[orig_patch_center_x, orig_patch_center_y] = orig_seg_patch.center()
[orig_patch_width, orig_patch_height] = orig_seg_patch.size()
for scale_i in range(len(scales)):
for x_offset_i in range(len(offsets)):
for y_offset_i in range(len(offsets)):
new_patch_width = orig_patch_width * scales[scale_i]
new_patch_height = orig_patch_height * scales[scale_i]
new_patch_min_x = orig_patch_center_x - new_patch_width / 2 + offsets[
x_offset_i]
new_patch_min_y = orig_patch_center_y - new_patch_height / 2 + offsets[
y_offset_i]
new_patch = Patch(new_patch_min_x, new_patch_width,
new_patch_min_y, new_patch_height)
if self.patch_exceeds_pic(new_patch, pic_patch):
stats.pos_noisy_seg_too_close_to_edges += 1
continue
if self.patch_exceeds_seg(new_patch, bbox_patch):
# this will not happen with the default constants (input size, max object dimension)
stats.pos_noisy_seg_cuts_seg += 1
continue
img_path = self.create_path(self.positive_output_dir,
'pos', 'im', pic_id, seg_id,
x_offset_i, y_offset_i,
scale_i)
patch_im = self.create_and_save_image_patch(
im_arr, new_patch, img_path)
mask_path = self.create_path(self.positive_output_dir,
'pos', 'mask', pic_id, seg_id,
x_offset_i, y_offset_i,
scale_i)
patch_seg_im = self.create_and_save_mask(
full_seg_im, new_patch, mask_path)
self.create_and_save_mirror(self.positive_output_dir,
'pos', patch_seg_im, patch_im,
pic_id, seg_id, x_offset_i,
y_offset_i, scale_i)
created_examples += 2 # example and mirror
return created_examples
def create_and_save_mirror(self, base_dir, ex_type, mask, im_patch, pic_id,
seg_id, x_offset, y_offset, scale):
mir_im = ImageOps.mirror(im_patch)
mir_im.save(
self.create_path(base_dir, ex_type, 'mir-im', pic_id, seg_id,
x_offset, y_offset, scale))
mir_mask = ImageOps.mirror(mask)
mir_mask.save(
self.create_path(base_dir, ex_type, 'mir-mask', pic_id, seg_id,
x_offset, y_offset, scale))
def create_path(self, base_dir, ex_type, im_type, pic_id, seg_id, offset_x,
offset_y, scale):
return str('%s/%s-%d-%d-%d-%d-%d-%s.png' %
(base_dir, ex_type, pic_id, seg_id, offset_x, offset_y,
scale, im_type))
def patch_exceeds_pic(self, seg_patch, pic_patch):
return not pic_patch.contains(seg_patch)
def patch_exceeds_seg(self, seg_patch, bbox_patch):
return not seg_patch.contains(bbox_patch)
def segment_size_not_right(self, seg_width, seg_height, seg_id, pic_id,
stats):
max_dim = max(seg_height, seg_width)
if max_dim > self.max_object_size:
if self.debug:
print 'segment %d in picture %d is too big' % (seg_id, pic_id)
stats.seg_too_big += 1
return True
if max_dim < self.max_object_size:
if self.debug:
print 'segment %d in picture %d is too small' % (seg_id,
pic_id)
stats.seg_too_small += 1
return True
return False
def create_and_save_image_patch(self, im_arr, new_patch, img_path):
new_patch_x_max = new_patch.x_min + new_patch.width # not inclusive
new_patch_y_max = new_patch.y_min + new_patch.height
patch_im_arr = im_arr[new_patch.y_min:new_patch_y_max,
new_patch.x_min:new_patch_x_max]
patch_im = Image.fromarray(patch_im_arr)
patch_im = patch_im.resize((self.window_size, self.window_size),
Image.ANTIALIAS)
patch_im.save(img_path)
return patch_im
def create_and_save_mask(self, full_seg_im, new_patch, mask_path):
new_patch_x_max = new_patch.x_min + new_patch.width # not inclusive
new_patch_y_max = new_patch.y_min + new_patch.height
patch_seg_im = full_seg_im.crop((new_patch.x_min, new_patch.y_min,
new_patch_x_max, new_patch_y_max))
patch_seg_im = patch_seg_im.resize(
(self.window_size, self.window_size), Image.ANTIALIAS)
patch_seg_im.save(mask_path)
return patch_seg_im
def create_negative_examples_from_picture(self, canonical_seg_patches,
pic_id, pic_patch, im_arr,
stats):
curr_ex_id = 0
examples_generated = 0
can_seg_patch_centers = [seg.center() for seg in canonical_seg_patches]
# including neutral translation to allow one dimension noise
offsets = [
-negative_ex_min_offset * 1.5, -negative_ex_min_offset, 0,
negative_ex_min_offset, negative_ex_min_offset * 1.5
]
scales = [
pow(2, -negative_ex_min_scale - 0.5),
pow(2, -negative_ex_min_scale),
pow(2, negative_ex_min_scale),
pow(2, negative_ex_min_scale + 0.5)
]
for can_seg_patch in canonical_seg_patches:
[can_patch_center_x, can_patch_center_y] = can_seg_patch.center()
for xoi, x_offset in enumerate(offsets):
for yoi, y_offset in enumerate(offsets):
if x_offset == 0 and y_offset == 0:
continue # no alteration
neg_patch = Patch(x_min=can_seg_patch.x_min + x_offset,
y_min=can_seg_patch.y_min + y_offset,
width=can_seg_patch.width,
height=can_seg_patch.height)
if self.is_close_to_other_patches(neg_patch.center(),
can_seg_patch_centers):
stats.neg_seg_too_close_to_other_segs += 1
continue
if self.patch_exceeds_pic(neg_patch, pic_patch):
stats.neg_seg_too_close_to_edges += 1
continue
neg_path = self.create_path(self.negative_output_dir,
'neg', 'im', pic_id,
curr_ex_id, xoi, yoi, 0)
im_patch = self.create_and_save_image_patch(
im_arr, neg_patch, neg_path)
mask_path = self.create_path(self.negative_output_dir,
'neg', 'mask', pic_id,
curr_ex_id, xoi, yoi, 0)
self.empty_mask.save(mask_path)
self.create_and_save_mirror(self.negative_output_dir,
'neg', self.empty_mask,
im_patch, pic_id, curr_ex_id,
xoi, yoi, 0)
curr_ex_id += 1
examples_generated += 2 # pic + mirror
# scale with aspect ratio kept- otherwise we might scale the lesser dimension, and the mask is legal
for scale_i, scale in enumerate(scales):
neg_patch_width = can_seg_patch.width * scale
neg_patch_height = can_seg_patch.height * scale
neg_patch_min_x = can_patch_center_x - neg_patch_width / 2
neg_patch_min_y = can_patch_center_y - neg_patch_height / 2
neg_patch = Patch(x_min=neg_patch_min_x,
width=neg_patch_width,
y_min=neg_patch_min_y,
height=neg_patch_height)
# if we only scale, we do allow the center to be close to another seg
if self.patch_exceeds_pic(neg_patch, pic_patch):
stats.neg_seg_too_close_to_edges += 1
continue
neg_path = self.create_path(self.negative_output_dir, 'neg',
'im', pic_id, curr_ex_id, 0, 0,
scale_i)
im_patch = self.create_and_save_image_patch(
im_arr, neg_patch, neg_path)
mask_path = self.create_path(self.negative_output_dir, 'neg',
'mask', pic_id, curr_ex_id, 0, 0,
scale_i)
self.empty_mask.save(mask_path)
self.create_and_save_mirror(self.negative_output_dir, 'neg',
self.empty_mask, im_patch, pic_id,
curr_ex_id, 0, 0, scale_i)
curr_ex_id += 1
examples_generated += 2 # pic + mirror
return examples_generated
def is_close_to_other_patches(self, seg_center, other_seg_centers):
# check seg_center x and y distance from the other centers.
# each pair must have a minimal distance in at least on dimension
for other_seg_center in other_seg_centers:
if (abs(seg_center[0] - other_seg_center[0]) <
negative_ex_min_offset
and abs(seg_center[1] - other_seg_center[1]) <
negative_ex_min_offset):
return True
return False
im = coco_utils.get_annotation_image(anns[ann_num], img['width'],
img['height'])
im_path = str.format('../Results/%s_%s_%s.jpg' %
(img_id, ann_num, centered_text))
im.save(im_path, 'JPEG')
return len(anns)
# these lines causes errors with ntdll. might also happen in showAnns, like in image 262148
# as an alternative, I use ImageDraw to manually calculate the mask
# Rs = frPyObjects(ann['segmentation'], img['height'], img['width'])
# masks = decode(Rs)
imgs_to_test = 10
imgs_path = '../../annotations/images/'
coco_utils = CocoUtils('../..', 'train2014')
success = 0
current_img_i = 0
while success < imgs_to_test:
pic_data = coco_utils.coco.imgs.items()[current_img_i]
pic_id = pic_data[0]
pic_path = imgs_path + pic_data[1]['file_name']
if os.path.isfile(pic_path):
anns_found = test_img(pic_id, pic_path)
print('Finished %s annotations in picture %s in %s' %
(anns_found, pic_id, pic_path))
success += 1
current_img_i += 1
from CocoUtils import *
import os
data_dir = '../..'
# coco_utils = CocoUtils(data_dir, 'val2014')
coco_utils = CocoUtils(data_dir, 'train2014')
image_ids_and_names = coco_utils.get_images_data()
# images_dir = '%s/annotations/images_val/' % data_dir
images_dir = '%s/annotations/images_train/' % data_dir
for pic_data in image_ids_and_names:
pic_id = pic_data['id']
pic_path = images_dir + pic_data['file_name']
if not os.path.isfile(pic_path):
try:
coco_utils.coco.download(images_dir, [pic_id])
except:
pass # skip
