def crop_once(anno, write_images):
image_id = anno['image_id']
if write_images:
image = cv2.imread(
os.path.join(settings.TEST_IMAGE_DIR, anno['file_name']))
assert image.shape == imshape
cropped_list = []
for level_id, (cropratio,
cropoverlap) in enumerate(settings.TEST_CROP_LEVELS):
cropshape = (int(round(settings.TEST_IMAGE_SIZE // cropratio)),
int(round(settings.TEST_IMAGE_SIZE // cropratio)))
for o in darknet_tools.get_crop_bboxes(imshape, cropshape,
(cropoverlap, cropoverlap)):
xlo = o['xlo']
xhi = xlo + cropshape[1]
ylo = o['ylo']
yhi = ylo + cropshape[0]
basename = '{}_{}_{}'.format(image_id, level_id, o['name'])
cropped_file_name = os.path.join(settings.TEST_CROPPED_DIR,
'{}.jpg'.format(basename))
cropped_list.append(cropped_file_name)
if write_images:
cropped = image[ylo:yhi, xlo:xhi]
cv2.imwrite(cropped_file_name, cropped)
return cropped_list
def read(file_paths):
all = defaultdict(list)
imshape = (2048, 2048, 3)
removal = (0., 0., 0.)
size_ranges = ((float('-inf'), float('inf')), (32., float('inf')),
(64., float('inf')))
levelmap = dict()
for level_id, (cropratio,
cropoverlap) in enumerate(settings.TEST_CROP_LEVELS):
cropshape = (settings.TEST_IMAGE_SIZE // cropratio,
settings.TEST_IMAGE_SIZE // cropratio)
for o in darknet_tools.get_crop_bboxes(imshape, cropshape,
(cropoverlap, cropoverlap)):
levelmap[level_id, o['name']] = (o['xlo'], o['ylo'], cropshape[1],
cropshape[0])
def bounded_bbox(bbox):
x, y, w, h = bbox
x1, y1 = x + w, y + h
x0, y0 = max(0, x), max(0, y)
x1, y1 = min(imshape[1], x1), min(imshape[0], y1)
return (x0, y0, x1 - x0, y1 - y0)
def read_one(result_file_path):
with open(result_file_path) as f:
lines = f.read().splitlines()
one = []
for line in lines:
file_path, cate_id, x, y, w, h, prob = line.split()
image_id, level_id, crop_name = os.path.splitext(
os.path.basename(file_path))[0].split('_', 2)
level_id = int(level_id)
cx, cy, cw, ch = levelmap[level_id, crop_name]
cate_id = settings.NUM_CHAR_CATES if proposal_output else int(
cate_id)
x, y, w, h, prob = float(x), float(y), float(w), float(h), float(
prob)
longsize = max(w, h)
size_range = size_ranges[level_id]
if longsize < size_range[0] or size_range[1] <= longsize:
continue
rm = removal[level_id]
if (cx != 0 and x < rm) or (cy != 0 and y < rm) or (
cx + cw != imshape[1]
and x + w + rm >= cw) or (cy + ch != imshape[0]
and y + h + rm >= ch):
continue
real_bbox = bounded_bbox((x + cx, y + cy, w, h))
if real_bbox[2] > 0 and real_bbox[3] > 0:
all[image_id].append({
'image_id': image_id,
'cate_id': cate_id,
'prob': prob,
'bbox': real_bbox
})
for file_path in file_paths:
read_one(file_path)
return all
def construct_levelmap(imshape):
levelmap = {}
for level_id, (cropratio,
cropoverlap) in enumerate(settings.TEST_CROP_LEVELS):
cropshape = (settings.TEST_IMAGE_SIZE // cropratio,
settings.TEST_IMAGE_SIZE // cropratio)
for o in darknet_tools.get_crop_bboxes(imshape, cropshape,
(cropoverlap, cropoverlap)):
levelmap[level_id, o['name']] = (o['xlo'], o['ylo'],
cropshape[1], cropshape[0])
return levelmap
def crop_once(line, write_images):
anno = json.loads(line.strip())
image_id = anno['image_id']
all = []
for char in anno_tools.each_char(anno):
if not char['is_chinese']:
continue
cate_id = text2cate[char['text']]
if cate_id >= settings.NUM_CHAR_CATES:
cate_id = settings.NUM_CHAR_CATES
all.append((char['adjusted_bbox'], cate_id))
if write_images:
image = cv2.imread(
os.path.join(settings.TRAINVAL_IMAGE_DIR, anno['file_name']))
assert image.shape == imshape
for o in anno['ignore']:
poly = (np.array(o['polygon'])).astype(np.int32)
cv2.fillConvexPoly(image, poly, (128, 128, 128))
cropped_list = list()
for o in darknet_tools.get_crop_bboxes(imshape, cropshape,
cropoverlap):
xlo = o['xlo']
xhi = xlo + cropshape[1]
ylo = o['ylo']
yhi = ylo + cropshape[0]
labels = []
for bbox, cate_id in all:
x, y, w, h = bbox
if x > xhi or x + w < xlo or y > yhi or y + h < ylo:
continue
bbox = ((x + w / 2 - xlo) / cropshape[1],
(y + h / 2 - ylo) / cropshape[0], w / cropshape[1],
h / cropshape[0])
if 0.5 < in_image_ratio(bbox):
labels.append((bbox, cate_id))
if 0 < len(labels):
basename = '{}_{}'.format(image_id, o['name'])
cropped_file_name = os.path.join(settings.TRAINVAL_CROPPED_DIR,
'{}.jpg'.format(basename))
cropped_xml_name = os.path.join(settings.TRAINVAL_CROPPED_DIR,
'{}.xml'.format(basename))
cropped_list.append('{} {}'.format(cropped_file_name,
cropped_xml_name))
if write_images:
cropped = image[ylo:yhi, xlo:xhi]
cv2.imwrite(cropped_file_name, cropped)
with open(
os.path.join(settings.TRAINVAL_CROPPED_DIR,
'{}.xml'.format(basename)), 'w') as f:
write_xml(labels, cropshape, f)
return cropped_list
def crop_once(anno, write_images):
image_id = anno['image_id']
print(image_id)
#if write_images:
image = cv2.imread(
os.path.join(settings.TEST_IMAGE_DIR, anno['file_name']))
imshape = image.shape # height,width,channel
# assert image.shape == imshape
cropped_list = []
#TEST_CROP_LEVELS=((1,32),(0.5,96),(.25,96)
#TEST_IMAGE_SIZE=(128,128), so cropped image is 128*128 and 256*256 and 512*512,
for level_id, (cropratio,
cropoverlap) in enumerate(settings.TEST_CROP_LEVELS):
print(cropratio, cropoverlap)
cropshape = (int(round(settings.TEST_IMAGE_SIZE // cropratio)),
int(round(settings.TEST_IMAGE_SIZE // cropratio)))
for o in darknet_tools.get_crop_bboxes(imshape, cropshape,
(cropoverlap, cropoverlap)):
xlo = o['xlo']
xhi = xlo + cropshape[1]
ylo = o['ylo']
yhi = ylo + cropshape[0]
basename = '{}_{}_{}'.format(image_id, level_id, o['name'])
cropped_file_name = os.path.join(settings.TEST_CROPPED_DIR,
'{}.jpg'.format(basename))
cropped_list.append(cropped_file_name)
if write_images:
if xhi > imshape[1]:
image = cv2.resize(image, (xhi, imshape[0]),
cv2.INTER_CUBIC)
imshape = image.shape
if yhi > imshape[0]:
image = cv2.resize(image, (imshape[1], yhi),
cv2.INTER_CUBIC)
imshape = image.shape
cropped = image[ylo:yhi, xlo:xhi]
cv2.imwrite(cropped_file_name, cropped)
return cropped_list
def crop_once(imgPath, testName_list):
image = cv2.imread(imgPath)
imshape = image.shape
fileName = os.path.basename(imgPath)
image_id = os.path.splitext(fileName)[0]
image_type = os.path.splitext(fileName)[1]
cropped_list = []
for level_id, (cropratio,
cropoverlap) in enumerate(settings.TEST_CROP_LEVELS):
cropshape = (int(round(settings.TEST_IMAGE_SIZE // cropratio)),
int(round(settings.TEST_IMAGE_SIZE // cropratio)))
for o in darknet_tools.get_crop_bboxes(imshape, cropshape,
(cropoverlap, cropoverlap)):
xlo = o['xlo']
xhi = xlo + cropshape[1]
ylo = o['ylo']
yhi = ylo + cropshape[0]
basename = '{}_{}_{}'.format(image_id, level_id, o['name'])
cropped_file_name = os.path.join(settings.TEST_CROPPED_DIR,
basename + image_type)
cropped_list.append(cropped_file_name)
cropped = image[ylo:yhi, xlo:xhi]
cv2.imwrite(cropped_file_name, cropped)
testName_list += cropped_list