def load_idl(idlfile, data_mean, net_config, jitter=True):
"""Take the idlfile, data mean and net configuration and create a generator
that outputs a jittered version of a random image from the annolist
that is mean corrected."""
annolist = al.parse(idlfile)
annos = [x for x in annolist]
for anno in annos:
anno.imageName = os.path.join(
os.path.dirname(os.path.realpath(idlfile)), anno.imageName)
while True:
random.shuffle(annos)
for anno in annos:
if jitter:
jit_image, jit_anno = annotation_jitter(
anno, target_width=net_config["img_width"],
target_height=net_config["img_height"])
else:
jit_image = imread(anno.imageName)
jit_anno = anno
image = image_to_h5(jit_image, data_mean, image_scaling=1.0)
boxes, box_flags = annotation_to_h5(
jit_anno, net_config["grid_width"], net_config["grid_height"],
net_config["region_size"], net_config["max_len"])
yield {"imname": anno.imageName, "raw": jit_image, "image": image,
"boxes": boxes, "box_flags": box_flags, "anno": jit_anno}
def load_idl(idlfile, data_mean, net_config, jitter=True):
"""Take the idlfile, data mean and net configuration and create a generator
that outputs a jittered version of a random image from the annolist
that is mean corrected."""
annolist = al.parse(idlfile)
annos = [x for x in annolist]
for anno in annos:
anno.imageName = os.path.join(
os.path.dirname(os.path.realpath(idlfile)), anno.imageName)
while True:
random.shuffle(annos)
for anno in annos:
if jitter:
jit_image, jit_anno = annotation_jitter(
anno,
target_width=net_config["img_width"],
target_height=net_config["img_height"])
else:
jit_image = imread(anno.imageName)
jit_anno = anno
image = image_to_h5(jit_image, data_mean, image_scaling=1.0)
boxes, box_flags = annotation_to_h5(jit_anno,
net_config["grid_width"],
net_config["grid_height"],
net_config["region_size"],
net_config["max_len"])
yield {
"imname": anno.imageName,
"raw": jit_image,
"image": image,
"boxes": boxes,
"box_flags": box_flags,
"anno": jit_anno
}
def convert_deploy_2_train(boot_deploy_list,
data_mean,
net_config,
max_heads=999999,
threshold=0.9,
jitter=True,
if_random=True):
annos = []
cnt = 0
pix_per_w = net_config["img_width"] / net_config["grid_width"]
pix_per_h = net_config["img_height"] / net_config["grid_height"]
for dic in boot_deploy_list:
anno = Annotation()
anno.imageName = dic["imname"]
bbox_list = dic["bbox"]
conf_list = dic["conf"]
all_rects = [[[] for x in range(net_config["grid_width"])]
for y in range(net_config["grid_height"])]
for n in range(len(bbox_list)):
for k in range(net_config["grid_height"] *
net_config["grid_width"]):
y = int(k / net_config["grid_width"])
x = int(k % net_config["grid_width"])
bbox = bbox_list[n][k]
conf = conf_list[n][k, 1].flatten()[0]
abs_cx = pix_per_w / 2 + pix_per_w * x + int(bbox[0, 0, 0])
abs_cy = pix_per_h / 2 + pix_per_h * y + int(bbox[1, 0, 0])
w = bbox[2, 0, 0]
h = bbox[3, 0, 0]
if conf < threshold:
continue
all_rects[y][x].append(Rect(abs_cx, abs_cy, w, h, conf))
acc_rects = stitch_rects(all_rects, net_config)
for rect in acc_rects:
if rect.true_confidence >= threshold:
r = AnnoRect()
r.x1 = int(rect.cx - rect.width / 2.)
r.x2 = int(rect.cx + rect.width / 2.)
r.y1 = int(rect.cy - rect.height / 2.)
r.y2 = int(rect.cy + rect.height / 2.)
anno.rects.append(r)
annos.append(anno)
cnt += len(anno.rects)
if cnt >= max_heads:
break
print 'deployed', len(annos), 'images with', cnt, 'heads'
while True:
if if_random:
random.shuffle(annos)
for anno in annos:
if jitter:
jit_image, jit_anno = annotation_jitter(
anno,
target_width=net_config["img_width"],
target_height=net_config["img_height"])
else:
jit_image = imread(anno.imageName)
jit_anno = anno
image = image_to_h5(jit_image, data_mean, image_scaling=1.0)
boxes, box_flags = annotation_to_h5(jit_anno,
net_config["grid_width"],
net_config["grid_height"],
net_config["region_size"],
net_config["max_len"])
yield {
"num": len(annos),
"imname": anno.imageName,
"raw": jit_image,
"image": image,
"boxes": boxes,
"box_flags": box_flags,
'anno': jit_anno
}