def gen_annot_file_lines(annot):
lines = []
# Get original annot line
img_fn, class_name, train_subset_class = util.parse_annot(annot)
annot_rect = util.get_annot_rect(annot)
lines.append(
gen_annot_file_line(img_fn, class_name, train_subset_class,
annot_rect))
# Load image
img = skimage.io.imread(os.path.join(common.TRAIN_IMAGE_DIR, img_fn))
# Selective search
object_proposals = util.get_object_proposals(img)
if len(object_proposals) == 0:
return lines
# Background proposals
bg_proposals = get_bg_proposals(object_proposals, annot)
if len(bg_proposals) == 0:
return lines
# Select bg proposal
bg_proposal = bg_proposals[np.random.choice(
np.array(bg_proposals).shape[0])]
x1, y1, x2, y2 = bg_proposal[0], bg_proposal[
1], bg_proposal[0] + bg_proposal[2], bg_proposal[1] + bg_proposal[3]
lines.append(
gen_annot_file_line(img_fn, common.CLASS_NAME[-1], train_subset_class,
[x1, y1, x2, y2]))
return lines
def gen_annot_file_lines(annot):
lines = []
# Get original annot line
img_fn, class_name, train_subset_class = util.parse_annot(annot)
annot_rect = util.get_annot_rect(annot)
lines.append(
gen_annot_file_line(img_fn, class_name, train_subset_class,
annot_rect))
# Load image
img = skimage.io.imread(os.path.join(common.TRAIN_IMAGE_DIR, img_fn))
# Selective search
object_proposals = util.get_object_proposals(img)
if len(object_proposals) == 0:
return lines
# Background proposals
bg_proposals = get_bg_proposals(object_proposals, annot)
if len(bg_proposals) == 0:
return lines
# Select bg proposal
bg_proposal = bg_proposals[np.random.choice(
np.array(bg_proposals).shape[0])]
x1, y1, x2, y2 = bg_proposal[0], bg_proposal[
1], bg_proposal[0] + bg_proposal[2], bg_proposal[1] + bg_proposal[3]
lines.append(
gen_annot_file_line(img_fn, common.CLASS_NAME[-1], train_subset_class,
[x1, y1, x2, y2]))
return lines
def main():
args = parse_cmdline()
img_fn = os.path.abspath(args.img_fn)
save_img = args.save_img
if not os.path.exists(img_fn):
print('Not found: {}'.format(img_fn))
sys.exit(-1)
else:
print('Target image: {}'.format(img_fn))
# Loaa target image
target_image = cv2.imread(img_fn)
# Get object proposals
object_proposals = util.get_object_proposals(target_image)
# Setup computation graph
graph_params = setup_graph()
# Model initialize
sess = tf.Session(graph=graph_params['graph'])
tf.global_variables_initializer()
if os.path.exists('models'):
save_path = os.path.join('models', 'deep_traffic_sign_model')
graph_params['saver'].restore(sess, save_path)
print('Model restored')
else:
print('Initialized')
# traffic sign recognition
results = []
for obj_proposal in object_proposals:
x, y, w, h = obj_proposal
crop_image = target_image[y:y + h, x:x + w]
results.append(
traffic_sign_recognition(sess, crop_image, obj_proposal,
graph_params))
"""
del_idx = []
for i, result in enumerate(results):
if result['pred_class'] == common.CLASS_NAME[-1]:
del_idx.append(i)
results = np.delete(results, del_idx)
"""
# Non-max suppression
nms_results = util.nms(results, pred_prob_th=0.999999, iou_th=0.4)
# Draw rectangles on the target image
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
ax.imshow(cv2.cvtColor(target_image, cv2.COLOR_BGR2RGB))
for result in nms_results:
print(result)
(x, y, w, h) = result['obj_proposal']
ax.text(x,
y,
cls2name(result['pred_class']),
fontsize=13,
bbox=dict(facecolor='red', alpha=0.7))
rect = mpatches.Rectangle((x, y),
w,
h,
fill=False,
edgecolor='red',
linewidth=1)
ax.add_patch(rect)
plt.show()
# save the target image
save_fname = os.path.splitext(os.path.basename(img_fn))[0] + '_result.jpg'
if save_img:
fig.savefig(save_fname, bbox_inches='tight', pad_inches=0.0)
def main():
args = parse_cmdline()
img_fn = os.path.abspath(args.img_fn)
if not os.path.exists(img_fn):
print('Not found: {}'.format(img_fn))
sys.exit(-1)
else:
print('Target image: {}'.format(img_fn))
# Loaa target image
target_image = util.load_target_image(img_fn)
# Get object proposals
object_proposals = util.get_object_proposals(target_image)
# Setup computation graph
graph_params = setup_graph()
# Model initialize
sess = tf.Session(graph=graph_params['graph'])
tf.global_variables_initializer()
if os.path.exists('models'):
save_path = os.path.join('models', 'deep_logo_model')
graph_params['saver'].restore(sess, save_path)
print('Model restored')
else:
print('Initialized')
# Logo recognition
results = []
for obj_proposal in object_proposals:
x, y, w, h = obj_proposal
crop_image = target_image[y:y + h, x:x + w]
results.append(
logo_recognition(sess, crop_image, obj_proposal, graph_params))
del_idx = []
for i, result in enumerate(results):
if result['pred_class'] == common.CLASS_NAME[-1]:
del_idx.append(i)
results = np.delete(results, del_idx)
# Non-max suppression
nms_results = util.nms(results, pred_prob_th=0.999999, iou_th=0.4)
# Draw rectangles on the target image
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
ax.imshow(target_image)
for result in nms_results:
print(result)
(x, y, w, h) = result['obj_proposal']
ax.text(x,
y,
result['pred_class'],
fontsize=13,
bbox=dict(facecolor='red', alpha=0.7))
rect = mpatches.Rectangle((x, y),
w,
h,
fill=False,
edgecolor='red',
linewidth=1)
ax.add_patch(rect)
plt.show()
def main(file_name, graph_params, sess):
img_fn = os.path.join("images", file_name)
if not os.path.exists(img_fn):
print('Not found: {}'.format(img_fn))
sys.exit(-1)
else:
print('Target image: {}'.format(img_fn))
# Load target image
target_image = util.load_target_image(img_fn)
#cv.normalize(target_image, target_image, 0, 255, cv.NORM_MINMAX)
# limg = np.arcsinh(target_image)
# limg /= limg.max()
# low = np.percentile(limg, 0.25)
# high = np.percentile(limg, 99.5)
# opt_img = skie.exposure.rescale_intensity(limg, in_range=(low, high))
# target_image = opt_img
# target_image = target_image.astype(np.float64)
# Get object proposals
object_proposals = util.get_object_proposals(target_image)
# Logo recognition
results = []
for obj_proposal in object_proposals:
x, y, w, h = obj_proposal
crop_image = target_image[y:y + h, x:x + w]
results.append(
logo_recognition(sess, crop_image, obj_proposal, graph_params))
del_idx = []
for i, result in enumerate(results):
if result['pred_class'] == common.CLASS_NAME[-1]:
del_idx.append(i)
results = np.delete(results, del_idx)
# Non-max suppression
nms_results = util.nms(results, pred_prob_th=0.9, iou_th=0.4)
# Draw rectangles on the target image
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
ax.imshow(target_image)
for result in nms_results:
print(result)
(x, y, w, h) = result['obj_proposal']
ax.text(x,
y,
"{} {:.2f}".format(result['pred_class'], result['pred_prob']),
fontsize=13,
bbox=dict(facecolor='red', alpha=0.7))
rect = mpatches.Rectangle((x, y),
w,
h,
fill=False,
edgecolor='red',
linewidth=1)
ax.add_patch(rect)
#img = BytesIO()
plt.tight_layout()
plt.savefig(os.path.join("results", file_name),
bbox_inches='tight',
pad_inches=0)