def segmentation(model,config):
images = load_images(config.IMAGE_PATH,config.LIMIT_IMAGES)
# Tf Session + Timeliner
tf_config = tf.ConfigProto(allow_soft_placement=True)
tf_config.gpu_options.allow_growth=True
detection_graph = model.detection_graph
if config.WRITE_TIMELINE:
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
timeliner = TimeLiner()
else:
options = tf.RunOptions(trace_level=tf.RunOptions.NO_TRACE)
run_metadata = False
timer = Timer().start()
print("> Starting Segmentaion")
with detection_graph.as_default():
with tf.Session(graph=detection_graph, config=tf_config) as sess:
for image in images:
# input
frame = cv2.imread(image)
height, width, channels = frame.shape
resize_ratio = 1.0 * 513 / max(width,height)
target_size = (int(resize_ratio * width), int(resize_ratio * height))
frame = cv2.resize(frame, target_size)
timer.tic()
batch_seg_map = sess.run('SemanticPredictions:0',
feed_dict={'ImageTensor:0': [cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)]},
options=options, run_metadata=run_metadata)
timer.toc()
if config.WRITE_TIMELINE:
timeliner.write_timeline(run_metadata.step_stats,
'test_results/timeline_{}{}.json'.format(
config.OD_MODEL_NAME,config.DEVICE))
# visualization
if config.VISUALIZE:
seg_map = batch_seg_map[0]
seg_image = create_colormap(seg_map).astype(np.uint8)
cv2.addWeighted(seg_image,config.ALPHA,frame,1-config.ALPHA,0,frame)
vis_text(frame,"fps: {}".format(timer.get_fps()),(10,30))
# boxes (ymin, xmin, ymax, xmax)
if config.BBOX:
map_labeled = measure.label(seg_map, connectivity=1)
for region in measure.regionprops(map_labeled):
if region.area > config.MINAREA:
box = region.bbox
p1 = (box[1], box[0])
p2 = (box[3], box[2])
cv2.rectangle(frame, p1, p2, (77,255,9), 2)
vis_text(frame,config.LABEL_NAMES[seg_map[tuple(region.coords[0])]],(p1[0],p1[1]-10))
cv2.imshow('segmentation',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
timer.stop()
def segmentation(model, config):
detection_graph = model.detection_graph
# fixed input sizes as model needs resize either way
vs = WebcamVideoStream(config.VIDEO_INPUT, 640, 480).start()
resize_ratio = 1.0 * 513 / max(vs.real_width, vs.real_height)
target_size = (int(resize_ratio * vs.real_width),
int(resize_ratio * vs.real_height)) #(513, 384)
tf_config = tf.ConfigProto(allow_soft_placement=True)
tf_config.gpu_options.allow_growth = True
fps = FPS(config.FPS_INTERVAL).start()
print("> Starting Segmentaion")
with detection_graph.as_default():
with tf.Session(graph=detection_graph, config=tf_config) as sess:
while vs.isActive():
frame = vs.resized(target_size)
batch_seg_map = sess.run(
'SemanticPredictions:0',
feed_dict={
'ImageTensor:0':
[cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)]
})
# visualization
if config.VISUALIZE:
seg_map = batch_seg_map[0]
seg_image = create_colormap(seg_map).astype(np.uint8)
cv2.addWeighted(seg_image, config.ALPHA, frame,
1 - config.ALPHA, 0, frame)
vis_text(frame, "fps: {}".format(fps.fps_local()),
(10, 30))
# boxes (ymin, xmin, ymax, xmax)
if config.BBOX:
map_labeled = measure.label(seg_map, connectivity=1)
for region in measure.regionprops(map_labeled):
if region.area > config.MINAREA:
box = region.bbox
p1 = (box[1], box[0])
p2 = (box[3], box[2])
cv2.rectangle(frame, p1, p2, (77, 255, 9), 2)
vis_text(
frame, config.LABEL_NAMES[seg_map[tuple(
region.coords[0])]],
(p1[0], p1[1] - 10))
cv2.imshow(config.DL_MODEL_NAME, frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
fps.update()
fps.stop()
vs.stop()