def main(name_scope, gpu_dev, num_images, args):
t=args.t
model = initialize_model_from_cfg()
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
temp_frame_folder = osp.join(args.out_path,args.vid_name + '_frames/',str(t))
imgs = glob.glob(temp_frame_folder+'/*.jpg')
for i in range(len(imgs)):
if i%100==0:
print('Processing Detection for Frame %d'%(i+1))
im_ = cv2.imread(imgs[i])
assert im_ is not None
im_ = np.expand_dims(im_, 0)
with core.NameScope(name_scope):
with core.DeviceScope(gpu_dev):
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
model, im_, None) #TODO: Parallelize detection
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
det_name = args.vid_name + '_' + str(args.t) + '_detections.pkl'
det_file = osp.join(args.out_path, det_name)
robust_pickle_dump(dict(all_keyps=all_keyps),det_file)
shutil.rmtree(osp.join(args.out_path, args.vid_name + '_frames'))
def localize_obj_in_image(path_to_image, maskRCNN, timers):
'''
Localize foreground objects in the image of 'path_to_image'.
You may replace this function with a call to your localization method
'''
img = imread(path_to_image)
base = basename(path_to_image)
name = splitext(base)[0]
h, w = img.shape[0:2]
bbox = []
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
img,
timers=timers)
import pdb
pdb.set_trace()
for x in cls_boxes:
print(x)
break
# randomly generate up to 5 bounding boxes for each image
# with size between [50, 100)
'''
for k in range(np.random.randint(5)):
label = classes[np.random.randint(11)]
score = np.random.rand(1)[0]
bb_w = np.random.randint(50, 100, 1)[0] # width
bb_h = np.random.randint(50, 100, 1)[0] # height
bb_x1 = np.random.randint(0, w-100, 1)[0]
bb_y1 = np.random.randint(0, h-100, 1)[0]
bb_x2 = bb_x1 + bb_w - 1
bb_y2 = bb_y1 + bb_h - 1
bbox.append([name, label, score, bb_x1, bb_y1, bb_x2, bb_y2])
'''
return bbox
def test_cls_net(output_dir, ind_range=None, gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert cfg.TEST.WEIGHTS != '', \
'TEST.WEIGHTS must be set to the model file to test'
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
assert cfg.TEST.DATASET != '', \
'TEST.DATASET must be set to the dataset name to test'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
ind_range, gt_cls=True)
model = initialize_model_from_cfg(gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_cls = []
all_labs = []
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
im = cv2.imread(entry['image'])
with c2_utils.NamedCudaScope(gpu_id):
cls_scores, _, _ = im_detect_all(model, im, None, timers)
all_cls.append(np.argmax(cls_scores))
all_labs.append(entry["gt_classes"][0])
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
cls_time = (timers['classify_im'].average_time)
logger.info(
('im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s (eta: {})').format(
start_ind + 1, end_ind, total_num_images,
start_ind + i + 1, start_ind + num_images, cls_time, eta))
correct_pred = np.equal(np.array(all_cls), np.array(all_labs))
acc = np.mean(correct_pred.astype(np.float32))
return acc
def test_net_Car3D(
args,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, 'Use rpn_generate to generate proposals from RPN-only models'
dataset = JsonDataset(dataset_name, args.dataset_dir)
timers = defaultdict(Timer)
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(dataset, proposal_file, ind_range, args)
num_images = len(roidb)
image_ids = []
if cfg.MODEL.TRANS_HEAD_ON:
json_dir = os.path.join(output_dir, 'json_'+args.list_flag+'_trans')
else:
json_dir = os.path.join(output_dir, 'json_'+args.list_flag)
json_dir += '_iou_' + str(args.iou_ignore_threshold)
if not cfg.TEST.BBOX_AUG.ENABLED:
json_dir += '_BBOX_AUG_single_scale'
else:
json_dir += '_BBOX_AUG_multiple_scale'
if not cfg.TEST.CAR_CLS_AUG.ENABLED:
json_dir += '_CAR_CLS_AUG_single_scale'
else:
json_dir += '_CAR_CLS_AUG_multiple_scale'
if cfg.TEST.GEOMETRIC_TRANS:
json_dir += '_GEOMETRIC_TRANS'
if cfg.TEST.CAR_CLS_AUG.H_FLIP and cfg.TEST.CAR_CLS_AUG.SCALE_H_FLIP:
json_dir += '_hflipped'
roidb = roidb
for i, entry in enumerate(roidb):
image_ids.append(entry['image'])
args.image_ids = image_ids
all_boxes = [[[] for _ in range(num_images)] for _ in range(cfg.MODEL.NUM_CLASSES)]
if ind_range is not None:
if cfg.TEST.SOFT_NMS.ENABLED:
det_name = 'detection_range_%s_%s_soft_nms' % tuple(ind_range)
else:
det_name = 'detection_range_(%d_%d)_nms_%.1f' % (ind_range[0], ind_range[1], cfg.TEST.NMS)
if cfg.TEST.BBOX_AUG.ENABLED:
det_name += '_multiple_scale'
det_name += '.pkl'
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
file_complete_flag = [not os.path.exists(os.path.join(json_dir, entry['image'].split('/')[-1][:-4] + '.json')) for entry in roidb]
# If we don't have the complete json file, we will load the model and execute the following:
if np.sum(file_complete_flag) or not os.path.exists(det_file):
model = initialize_model_from_cfg(args, gpu_id=gpu_id)
for i in tqdm(range(len(roidb))):
entry = roidb[i]
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
ignored_mask_img = os.path.join(('/').join(entry['image'].split('/')[:-2]), 'ignore_mask', entry['image'].split('/')[-1])
ignored_mask = cv2.imread(ignored_mask_img, cv2.IMREAD_GRAYSCALE)
ignored_mask_binary = np.zeros(ignored_mask.shape)
ignored_mask_binary[ignored_mask > 250] = 1
if cfg.MODEL.NON_LOCAL_TEST and not cfg.TEST.BBOX_AUG.ENABLED:
cls_boxes_i, cls_segms_i, _, car_cls_i, euler_angle_i, trans_pred_i, f_div_C = im_detect_all(model, im, box_proposals, timers, dataset)
else:
cls_boxes_i, cls_segms_i, _, car_cls_i, euler_angle_i, trans_pred_i = im_detect_all(model, im, box_proposals, timers, dataset)
extend_results(i, all_boxes, cls_boxes_i)
# We draw the grid overlap with an image here
if False:
f_div_C_plot = f_div_C.copy()
grid_size = 32 # This is the res5 output space
fig = plt.figure()
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2)
ax1.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
ax1.grid(which='minor')
# We choose the point here:
# x, y = int(1757/grid_size), int(1040/grid_size) # val 164
x, y = int(1830/grid_size), int(1855/grid_size)
# draw a patch hre
rect = patches.Rectangle((x*grid_size-grid_size, y*grid_size-grid_size), grid_size*3, grid_size*3,
linewidth=1, edgecolor='m', facecolor='m')
ax1.add_patch(rect)
#att_point_map = f_div_C_plot[106*x+y, :]
att_point_map = f_div_C_plot[106*y+x, :]
att_point_map = np.reshape(att_point_map, (85, 106))
ax2.imshow(att_point_map, cmap='jet')
# we draw 20 arrows
for i in range(10):
x_max, y_max = np.unravel_index(att_point_map.argmax(), att_point_map.shape)
v = att_point_map[x_max, y_max]
att_point_map[x_max, y_max] = 0
ax1.arrow(x*grid_size, y*grid_size, (y_max-x)*grid_size, (x_max-y)*grid_size,
fc="r", ec="r", head_width=(10-i)*grid_size/2, head_length=grid_size)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = timers['im_detect_bbox'].average_time
triple_head_time = timers['triple_head'].average_time
misc_time = (
timers['misc_bbox'].average_time +
timers['misc_mask'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} det-time: {:.3f}s + triple-head-time: {:.3f}s + misc_time: {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + i + 1,
start_ind + num_images, det_time, triple_head_time, misc_time, eta
)
)
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.write_pose_to_json(
im_name=im_name,
output_dir=json_dir,
boxes=cls_boxes_i,
car_cls_prob=car_cls_i,
euler_angle=euler_angle_i,
trans_pred=trans_pred_i,
segms=cls_segms_i,
dataset=dataset.Car3D,
thresh=cfg.TEST.SCORE_THRESH_FOR_TRUTH_DETECTION,
ignored_mask_binary=ignored_mask_binary.astype('uint8'),
iou_ignore_threshold=args.iou_ignore_threshold
)
if cfg.VIS:
vis_utils.vis_one_image_eccv2018_car_3d(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis_'+args.list_flag),
boxes=cls_boxes_i,
car_cls_prob=car_cls_i,
euler_angle=euler_angle_i,
trans_pred=trans_pred_i,
car_models=dataset.Car3D.car_models,
intrinsic=dataset.Car3D.get_intrinsic_mat(),
segms=cls_segms_i,
keypoints=None,
thresh=0.9,
box_alpha=0.8,
dataset=dataset.Car3D)
save_object(dict(all_boxes=all_boxes), det_file)
# The following evaluate the detection result from Faster-RCNN Head
# If we have already computed the boxes
if os.path.exists(det_file):
obj = load_object(det_file)
all_boxes = obj['all_boxes']
# this is a hack
if False:
import glob
det_files = sorted(glob.glob(args.output_dir+'/detection_range_*.pkl'))
det_files = [det_files[4], det_files[1], det_files[2], det_files[3]]
obj = load_object(det_files[0])
all_boxes = obj['all_boxes']
for df in det_files:
obj = load_object(df)
boxes = obj['all_boxes']
for i in range(len(boxes)):
all_boxes[i] = all_boxes[i] + boxes[i]
save_object(dict(all_boxes=all_boxes), det_file)
results = task_evaluation.evaluate_boxes(dataset, all_boxes, output_dir, args)
# The following evaluate the mAP of car poses
args.test_dir = json_dir
args.gt_dir = args.dataset_dir + 'car_poses'
args.res_file = os.path.join(output_dir, 'json_'+args.list_flag+'_res.txt')
args.simType = None
det_3d_metric = Detect3DEval(args)
det_3d_metric.evaluate()
det_3d_metric.accumulate()
det_3d_metric.summarize()
def main():
"""main function"""
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
video_path = args.video
video_save_dir = args.video_save_dir
if not os.path.exists(video_save_dir):
os.mkdir(video_save_dir)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
capture = cv2.VideoCapture(video_path)
frame_count = 0
batch_size = 1
frame_list = []
while True:
ret, frame = capture.read()
# Bail out when the video file ends
if not ret:
break
# Save each frame of the video to a list
frame_count += 1
frame_list.append(frame)
if len(frame_list) == batch_size:
for i, frame in enumerate(frame_list):
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
frame,
timers=timers)
name = '{0}'.format(frame_count + i - batch_size)
print('\n' + name)
vis_utils.vis_one_image(
frame[:, :, ::-1], # BGR -> RGB for visualization
name,
video_save_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.8,
kp_thresh=2,
ext='jpg')
# Clear the frames array to start the next batch
frame_list = []
images = list(glob.iglob(os.path.join(video_save_dir, '*.jpg')))
# Sort the images by integer index
images = sorted(images, key=lambda x: float(os.path.split(x)[1][:-3]))
outvid = os.path.join(video_save_dir, "out.mp4")
make_video(outvid, images, fps=30)
extract_audio = 'ffmpeg -i %s -vn -acodec copy %s/out.aac' % (
video_path, video_save_dir)
subprocess.call(extract_audio, shell=True)
merge_audio_video = "ffmpeg -i %s/out.aac -i %s/out.mp4 -codec copy -shortest %s/final.mp4" % (
video_save_dir, video_save_dir, video_save_dir)
subprocess.call(merge_audio_video, shell=True)
def test_net(
args,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, 'Use rpn_generate to generate proposals from RPN-only models'
dataset = JsonDataset(dataset_name, args.dataset_dir)
timers = defaultdict(Timer)
if ind_range is not None:
if cfg.TEST.SOFT_NMS.ENABLED:
det_name = 'detection_range_%s_%s_soft_nms.pkl' % tuple(ind_range)
else:
det_name = 'detection_range_(%d_%d)_nms_%.1f.pkl' % (ind_range[0], ind_range[1], cfg.TEST.NMS)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(dataset, proposal_file, ind_range, args)
num_images = len(roidb)
image_ids = []
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
for i, entry in enumerate(roidb):
image_ids.append(entry['image'])
args.image_ids = image_ids
# If we have already computed the boxes
if os.path.exists(det_file):
obj = load_object(det_file)
all_boxes, all_segms, all_keyps = obj['all_boxes'], obj['all_segms'], obj['all_keyps']
else:
model = initialize_model_from_cfg(args, gpu_id=gpu_id)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
cls_boxes_i, cls_segms_i, cls_keyps_i, car_cls_i, euler_angle_i, trans_pred_i = im_detect_all(model, im, box_proposals, timers, dataset)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (
timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time
)
misc_time = (
timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + i + 1,
start_ind + num_images, det_time, misc_time, eta
)
)
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image_eccv2018_car_3d(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
boxes=cls_boxes_i,
car_cls_prob=car_cls_i,
euler_angle=euler_angle_i,
trans_pred=trans_pred_i,
car_models=dataset.Car3D.car_models,
intrinsic=dataset.Car3D.get_intrinsic_mat(),
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=0.9,
box_alpha=0.8,
dataset=dataset.Car3D)
cfg_yaml = yaml.dump(cfg)
save_object(
dict(
all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
results = task_evaluation.evaluate_all(dataset, all_boxes, all_segms, all_keyps, output_dir, args)
return results
def main():
"""main function"""
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
for i in xrange(num_images):
print('img', i)
im = cv2.imread(imglist[i])
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
if args.merge_pdfs and num_images > 1:
merge_out_path = '{}/results.pdf'.format(args.output_dir)
if os.path.exists(merge_out_path):
os.remove(merge_out_path)
command = "pdfunite {}/*.pdf {}".format(args.output_dir,
merge_out_path)
subprocess.call(command, shell=True)
from __future__ import absolute_import
def test_net(args,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
#assert not cfg.MODEL.RPN_ONLY, \
# 'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range)
model = initialize_model_from_cfg(args, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = io.imread(entry['image'])
im_name = entry['image'].split('/')[-1][:-4]
if cfg.TEST.DATASETS == ('nuclei_det_seg_train', ):
track = entry['image'].split('/')[-2]
det_file = os.path.join(output_dir, track + '_' + im_name + '.pkl')
else:
det_file = os.path.join(output_dir, im_name + '.pkl')
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
model, im, box_proposals, timers)
save_object(
dict(
all_boxes=cls_boxes_i,
all_segms=cls_segms_i,
#all_keyps=all_keyps,
#cfg=cfg_yaml
),
det_file)
logger.info('Wrote detections to: {}'.format(
os.path.abspath(det_file)))
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time)
misc_time = (timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time)
logger.info(('im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})').format(
start_ind + 1, end_ind, total_num_images,
start_ind + i + 1, start_ind + num_images,
det_time, misc_time, eta))
# det_file = os.path.join(output_dir, det_name)
'''save_object(
dict(
all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml
), det_file
)'''
#logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps
def main():
"""main function"""
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN, cpu_keywords=['im_info', 'roidb'],
minibatch=True, device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
for i in xrange(num_images):
print('img', i)
im = cv2.imread(imglist[i])
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN, im, timers=timers)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
if args.merge_pdfs and num_images > 1:
merge_out_path = '{}/results.pdf'.format(args.output_dir)
if os.path.exists(merge_out_path):
os.remove(merge_out_path)
command = "pdfunite {}/*.pdf {}".format(args.output_dir,
merge_out_path)
subprocess.call(command, shell=True)
def main():
"""main function"""
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset.startswith("keypoints_carfusion"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
assert bool(args.load_ckpt_car) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
print('load cfg from file: {}'.format(args.cfg_file_person))
cfg_from_file(args.cfg_file_person)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
cfg.RESNETS.IMAGENET_PRETRAINED = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN_person = Generalized_RCNN()
#print('load cfg from file: {}'.format(args.cfg_file_person))
#cfg_from_file(args.cfg_file_person)
#assert_and_infer_cfg()
#maskRCNN_person = Generalized_RCNN()
if args.visualize:
save_image = True
if args.cuda:
maskRCNN_person.cuda()
if args.load_ckpt_person:
load_name = args.load_ckpt_person
print("loading checkpoint for person %s" % (load_name))
checkpoint_person = torch.load(
load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN_person, checkpoint_person['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN_car, args.load_detectron)
maskRCNN_person = mynn.DataParallel(
maskRCNN_person,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN_person.eval()
print('load cfg from file: {}'.format(args.cfg_file_car))
cfg_from_file(args.cfg_file_car)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
#cfg.RESNETS.IMAGENET_PRETRAINED = False # Don't need to load imagenet pretrained weights
#assert_and_infer_cfg()
maskRCNN_car = Generalized_RCNN()
if args.cuda:
maskRCNN_car.cuda()
if args.load_ckpt_car:
load_name = args.load_ckpt_car
print("loading checkpoint for car %s" % (load_name))
checkpoint_car = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN_car, checkpoint_car['model'])
maskRCNN_car = mynn.DataParallel(maskRCNN_car,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN_car.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
#imglist.sort(key=lambda f: int(filter(str.isdigit, f)))
l = imglist
try:
imglist = sorted(
l, key=lambda x: int(os.path.splitext(x)[0].split('/')[-1]))
except:
print('images couldnot be sorted')
for i in xrange(num_images):
print('img', i, ' out of ', num_images, ' filename: ',
imglist[i].split('/')[-1], ' in camera',
imglist[i].split('/')[-2])
im = cv2.imread(imglist[i])
try:
assert im is not None
except:
continue
timers = defaultdict(Timer)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
#print(im_name)
output_name = os.path.basename(im_name) + '.txt'
output_file = os.path.join(args.output_dir, '{}'.format(output_name))
text_file = open(output_file, "w")
cfg_from_file(args.cfg_file_car)
cls_boxes_car, cls_segms_car, cls_keyps_car, features_car = im_detect_all(
maskRCNN_car, im, timers=timers)
if len(cls_boxes_car[1]) > 0:
features_car = features_car.data.cpu().numpy()
#print(loop,loop2)
#print(distance_matrix)
#plt.figure()
#plot_confusion_matrix(distance_matrix, classes=[0,1,2,3,4,5,6],
#title='Confusion matrix, without normalization')
#fig = plt.figure()
#ax = fig.add_subplot(1,1,1)
#ax.set_aspect('equal')
#plt.imshow(distance_matrix, interpolation='nearest', cmap=plt.cm.ocean)
#plt.colorbar()
#plt.show()
#fig.savefig('1.png')
count = 0
filename = 'finalized_model.txt'
loaded_model = pickle.load(open(filename, 'rb'))
for ind, bb in enumerate(cls_boxes_car[1]):
string = str(count)
keyps = [k for klist in cls_keyps_car for k in klist]
bb_new = [bb[0], bb[1], bb[2] - bb[0], bb[3] - bb[1]]
features = features_car[ind, :, :, :].flatten()
pca_feature = []
pca_feature.append(np.transpose(features.astype(np.float)))
#print(pca_feature)
features = loaded_model.transform(pca_feature)
features = features[0] #loaded_model.transform(pca_feature)
if bb[4] < 0.5:
continue
#for bb_ind,val in enumerate(bb_new):
# string = string + ',' + str(val)
for kp_ind, kp in enumerate(keyps[ind][0]):
string = string + ',' + str(kp) + ',' + str(
keyps[ind][1][kp_ind]) + ',' + str(
int(keyps[ind][2][kp_ind]))
for feature_ind, feature in enumerate(features):
string = string + ',' + str(feature)
string = string + ',car'
text_file.write(string)
text_file.write('\n')
#print(string)
count = count + 1
cfg_from_file(args.cfg_file_person)
cls_boxes_person, cls_segms_person, cls_keyps_person, features_person = im_detect_all(
maskRCNN_person, im, timers=timers)
if len(cls_boxes_person[1]) > 0:
features_person = features_person.data.cpu().numpy()
for ind, bb in enumerate(cls_boxes_person[1]):
string = str(count)
keyps = [k for klist in cls_keyps_person for k in klist]
bb_new = [bb[0], bb[1], bb[2] - bb[0], bb[3] - bb[1]]
features = features_person[ind, :, :, :].flatten()
pca_feature = []
pca_feature.append(np.transpose(features.astype(np.float)))
#print(pca_feature)
features = loaded_model.transform(pca_feature)
features = features[0] #loaded_model.transform(pca_feature)
#features = loaded_model.transform(np.transpose(features.astype(np.float)))
# print(features)
if bb[4] < 0.5:
continue
for bb_ind, val in enumerate(bb_new):
string = string + ',' + str(val)
for kp_ind, kp in enumerate(keyps[ind][0]):
string = string + ',' + str(kp) + ',' + str(
keyps[ind][1][kp_ind]) + ',' + str(
int(keyps[ind][2][kp_ind]))
for feature_ind, feature in enumerate(features):
string = string + ',' + str(feature)
string = string + ',person'
text_file.write(string)
text_file.write('\n')
#print(string)
count = count + 1
if save_image == True:
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
image_car = vis_utils.vis_one_image_car(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes_car,
cls_segms_car,
cls_keyps_car,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.5,
kp_thresh=0.1)
output_name = os.path.basename(im_name) + '.png'
im = cv2.imread(
os.path.join(args.output_dir, '{}'.format(output_name)))
if im is None:
continue
continue
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes_person,
cls_segms_person,
cls_keyps_person,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.5,
kp_thresh=10)
if args.merge_pdfs and num_images > 1 and save_image == True:
merge_out_path = '{}/results.pdf'.format(args.output_dir)
if os.path.exists(merge_out_path):
os.remove(merge_out_path)
command = "pdfunite {}/*.pdf {}".format(args.output_dir,
merge_out_path)
subprocess.call(command, shell=True)
def test_net(args,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0,
active_model=None,
step=None):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range)
if active_model is None:
model = initialize_model_from_cfg(args, gpu_id=gpu_id)
else:
model = active_model
if 'train' in dataset_name:
mode = 'train'
elif 'val' in dataset_name:
mode = 'val'
elif 'test' in dataset_name:
mode = 'test'
else:
raise Exception
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
# num_images = 5
all_boxes, all_segms, all_keyps, all_hois, all_keyps_vcoco = empty_results(
num_classes, num_images)
timers = defaultdict(Timer)
all_losses = defaultdict(list)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
# h, w, c
im = cv2.imread(entry['image'])
if not cfg.VCOCO.USE_PRECOMP_BOX:
cls_boxes_i, cls_segms_i, cls_keyps_i, hoi_res_i, vcoco_cls_keyps_i, loss_i = \
im_detect_all(model, im, box_proposals, timers, entry)
else:
cls_boxes_i, cls_segms_i, cls_keyps_i, hoi_res_i, vcoco_cls_keyps_i, loss_i = \
im_detect_all_precomp_box(model, im, timers, entry, mode, dataset.json_category_id_to_contiguous_id)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if hoi_res_i is not None:
all_hois[entry['id']] = hoi_res_i
if vcoco_cls_keyps_i is not None:
extend_results(i, all_keyps_vcoco, vcoco_cls_keyps_i)
if loss_i['interaction_action_loss'] is not None:
for k, v in loss_i.items():
all_losses[k].append(v)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
if step is None:
det_name = 'detections.pkl'
else:
det_name = 'detections_step{}.pkl'.format(step)
det_file = os.path.join(output_dir, det_name)
save_object(
dict(all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
all_hois=all_hois,
all_keyps_vcoco=all_keyps_vcoco,
all_losses=all_losses,
cfg=cfg_yaml), det_file)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps, all_hois, all_keyps_vcoco, all_losses
def main():
"""main function"""
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
prefix_path = args.output_dir + '_results'
if os.path.exists(prefix_path):
shutil.rmtree(prefix_path)
os.mkdir(prefix_path)
else:
os.mkdir(prefix_path)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
for i in tqdm(range(num_images)):
im = cv2.imread(imglist[i])
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
boxes, _, _, classes = convert_from_cls_format(cls_boxes, cls_segms,
cls_keyps)
if classes == []:
continue
voc_boxes = np.zeros_like(boxes)
voc_boxes[:, 0:1] = boxes[:, 4:5]
voc_boxes[:, 1:3] = boxes[:, 0:2] + 1
voc_boxes[:, 3:5] = boxes[:, 2:4] + 1
for instance_idx, cls_idx in enumerate(classes):
cls_name = dataset.classes[cls_idx]
if cls_name == 'motorcycle':
cls_name = 'motorbike'
f = open(os.path.join(prefix_path, cls_name + ".txt"), "a+")
f.write("%s " % im_name)
for item in voc_boxes[instance_idx]:
f.write("%f " % item)
f.write("\n")
f.close()
def main():
"""main function"""
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
params = list(maskRCNN.parameters())
k = 0
for i in params:
l = 1
for j in i.size():
l *= j
k = k + l
print('zonghe:' + str(k))
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
for i in xrange(num_images):
print('img', i)
im = cv2.imread(imglist[i])
assert im is not None
timers = defaultdict(Timer)
start = time.time()
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
class_result_boxes = []
for index, class_boxes in enumerate(cls_boxes):
if len(class_boxes) != 0:
class_boxes = class_boxes.tolist()
results_oneclass = threeD_detect(imglist[i], class_boxes,
index)
class_result_boxes.append(results_oneclass)
save_image = im
color_class = {
'Car': [0, 255, 255],
'Cyclist': [255, 0, 0],
'Pedestrian': [0, 0, 255]
}
for result_boxes in class_result_boxes:
for box in result_boxes:
cv2.rectangle(save_image, (box[0], box[1]), (box[2], box[3]),
color_class[box[-1]], 2)
height = round(box[-2][0], 2)
width = round(box[-2][1], 2)
length = round(box[-2][2], 2)
threeD_info = str(height) + ' ' + str(width) + ' ' + str(
length)
cv2.putText(save_image, threeD_info, (box[0], box[1] - 20),
cv2.FONT_HERSHEY_COMPLEX, 1, (255, 0, 0), 2)
_, imagename = os.path.split(imglist[i])
imagename2 = imagename.split('.')[0]
cv2.imwrite('../output1/%s.png' % imagename2, save_image)
end = time.time()
print(end - start)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
if args.merge_pdfs and num_images > 1:
merge_out_path = '{}/results.pdf'.format(args.output_dir)
if os.path.exists(merge_out_path):
os.remove(merge_out_path)
command = "pdfunite {}/*.pdf {}".format(args.output_dir,
merge_out_path)
subprocess.call(command, shell=True)
def main():
"""main function"""
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
#Do not use RPN.
#cfg.MODEL.FASTER_RCNN = False
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
train_db = davis_db.DAVIS_imdb(split='val')
for seq_idx in range(train_db.get_num_sequence()):
train_db.set_to_sequence(seq_idx)
seq_name = train_db.get_current_seq_name()
save_dir = osp.join(args.output_dir, seq_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
else:
merge_out_path = '{}/results.pdf'.format(save_dir)
if osp.exists(merge_out_path):
continue
for idx in range(train_db.get_current_seq_length()):
im_name = '%02d.pdf' % (idx)
print(osp.join(save_dir, im_name))
if osp.exists(osp.join(save_dir, im_name)):
continue
im = train_db.get_image_cv2(idx)
boxes = train_db.get_bboxes(idx)
new_boxes = []
for bbox in boxes:
new_box = []
new_box.extend(bbox)
new_box[2] = new_box[0] + new_box[2]
new_box[3] = new_box[1] + new_box[3]
new_boxes.append(new_box)
boxes = np.array(new_boxes, np.float32)
print(boxes.shape)
if boxes.shape[0] > 0:
device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
#boxes = torch.tensor(boxes,device = device)
maskRCNN_predictor_with_boxes = Generalized_RCNN_Predictor_with_Boxes(
)
if args.cuda:
maskRCNN_predictor_with_boxes.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(
load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN_predictor_with_boxes,
checkpoint['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN_predictor_with_boxes,
args.load_detectron)
maskRCNN_predictor_with_boxes = mynn.DataParallel(
maskRCNN_predictor_with_boxes,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN_predictor_with_boxes.eval()
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(
maskRCNN_predictor_with_boxes,
im,
timers=timers,
box_proposals=boxes)
else:
cls_boxes = None
cls_segms = None
cls_keyps = None
im_name = '%02d' % (idx)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
save_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.01,
kp_thresh=2)
if args.merge_pdfs:
merge_out_path = '{}/results.pdf'.format(save_dir)
if os.path.exists(merge_out_path):
os.remove(merge_out_path)
command = "pdfunite {}/*.pdf {}".format(save_dir, merge_out_path)
subprocess.call(command, shell=True)
def main():
"""main function"""
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset.startswith("gangjin"):
dataset = datasets.get_gangjin_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
img_ids = []
rects = []
for i in range(num_images):
print('img', i)
im = cv2.imread(imglist[i])
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
boxes, segms, keypoints, classes = vis_utils.convert_from_cls_format(
cls_boxes, cls_segms, cls_keyps)
if boxes is not None:
for j in range(len(boxes)):
# print(boxes[j][-1])
if float(boxes[j][-1]) < 0.99: # 阀值
continue
xmin = float(boxes[j, 0])
xmax = float(boxes[j, 2])
ymin = float(boxes[j, 1])
ymax = float(boxes[j, 3])
img_ids.append(os.path.basename(imglist[i]))
rects.append(
str(xmin) + " " + str(ymin) + " " + str(xmax) + " " +
str(ymax))
# im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
# vis_utils.vis_one_image(
# im[:, :, ::-1], # BGR -> RGB for visualization
# im_name,
# args.output_dir,
# cls_boxes,
# cls_segms,
# cls_keyps,
# dataset=dataset,
# box_alpha=0.3,
# show_class=False,
# thresh=0.99,
# kp_thresh=2,
# ext="jpg"
# )
result_dict = {"ID": img_ids, "rects": rects}
import pandas as pd
result = pd.DataFrame.from_dict(result_dict)
result.to_csv('submit/submit1.csv', header=None, index=False)
def main():
"""main function"""
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
prefix_path = args.output_dir
os.makedirs(prefix_path, exist_ok=True)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
writen_results = []
# validate
demo_im = cv2.imread(imglist[0])
print(np.shape(demo_im))
h, w, _ = np.shape(demo_im)
#print(h)
#print(args.height)
assert h == args.height
assert w == args.width
h_scale = 720 / args.height
w_scale = 1280 / args.width
for i in tqdm(range(num_images)):
im = cv2.imread(imglist[i])
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
# boxs = [[x1, y1, x2, y2, cls], ...]
boxes, _, _, classes = convert_from_cls_format(cls_boxes, cls_segms,
cls_keyps)
if boxes is None:
continue
# scale
boxes[:, 0] = boxes[:, 0] * w_scale
boxes[:, 2] = boxes[:, 2] * w_scale
boxes[:, 1] = boxes[:, 1] * h_scale
boxes[:, 3] = boxes[:, 3] * h_scale
if classes == []:
continue
for instance_idx, cls_idx in enumerate(classes):
cls_name = dataset.classes[cls_idx]
if cls_name == 'motorcycle':
cls_name = 'motor'
elif cls_name == 'stop sign':
cls_name = 'traffic sign'
elif cls_name == 'bicycle':
cls_name = 'bike'
if cls_name not in bdd_category:
continue
writen_results.append({
"name": imglist[i].split('/')[-1],
"timestamp": 1000,
"category": cls_name,
"bbox": boxes[instance_idx, :4],
"score": boxes[instance_idx, -1]
})
with open(os.path.join(prefix_path, args.name + '.json'),
'w') as outputfile:
json.dump(writen_results, outputfile, cls=MyEncoder)
def test_net(
args,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range
)
model = initialize_model_from_cfg(args, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(model, im, box_proposals, timers)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (
timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time
)
misc_time = (
timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + i + 1,
start_ind + num_images, det_time, misc_time, eta
)
)
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True
)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(
all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps
def test_net(args,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
full_roidb, dataset, start_ind, end_ind, total_num_images, total_num_cls, support_dict = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range)
model = initialize_model_from_cfg(args, gpu_id=gpu_id)
base_real_index = full_roidb[start_ind]['real_index']
roidb = full_roidb[start_ind:end_ind]
index_ls = []
for item in roidb:
index_ls.append(item['real_index'])
num_annotations = len(roidb)
num_images = len(list(set(index_ls)))
num_classes = total_num_cls #cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
print('part:', num_images)
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
# Get support box
index = entry['index']
assert len(list(set(entry['gt_classes']))) == 1
query_cls = list(set(entry['gt_classes']))[
0] #index_pd.loc[index_pd['index']==index, 'cls_ls'].tolist()[0]
query_img = entry[
'image'] #index_pd.loc[index_pd['index']==index, 'img_ls'].tolist()[0]
all_cls = support_dict[query_cls]['all_cls']
support_way = 5
support_shot = 5
support_data_all = np.zeros((support_way * support_shot, 3, 320, 320),
dtype=np.float32)
support_box_all = np.zeros((support_way * support_shot, 4),
dtype=np.float32)
support_cls_ls = []
for cls_id, cls in enumerate(all_cls):
begin = cls_id * support_shot
end = (cls_id + 1) * support_shot
support_data_all[begin:end] = support_dict[cls]['img']
support_box_all[begin:end] = support_dict[cls]['box']
support_cls_ls.append(cls)
save_path = './vis'
im = cv2.imread(entry['image'])
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
model, im, support_data_all, support_box_all, support_cls_ls,
support_shot, save_path, box_proposals, timers)
#real_index = entry['real_index']
real_index = entry['real_index'] - base_real_index
cls_boxes_i = cls_boxes_i[1]
for cls in support_cls_ls:
extend_support_results(
real_index, all_boxes,
cls_boxes_i[cls_boxes_i[:, 5] == cls][:, :5], cls)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_annotations - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time)
misc_time = (timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time)
logger.info(('im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})').format(
start_ind + 1, end_ind, total_num_images,
start_ind + i + 1, start_ind + num_annotations,
det_time, misc_time, eta))
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image(im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml), det_file)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps
def main():
"""main function"""
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset == "pascal_parts_heads":
dataset = datasets.get_head_dataset()
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "scuthead_a":
dataset = datasets.get_head_dataset()
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
for i in xrange(num_images):
im = cv2.imread(imglist[i])
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
outputfile = os.path.join(args.output_dir, im_name)
head_boxes = cls_boxes[1]
print('img :', i, ' num_heads :', len(head_boxes), ' img_path :',
imglist[i])
np.save(outputfile, head_boxes)
def test_net(args,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range)
model = initialize_model_from_cfg(args, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
timers = defaultdict(Timer)
if cfg.LESION.USE_POSITION:
true_pos = 0
if cfg.TEST.OFFLINE_MAP and (cfg.DATA_SOURCE == 'mammo'
or cfg.DATA_SOURCE == 'lesion'):
if osp.exists(os.path.join(output_dir, 'ground-truth')):
shutil.rmtree(os.path.join(output_dir, 'ground-truth'))
os.makedirs(os.path.join(output_dir, 'ground-truth'))
if osp.exists(os.path.join(output_dir, 'predicted')):
shutil.rmtree(os.path.join(output_dir, 'predicted'))
os.makedirs(os.path.join(output_dir, 'predicted'))
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
if cfg.DATA_SOURCE == 'coco':
if cfg.LESION.LESION_ENABLED:
im = load_multislice_16bit_png(roidb[i])
else:
im = cv2.imread(roidb[i]['image'])
elif cfg.DATA_SOURCE == 'mammo':
im, shape = get_a_img(roidb[i])
if (cfg.MODEL.LR_VIEW_ON or cfg.MODEL.GIF_ON
or cfg.MODEL.LRASY_MAHA_ON) and cfg.DATA_SOURCE == 'mammo':
other_im = get_b_img(entry, im)
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
model, [im, other_im], box_proposals, timers)
else:
if cfg.LESION.USE_POSITION:
cls_boxes_i, cls_segms_i, cls_keyps_i, return_dict = im_detect_all(
model, im, box_proposals, timers)
bins = np.array((0.58, 0.72, 1))
bin_pred = np.argmax(
return_dict['pos_cls_pred'][0].data.cpu().numpy())
bin_gt = np.digitize(entry['z_position'], bins)
if bin_gt == bin_pred:
true_pos += 1
else:
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
model, im, box_proposals, timers)
if cfg.TEST.OFFLINE_MAP and cfg.DATA_SOURCE == 'mammo':
gt_bboxes = get_vis_gt_bboxes(entry)
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
with open(
os.path.join(output_dir, 'ground-truth', im_name + '.txt'),
'w') as w:
for gt_bbox in gt_bboxes[0]:
w.write('mass %d %d %d %d\n' %
(gt_bbox[0], gt_bbox[1], gt_bbox[2], gt_bbox[3]))
for gt_bbox in gt_bboxes[1]:
w.write('mass %d %d %d %d difficult\n' %
(gt_bbox[0], gt_bbox[1], gt_bbox[2], gt_bbox[3]))
#w.write('mass %d %d %d %d\n'%(gt_bbox[0], gt_bbox[1], gt_bbox[2], gt_bbox[3]))
with open(os.path.join(output_dir, 'predicted', im_name + '.txt'),
'w') as w:
for idx in range(cls_boxes_i[1].shape[0]):
w.write('mass %.5f %d %d %d %d\n' %
(cls_boxes_i[1][idx][4], cls_boxes_i[1][idx][0],
cls_boxes_i[1][idx][1], cls_boxes_i[1][idx][2],
cls_boxes_i[1][idx][3]))
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
if cfg.TEST.VIS_TEST_ONLY:
vis_utils.vis_one_image(im[:, :, ::-1].astype('uint8'),
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True)
else:
if cfg.DATA_SOURCE == 'coco': #or cfg.DATA_SOURCE == 'lesion':
other_im_show = im[:, :, ::-1].astype('uint8')
if cfg.TEST.VIS_SINGLE_SLICE:
other_im_show = cv2.merge([
other_im_show[:, :, 1], other_im_show[:, :, 1],
other_im_show[:, :, 1]
])
gt_boxes_show = [entry['boxes'].tolist(), []]
gt_classes = [entry['gt_classes'].tolist(), []]
elif cfg.DATA_SOURCE == 'mammo':
if (cfg.MODEL.LR_VIEW_ON or cfg.MODEL.GIF_ON
or cfg.MODEL.LRASY_MAHA_ON):
other_im_show = other_im[:, :, ::-1].astype('uint8')
else:
other_im_show = im[:, :, ::-1].astype('uint8')
gt_boxes_show = get_vis_gt_bboxes(entry)
gt_classes = None
im_show = im[:, :, ::-1].astype('uint8')
if cfg.TEST.VIS_SINGLE_SLICE:
im_show = cv2.merge(
[im_show[:, :, 1], im_show[:, :, 1], im_show[:, :, 1]])
vis_gt_utils.vis_one_image(im_show,
other_im_show,
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
gt_boxes_show,
segms=cls_segms_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True,
gt_classes=gt_classes)
if cfg.DATA_SOURCE == 'mammo':
cls_boxes_i = unalign_boxes(
entry, shape, cls_boxes_i) # cls_boxes_i[c]:array n x 5
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
if cfg.DATA_SOURCE == 'mammo':
cls_segms_i = unalign_segms(entry, shape, cls_segms_i)
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 500 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time)
misc_time = (timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time)
logger.info(('im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})').format(
start_ind + 1, end_ind, total_num_images,
start_ind + i + 1, start_ind + num_images,
det_time, misc_time, eta))
if cfg.TEST.OFFLINE_MAP and cfg.DATA_SOURCE == 'mammo':
os.system(
"python ./lib/datasets/map_evaluator.py --output_dir=%s -na -np" %
output_dir)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml), det_file)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
if cfg.LESION.USE_POSITION:
print('####' * 10, true_pos, '/', num_images)
print('position acc: ', float(true_pos) / num_images)
return all_boxes, all_segms, all_keyps
def test_net(args,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range)
model = initialize_model_from_cfg(args, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
timers['im_load'].tic()
# Shape (w, h, 3 * DATA_LOADER.NUM_INPUTS)
im = entry['dataset'].load_image(entry)
im = pack_sequence(im)
timers['im_load'].toc()
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
model, im, box_proposals, timers)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time)
misc_time = (timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time)
logger.info(('im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})').format(
start_ind + 1, end_ind, total_num_images,
start_ind + i + 1, start_ind + num_images,
det_time, misc_time, eta))
if cfg.VIS:
im_name = os.path.splitext(entry['image'])[0].replace(
os.path.sep, '_')
# im_name = os.path.splitext(os.path.basename(entry['image']))[0]
# Use the first input (in case DATA_LOADER.NUM_INPUTS > 1)
im_vis = im[:, :, :3]
vis_utils.vis_one_image(im_vis[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
if hasattr(args, 'objectness_eval') and args.objectness_eval:
all_boxes, all_segms, all_keyps = collapse_categories(
all_boxes, all_segms, all_keyps)
save_object(
dict(all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml), det_file)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps
def test_net(ind_range=None):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert cfg.TEST.WEIGHTS != '', \
'TEST.WEIGHTS must be set to the model file to test'
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
assert cfg.TEST.DATASET != '', \
'TEST.DATASET must be set to the dataset name to test'
output_dir = get_output_dir(training=False)
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
ind_range
)
model = initialize_model_from_cfg()
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps, \
all_refined_segms, all_refined_keyps = \
empty_results(num_classes, num_images)
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
if cfg.MODEL.FASTER_RCNN:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN
box_proposals = None
else:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
im = cv2.imread(entry['image'])
with c2_utils.NamedCudaScope(0):
cls_boxes_i, cls_segms_i, cls_keyps_i, \
cls_refined_segms_i, cls_refined_keyps_i = \
im_detect_all(model, im, box_proposals, timers)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if cls_refined_segms_i is not None:
extend_results(i, all_refined_segms, cls_refined_segms_i)
if cls_refined_keyps_i is not None:
extend_results(i, all_refined_keyps, cls_refined_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (
timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time +
timers['im_detect_refined_mask'].average_time +
timers['im_detect_refined_keypoints'].average_time
)
misc_time = (
timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time +
timers['misc_refined_mask'].average_time +
timers['misc_refined_keypoints'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + i + 1,
start_ind + num_images, det_time, misc_time, eta
)
)
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
# visualize local result
vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis_local'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True
)
# visualize refined result
vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis_refined'),
cls_boxes_i,
segms=cls_refined_segms_i,
keypoints=cls_refined_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True
)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(
all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
all_refined_segms=all_refined_segms,
all_refined_keyps=all_refined_keyps,
cfg=cfg_yaml
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps, all_refined_segms, all_refined_keyps
def test_net(
args,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range
)
model = initialize_model_from_cfg(args, roidb = roidb, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
timers = defaultdict(Timer)
if cfg.TEST.TAGGING:
all_scores = []
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
elif cfg.TEST.USE_GT_PROPOSALS:
box_proposals = entry['boxes'][entry['gt_classes'] > 0]
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
if cfg.TEST.TAGGING:
cls_boxes_i, cls_segms_i, cls_keyps_i, scores = im_detect_all(model, im, entry, box_proposals, timers)
all_scores.append(scores)
else:
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(model, im, entry, box_proposals, timers)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (
timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time
)
misc_time = (
timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + i + 1,
start_ind + num_images, det_time, misc_time, eta
)
)
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True
)
# Evaluate relations
if cfg.TEST.EVALUATE_REL_ACC:
from sklearn.metrics import precision_recall_curve
scores_collect = np.vstack(model.module.Rel_Inf.rel_scores_collect)
gt_collect = np.hstack(model.module.Rel_Inf.rel_gt_collect)
recalls = []
for i in range(scores_collect.shape[-1]):
filt = gt_collect==i
print(i, accuracy(torch.from_numpy(scores_collect[filt]), torch.from_numpy(gt_collect[filt]).long(), (1,2)))
recalls.append(precision_recall_curve(gt_collect==i, scores_collect[:,i])[1].mean())
print(i, recalls[-1])
# how to get recall!
import pdb;pdb.set_trace()
print(np.array(recalls).mean())
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(
all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml,
im_filenames=[entry['image'] for entry in roidb],
classes=dataset.classes
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
if cfg.TEST.TAGGING:
# Save results
tagging_name = 'tagging.pkl' if ind_range is None else 'tagging_range_%s_%s.pkl' % tuple(ind_range)
tagging_file = os.path.join(output_dir, tagging_name)
save_object(
dict(
all_scores=all_scores,
gt_classes=[r['gt_classes'] for r in roidb]
), tagging_file
)
logger.info('Wrote tagging results to: {}'.format(tagging_file))
# Evaluate tagging
all_scores = np.vstack(all_scores)
gt_classes = np.hstack([r['gt_classes'] for r in roidb])
img_id = \
np.hstack([np.ones(len(roidb[i]['gt_classes']))*i for i in range(len(roidb))])
tagging_eval = {}
print('Compute AUSUC')
tagging_eval['ausuc'] = Compute_AUSUC(dataset, all_scores, gt_classes, cfg.TEST.CLASS_SPLIT['source'], cfg.TEST.CLASS_SPLIT['target'])
print('Generalized on all')
tagging_eval['all'] = evaluate(dataset, all_scores, gt_classes)
tagging_eval['all'].update(mean_img_eval(all_scores, gt_classes, img_id))
# Generalized on source
source_filter = np.isin(gt_classes, cfg.TEST.CLASS_SPLIT['source'])
_all_scores, _gt_classes = all_scores[source_filter], gt_classes[source_filter]
_img_id = img_id[source_filter]
if source_filter.any(): # Only when there are source ground truth
print('Generalized on source')
tagging_eval['gen_source'] = evaluate(dataset, _all_scores, _gt_classes)
tagging_eval['gen_source'].update(\
mean_img_eval(_all_scores, _gt_classes, _img_id))
# not generalized on source
inf_scores = np.zeros(all_scores.shape[1])
inf_scores[cfg.TEST.CLASS_SPLIT['target']] = float('-inf')
_all_scores, _gt_classes = all_scores[source_filter]+inf_scores, gt_classes[source_filter]
print('Ungeneralized on source')
tagging_eval['ungen_source'] = evaluate(dataset, _all_scores, _gt_classes)
tagging_eval['ungen_source'].update(\
mean_img_eval(_all_scores, _gt_classes, _img_id))
# The above is showing how target labels are confusing source boxes.
# Generalized on target
target_filter = np.isin(gt_classes, cfg.TEST.CLASS_SPLIT['target'])
_all_scores, _gt_classes = all_scores[target_filter], gt_classes[target_filter]
_img_id = img_id[target_filter]
if target_filter.any(): # Only when there are target ground truth
print('Generalized on target')
tagging_eval['gen_target'] = evaluate(dataset, _all_scores, _gt_classes)
tagging_eval['gen_target'].update(\
mean_img_eval(_all_scores, _gt_classes, _img_id))
# not geralized on target
inf_scores = np.zeros(all_scores.shape[1])
inf_scores[cfg.TEST.CLASS_SPLIT['source']] = float('-inf')
_all_scores, _gt_classes = all_scores[target_filter]+inf_scores, gt_classes[target_filter]
img_id = \
np.hstack([\
np.ones(np.isin(roidb[i]['gt_classes'], cfg.TEST.CLASS_SPLIT['source']).sum())\
for i in range(len(roidb))])
print('Ungeneralized on target')
tagging_eval['ungen_target'] = evaluate(dataset, _all_scores, _gt_classes)
tagging_eval['ungen_target'].update(\
mean_img_eval(_all_scores, _gt_classes, _img_id))
tagging_eval_name = 'tagging_eval.pkl' if ind_range is None else 'tagging_eval_range_%s_%s.pkl' % tuple(ind_range)
tagging_eval_file = os.path.join(output_dir, tagging_eval_name)
save_object(tagging_eval, tagging_eval_file)
logger.info('Wrote tagging eval results to: {}'.format(tagging_eval_file))
# # Pad None for standard coco evaluation
# if ind_range is not None:
# for i in range(len(all_boxes)):
# all_boxes[i] = [None] * start_ind + all_boxes[i] + [None] * (total_num_images - end_id)
return all_boxes, all_segms, all_keyps
def test_net(
args,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range
)
model = initialize_model_from_cfg(args, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes = {}
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes']
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
cls_boxes_i = im_detect_all(model, im, box_proposals, timers)
all_boxes[entry['image']] = cls_boxes_i
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (
timers['im_detect_bbox'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + i + 1,
start_ind + num_images, det_time, eta
)
)
cfg_yaml = yaml.dump(cfg)
if 'train' in dataset_name:
if ind_range is not None:
det_name = 'discovery_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'discovery.pkl'
else:
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(
all_boxes=all_boxes,
cfg=cfg_yaml
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes
def test_net(ind_range=None):
assert cfg.TEST.WEIGHTS != '', \
'TEST.WEIGHTS must be set to the model file to test'
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
assert cfg.TEST.DATASET != '', \
'TEST.DATASET must be set to the dataset name to test'
output_dir = get_output_dir(training=False)
roidb, dataset, start_ind, end_ind, total_num_images = \
get_roidb_and_dataset(ind_range)
model = initialize_model_from_cfg()
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
timers = defaultdict(Timer)
gpu_dev = core.DeviceOption(caffe2_pb2.CUDA, cfg.ROOT_GPU_ID)
name_scope = 'gpu_{}'.format(cfg.ROOT_GPU_ID)
for i, entry in enumerate(roidb):
if cfg.MODEL.FASTER_RCNN:
box_proposals = None
else:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
im = image_utils.read_image_video(entry)
with core.NameScope(name_scope):
with core.DeviceScope(gpu_dev):
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
model, im, box_proposals, timers)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time)
misc_time = (timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time)
logger.info(('im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})').format(
start_ind + 1, end_ind, total_num_images,
start_ind + i + 1, start_ind + num_images,
det_time, misc_time, eta))
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image(im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_THR,
box_alpha=0.8,
dataset=dataset,
show_class=True)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
robust_pickle_dump(
dict(all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml), det_file)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps
def main():
"""main function"""
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
if args.dataset.startswith("coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = len(dataset.classes)
elif args.dataset == "miotcd":
dataset = datasets.get_miotcd_dataset()
cfg.MODEL.NUM_CLASSES = 12
elif args.dataset.startswith("keypoints_coco"):
dataset = datasets.get_coco_dataset()
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset.startswith("bogota"):
dataset = datasets.get_bogota_dataset()
cfg.MODEL.NUM_CLASSES = 12
else:
raise ValueError('Unexpected dataset name: {}'.format(args.dataset))
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN, cpu_keywords=['im_info', 'roidb'],
minibatch=True, device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
dataset_result = {}
for i in xrange(num_images):
print('img', i)
im = cv2.imread(imglist[i])
assert im is not None
try:
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN, im, timers=timers)
boxes_, segme_ , keyps_ ,clasies= convert_from_cls_format(cls_boxes,cls_segms,cls_keyps)
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
dataset_result[im_name] = localize_obj_in_image(im_name,boxes_,clasies)
except(e):
import pdb
pdb.set_trace()
np.save('dictionary_answer.npy', dataset_result)
tmp = args.image_dir.split('/')
txt = str(tmp[-2:])+'_'+str(tmp[-1:])+'.csv'
save_localization_result(dataset_result,txt)
def test_net(
args,
dataset_name,
proposal_file,
output_dir,
ind_range = None,
gpu_id = 0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range
)
# 在这里获得gt的信息
model = initialize_model_from_cfg(args, gpu_id = gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
timers = defaultdict(Timer)
dict_all = {}
if cfg.TEST.IOU_OUT or cfg.FAST_RCNN.FAST_HEAD2_DEBUG:
with open("/nfs/project/libo_i/IOU.pytorch/data/cache/coco_2017_val_gt_roidb.pkl", 'rb') as fp:
cached_roidb = pickle.load(fp)
assert len(roidb) == len(cached_roidb)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
im_name = entry['image'].split('/')[-1][:-4]
cls_boxes_i, cls_segms_i, cls_keyps_i, dict_all[im_name] = im_detect_all(model, im, box_proposals, timers,
im_name_tag = im_name)
if cfg.FAST_RCNN.FAST_HEAD2_DEBUG:
gt_i = cached_roidb[i]['boxes']
shift_gt_iou = predbox_roi_iou(np.array(dict_all[im_name]['stage1_out'], dtype = np.float32),
np.array(gt_i, dtype = "float32"))
dict_all[im_name]['final_iou'] = shift_gt_iou.tolist()
dict_all[im_name]['shift_iou'] = dict_all[im_name]['shift_iou'].tolist()
if cfg.FAST_RCNN.FAST_HEAD2_DEBUG_VIS and i < 100:
if cfg.FAST_RCNN.IOU_NMS:
with open("/nfs/project/libo_i/IOU.pytorch/IOU_Validation/cls_tracker.json", 'r') as f:
cls_tracker = json.load(f)
elif cfg.FAST_RCNN.SCORE_NMS:
with open("/nfs/project/libo_i/IOU.pytorch/IOU_Validation/cls_tracker.json", 'r') as f:
cls_tracker = json.load(f)
# Draw stage1 pred_boxes onto im and gt
dpi = 200
fig = plt.figure(frameon = False)
fig.set_size_inches(im.shape[1] / dpi, im.shape[0] / dpi)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.axis('off')
fig.add_axes(ax)
ax.imshow(im[:, :, ::-1])
# 在im上添加gt
for item in gt_i:
ax.add_patch(
plt.Rectangle((item[0], item[1]),
item[2] - item[0],
item[3] - item[1],
fill = False, edgecolor = 'r',
linewidth = 0.3, alpha = 1))
# 在im上添加proposals
length = len(dict_all[im_name]['boxes'])
for ind in range(length):
# stage1_item = dict_all[im_name]['stage1_pred_boxes'][ind]
stage1_item = dict_all[im_name]['boxes'][ind]
score_item = dict_all[im_name]['score'][ind]
score_item = round(score_item, 2)
ax.add_patch(
plt.Rectangle((stage1_item[0], stage1_item[1]),
stage1_item[2] - stage1_item[0],
stage1_item[3] - stage1_item[1],
fill = False, edgecolor = 'g',
linewidth = 0.5, alpha = 1))
ax.text(
stage1_item[0], stage1_item[1] - 2,
str(score_item),
fontsize = 4,
family = 'serif',
bbox = dict(facecolor = 'g', alpha = 1, pad = 0, edgecolor = 'none'), color = 'white')
length = len(dict_all[im_name]['stage1_out'])
for ind in range(length):
# stage1_item = dict_all[im_name]['stage1_pred_boxes'][ind]
stage1_item = dict_all[im_name]['stage1_out'][ind]
ax.add_patch(
plt.Rectangle((stage1_item[0], stage1_item[1]),
stage1_item[2] - stage1_item[0],
stage1_item[3] - stage1_item[1],
fill = False, edgecolor = 'orange',
linewidth = 0.1, alpha = 0.6))
fig.savefig("/nfs/project/libo_i/IOU.pytorch/2stage_iminfo/{}.png".format(im_name), dpi = dpi)
plt.close('all')
dict_all[im_name].pop('stage1_out')
dict_all[im_name].pop('stage2_out')
dict_all[im_name].pop('stage2_score')
dict_all[im_name].pop('score')
dict_all[im_name].pop('boxes')
if cfg.TEST.IOU_OUT:
gt_i = cached_roidb[i]['boxes']
# NMS
keep = np.array(dict_all[im_name]['keep'])
dict_all[im_name]['shift_iou'] = np.array(dict_all[im_name]['shift_iou'], dtype = np.float32)[
keep].tolist()
dict_all[im_name]['rois_score'] = np.array(dict_all[im_name]['rois_score'], dtype = np.float32)[
keep].tolist()
dict_all[im_name]['rois'] = np.array(dict_all[im_name]['rois'], dtype = np.float32)[keep].tolist()
pred_boxes_scores = dict_all[im_name]['pred_boxes_scores']
# Thresh filter
iou_thrsh_keep = np.where(np.array(dict_all[im_name]['shift_iou'], dtype = np.float32) >= 0.1)[0]
score_thrsh_keep = np.where(np.array(dict_all[im_name]['rois_score'], dtype = np.float32) >= 0.8)[0]
iou_rois = np.array(dict_all[im_name]['rois'], dtype = np.float32)[iou_thrsh_keep]
score_rois = np.array(dict_all[im_name]['rois'], dtype = np.float32)[score_thrsh_keep]
roi_to_final = predbox_roi_iou(np.array(dict_all[im_name]['rois'], dtype = np.float32),
np.array(gt_i, dtype = "float32"))
iou_final_to_rois = predbox_roi_iou(np.array(gt_i, dtype = "float32"), iou_rois)
score_final_to_rois = predbox_roi_iou(np.array(gt_i, dtype = "float32"), score_rois)
dict_all[im_name]['final_iou'] = roi_to_final.tolist()
dict_all[im_name]['iou_final_vertical'] = iou_final_to_rois.tolist()
dict_all[im_name]['score_final_vertical'] = score_final_to_rois.tolist()
if cfg.TEST.IOU_OUT_VIS:
# 试着画出图像看一看
dpi = 200
fig = plt.figure(frameon = False)
fig.set_size_inches(im.shape[1] / dpi, im.shape[0] / dpi)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.axis('off')
fig.add_axes(ax)
ax.imshow(im[:, :, ::-1])
# 在im上添加gt
for item in gt_i:
ax.add_patch(
plt.Rectangle((item[0], item[1]),
item[2] - item[0],
item[3] - item[1],
fill = False, edgecolor = 'g',
linewidth = 0.6, alpha = 1))
# 在im上添加proposals
cnt = 0
for ind, item in enumerate(dict_all[im_name]['rois']):
iou_value = dict_all[im_name]['shift_iou'][ind]
if iou_value > 0.8:
cnt += 1
ax.add_patch(
plt.Rectangle((item[0], item[1]),
item[2] - item[0],
item[3] - item[1],
fill = False, edgecolor = 'orange',
linewidth = 0.5, alpha = 1))
ax.text(
item[0], item[1] - 2,
str(round(iou_value, 2)),
fontsize = 4,
family = 'serif',
bbox = dict(
facecolor = 'g', alpha = 1, pad = 0, edgecolor = 'none'),
color = 'white')
for ind, item in enumerate(dict_all[im_name]['pred_boxes']):
score_value = dict_all[im_name]['pred_boxes_scores'][ind]
if score_value > 0.5:
cnt += 1
ax.add_patch(
plt.Rectangle((item[0], item[1]),
item[2] - item[0],
item[3] - item[1],
fill = False, edgecolor = 'red',
linewidth = 0.5, alpha = 1))
ax.text(
item[0], item[1] - 2,
str(round(score_value, 2)),
fontsize = 4,
family = 'serif',
bbox = dict(
facecolor = 'red', alpha = 1, pad = 0, edgecolor = 'none'),
color = 'white')
print("Here is {} proposals above 0.5 in im {}".format(cnt, im_name))
fig.savefig("/nfs/project/libo_i/IOU.pytorch/im_out/{}.png".format(im_name), dpi = dpi)
plt.close('all')
dict_all[im_name].pop('rois')
dict_all[im_name].pop('pred_boxes')
dict_all[im_name].pop('keep')
if i == 100:
method = "IOU_Exp"
if cfg.FAST_RCNN.IOU_NMS:
method = "FPN_IOU_NMS"
elif cfg.FAST_RCNN.SCORE_NMS:
method = "FPN_SCORE_NMS"
with open("/nfs/project/libo_i/IOU.pytorch/IOU_Validation/{}.json".format(method), 'w') as f:
f.write(json.dumps(dict_all))
print("In {} round, saved dict_all ".format(i))
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds = int(eta_seconds)))
det_time = (
timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time
)
misc_time = (
timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + i + 1,
start_ind + num_images, det_time, misc_time, eta
)
)
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms = cls_segms_i,
keypoints = cls_keyps_i,
thresh = cfg.VIS_TH,
box_alpha = 0.8,
dataset = dataset,
show_class = True
)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(
all_boxes = all_boxes,
all_segms = all_segms,
all_keyps = all_keyps,
cfg = cfg_yaml
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps
def test_net(
weights_file,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0
):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range
)
model = initialize_model_from_cfg(weights_file, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
timers = defaultdict(Timer)
anchor_blobs = _create_anchors()
with c2_utils.NamedCpuScope(gpu_id):
for k, v in anchor_blobs.items():
workspace.FeedBlob(core.ScopedName(k), v)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
with c2_utils.NamedCudaScope(gpu_id):
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
model, im, box_proposals, timers
)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (
timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time
)
misc_time = (
timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + i + 1,
start_ind + num_images, det_time, misc_time, eta
)
)
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True
)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = cfg.CFG_FILE + '_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(
all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps
for seq_idx in range(db.get_num_sequence()):
db.set_to_sequence(seq_idx)
seq_name = db.get_current_seq_name()
cur_output_dir = osp.join(args.output_dir, seq_name)
if args.no_overwrite is True and osp.exists(
osp.join(cur_output_dir, 'results.pdf')):
continue
if not osp.isdir(cur_output_dir):
os.makedirs(cur_output_dir)
assert (cur_output_dir)
for idx in range(db.get_current_seq_length()):
im = db.get_image_cv2(idx)
assert im is not None
timers = defaultdict(Timer)
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
im_name = '%03d-%03d' % (seq_idx, idx)
print(osp.join(seq_name, im_name))
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
cur_output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
def main():
"""main function"""
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
args = parse_args()
print('Called with args:')
print(args)
assert args.image_dir or args.images
assert bool(args.image_dir) ^ bool(args.images)
dataset = datasets.get_hospital_dataset()
cfg.MODEL.NUM_CLASSES = 20 # with bg
num_class = cfg.MODEL.NUM_CLASSES
sents = dataset.sents
th_cls = dataset.th_cls
cls2eng = dataset.cls2eng
eng2type = dataset.eng2type
print('load cfg from file: {}'.format(args.cfg_file))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
assert bool(args.load_ckpt) ^ bool(args.load_detectron), \
'Exactly one of --load_ckpt and --load_detectron should be specified.'
cfg.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = False # Don't need to load imagenet pretrained weights
assert_and_infer_cfg()
maskRCNN = Generalized_RCNN()
if args.cuda:
maskRCNN.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
print("loading checkpoint %s" % (load_name))
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.load_detectron:
print("loading detectron weights %s" % args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True,
device_ids=[0]) # only support single GPU
maskRCNN.eval()
if args.image_dir:
imglist = misc_utils.get_imagelist_from_dir(args.image_dir)
else:
imglist = args.images
num_images = len(imglist)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
for i in xrange(num_images): # for each image
print('img', i)
im = cv2.imread(imglist[i])
assert im is not None
# segmentation
# d = segment(im)
# pdb.set_trace()
timers = defaultdict(Timer)
# detection
cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN,
im,
timers=timers)
# first we collect boxes from all classes
dets_total = np.empty([0, 6], dtype=np.float32)
for cls in range(1, num_class): # for each cls
dets = cls_boxes[cls]
if dets.shape[0] == 0:
continue
dets_extend = np.pad(
dets,
((0, 0),
(0, 1)), # add 0 rows above, below and left, but 1 row right
mode='constant',
constant_values=cls) # append cls to dets
dets_total = np.vstack((dets_total, dets_extend))
# then use a loose NMS to make each region has only one symptom
keep = box_utils.nms(dets_total, 0.7)
nms_dets = dets_total[keep, :]
# iterate through remained boxes
report, healthy = '', True
have_sym_of_cls = [False for _ in range(num_class)]
n = nms_dets.shape[0]
final_results = [] # return to the web
for idx in range(n): # for each region
th, cls = nms_dets[idx, -2], int(nms_dets[idx, -1])
if th > th_cls[cls]: # diagnosed to have the sym
report += sents[cls][1]
have_sym_of_cls[cls] = True
healthy = False
ename = cls2eng[int(cls)]
_type = eng2type[ename]
final_results.append({
'name': ename,
'type': _type,
'box': list(nms_dets[idx, 0:4])
})
for cls in range(1, num_class): # for each cls
if not have_sym_of_cls[cls]: # if have no sym of this cls
report += sents[cls][0]
if healthy:
report = sents[0][0]
print(report)
pdb.set_trace()
# healthy = True # flag indicating healthy or not
# for cls in range(1, num_class): # for each cls
# dets = cls_boxes[cls]
# if dets.shape[0] == 0:
# report += sents[cls][0]
# continue
# n = dets.shape[0]
# flag = False # indicates if the sym exists
# for k in range(n): # for each region
# if dets[k, -1] > th_cls[cls]: # above threshold for this cls, means have this cls of symptom
# report += sents[cls][1]
# flag = True
# healthy = False
# if not flag: # don't have this symptom
# report += sents[cls][0]
#
# if healthy: # use the report for healthy people
# report = sents[0][0]
im_name, _ = os.path.splitext(os.path.basename(imglist[i]))
# vis_utils.vis_one_image(
# im[:, :, ::-1], # BGR -> RGB for visualization
# im_name,
# args.output_dir,
# cls_boxes,
# cls_segms,
# cls_keyps,
# dataset=dataset,
# box_alpha=0.3,
# show_class=True,
# thresh=0.05,
# kp_thresh=2
# )
if args.merge_pdfs and num_images > 1:
merge_out_path = '{}/results.pdf'.format(args.output_dir)
if os.path.exists(merge_out_path):
os.remove(merge_out_path)
command = "pdfunite {}/*.pdf {}".format(args.output_dir,
merge_out_path)
subprocess.call(command, shell=True)
def test_net( # child func
args,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range)
model = initialize_model_from_cfg(args, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
res_dict = {}
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
print(i)
tmp = {'boxes': entry['gt_boxes'], 'clses': entry['cls_list']}
res_dict[entry['eva_id']] = tmp
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
if cfg.HISTO_EQUAL: # LJY
gray = cv2.equalizeHist(im[:, :, 0])
im = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
model, im, box_proposals, timers)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time)
misc_time = (timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time)
logger.info(('im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})').format(
start_ind + 1, end_ind, total_num_images,
start_ind + i + 1, start_ind + num_images,
det_time, misc_time, eta))
# if cfg.VIS:
if False:
# im_name = os.path.splitext(os.path.basename(entry['image']))[0]
im_name = str(entry['eva_id'])
vis_utils.vis_one_image_orig(im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=0.5,
box_alpha=0.8,
dataset=dataset,
show_class=True)
# LJY: save detection file !!!
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml), det_file)
with open(os.path.join(output_dir, 'res_dict.json'), 'w') as f:
json.dump(res_dict, f, ensure_ascii=False)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps
def test_net(ind_range=None):
assert cfg.TEST.WEIGHTS != '', \
'TEST.WEIGHTS must be set to the model file to test'
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
assert cfg.TEST.DATASET != '', \
'TEST.DATASET must be set to the dataset name to test'
output_dir = get_output_dir(training=False)
roidb, dataset, start_ind, end_ind, total_num_images = \
get_roidb_and_dataset(ind_range)
model = initialize_model_from_cfg()
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
timers = defaultdict(Timer)
gpu_dev = core.DeviceOption(caffe2_pb2.CUDA, cfg.ROOT_GPU_ID)
name_scope = 'gpu_{}'.format(cfg.ROOT_GPU_ID)
for i, entry in enumerate(roidb):
if cfg.MODEL.FASTER_RCNN:
box_proposals = None
else:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
im = image_utils.read_image_video(entry)
with core.NameScope(name_scope):
with core.DeviceScope(gpu_dev):
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
model, im, box_proposals, timers)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time)
misc_time = (timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time)
logger.info(
('im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})').format(
start_ind + 1, end_ind, total_num_images,
start_ind + i + 1, start_ind + num_images,
det_time, misc_time, eta))
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image(
im[:, :, ::-1], '{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'), cls_boxes_i,
segms=cls_segms_i, keypoints=cls_keyps_i,
thresh=cfg.VIS_THR,
box_alpha=0.8, dataset=dataset, show_class=True)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
robust_pickle_dump(
dict(all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml),
det_file)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps
