def get_detections(cfg, ckpt):
model = SSDDetector(cfg)
model = torch_utils.to_cuda(model)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
dataset_path = DatasetCatalog.DATASETS["tdt4265_test"]["data_dir"]
dataset_path = pathlib.Path(cfg.DATASET_DIR, dataset_path)
image_path = pathlib.Path(dataset_path, "images")
image_paths = list(image_path.glob("*.jpg"))
transforms = build_transforms(cfg, is_train=False)
model.eval()
detections = []
labels = read_labels(
image_path.parent.parent.joinpath("train", "labels.json"))
check_all_images_exists(labels, image_paths)
for i, image_path in enumerate(
tqdm.tqdm(image_paths, desc="Inference on images")):
image_detections = {"image_id": image_path.stem, "bounding_boxes": []}
image = np.array(Image.open(image_path).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
result = model(torch_utils.to_cuda(images))[0]
result = result.resize((width, height)).cpu().numpy()
boxes, labels, scores = result['boxes'], result['labels'], result[
'scores']
for idx in range(len(boxes)):
box = boxes[idx]
label_id = labels[idx]
label = TDT4265Dataset.class_names[label_id]
assert label != "__background__"
score = float(scores[idx])
assert box.shape == (4, )
json_box = {
"xmin": float(box[0]),
"ymin": float(box[1]),
"xmax": float(box[2]),
"ymax": float(box[3]),
"label": str(label),
"label_id": int(label_id),
"confidence": float(score)
}
image_detections["bounding_boxes"].append(json_box)
detections.append(image_detections)
return detections
def make_data_loader(cfg,
is_train=True,
distributed=False,
max_iter=None,
start_iter=0):
train_transform = build_transforms(cfg, is_train=is_train)
target_transform = build_target_transform(cfg) if is_train else None
dataset_list = cfg.DATASETS.TRAIN if is_train else cfg.DATASETS.TEST
datasets = build_dataset(dataset_list,
transform=train_transform,
target_transform=target_transform,
is_train=is_train)
shuffle = is_train or distributed
data_loaders = []
for dataset in datasets:
if distributed:
sampler = samplers.DistributedSampler(dataset, shuffle=shuffle)
elif shuffle:
sampler = torch.utils.data.RandomSampler(dataset)
else:
sampler = torch.utils.data.sampler.SequentialSampler(dataset)
batch_size = cfg.SOLVER.BATCH_SIZE if is_train else cfg.TEST.BATCH_SIZE
batch_sampler = torch.utils.data.sampler.BatchSampler(
sampler=sampler, batch_size=batch_size, drop_last=False)
print('max_iter', max_iter)
if max_iter is not None:
batch_sampler = samplers.IterationBasedBatchSampler(
batch_sampler, num_iterations=max_iter, start_iter=start_iter)
torch.manual_seed(3)
data_loader = DataLoader(dataset,
num_workers=cfg.DATA_LOADER.NUM_WORKERS,
batch_sampler=batch_sampler,
pin_memory=cfg.DATA_LOADER.PIN_MEMORY,
collate_fn=BatchCollator(is_train),
worker_init_fn=_init_fn)
# data_loader = DataLoader(dataset, num_workers=cfg.DATA_LOADER.NUM_WORKERS, batch_sampler=batch_sampler,
# pin_memory=cfg.DATA_LOADER.PIN_MEMORY, collate_fn=BatchCollator(is_train))
data_loaders.append(data_loader)
if is_train:
# during training, a single (possibly concatenated) data_loader is returned
assert len(data_loaders) == 1
return data_loaders[0]
return data_loaders
def run_demo(cfg, ckpt, score_threshold, images_dir: pathlib.Path,
output_dir: pathlib.Path, dataset_type):
if dataset_type == "voc":
class_names = VOCDataset.class_names
elif dataset_type == "mnist":
class_names = MNISTDetection.class_names
else:
raise NotImplementedError('Not implemented now.')
model = SSDDetector(cfg)
model = torch_utils.to_cuda(model)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
image_paths = list(images_dir.glob("*.png")) + list(
images_dir.glob("*.jpg"))
output_dir.mkdir(exist_ok=True, parents=True)
transforms = build_transforms(cfg, is_train=False)
model.eval()
drawn_images = []
for i, image_path in enumerate(
tqdm.tqdm(image_paths, desc="Predicting on images")):
image_name = image_path.stem
image = np.array(Image.open(image_path).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
result = model(torch_utils.to_cuda(images))[0]
result = result.resize((width, height)).cpu().numpy()
boxes, labels, scores = result['boxes'], result['labels'], result[
'scores']
indices = scores > score_threshold
boxes = boxes[indices]
labels = labels[indices]
scores = scores[indices]
drawn_image = draw_boxes(image, boxes, labels, scores,
class_names).astype(np.uint8)
drawn_images.append(drawn_image)
im = Image.fromarray(drawn_image)
output_path = output_dir.joinpath(f"{image_name}.png")
im.save(output_path)
return drawn_images
def get_detections(cfg, ckpt):
model = SSDDetector(cfg)
model = torch_utils.to_cuda(model)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
dataset_path = DatasetCatalog.DATASETS["tdt4265_test"]["data_dir"]
dataset_path = pathlib.Path(cfg.DATASET_DIR, dataset_path)
image_dir = pathlib.Path(dataset_path)
image_paths = list(image_dir.glob("*.jpg"))
transforms = build_transforms(cfg, is_train=False)
model.eval()
detections = []
for image_path in tqdm.tqdm(image_paths, desc="Inference on images"):
image = np.array(Image.open(image_path).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
result = model(torch_utils.to_cuda(images))[0]
result = result.resize((width, height)).cpu().numpy()
boxes, labels, scores = result['boxes'], result['labels'], result[
'scores']
for idx in range(len(boxes)):
box = boxes[idx]
label_id = labels[idx]
label = TDT4265Dataset.class_names[label_id]
assert label != "__background__"
score = float(scores[idx])
assert box.shape == (4, )
xmin, ymin, xmax, ymax = box
width = xmax - xmin
height = ymax - ymin
detections.append({
"image_id": image_path.stem,
"category_id": LABEL_MAP[label],
"score": score,
"bbox": [xmin, ymin, width, height]
})
return detections
def __init__(self):
self.threshold = 0.5
self.device = torch.device('cpu')
self.class_names = VOCDataset.class_names
ssd_dir = os.path.expanduser(rospy.get_param('~model_path'))
config = os.path.join(ssd_dir,
'configs/mobilenet_v2_ssd320_voc0712.yaml')
weightfile = os.path.join(ssd_dir,
'weight/mobilenet_v2_ssd320_voc0712_v2.pth')
cfg.merge_from_file(config)
cfg.freeze()
self.model = self.get_model(cfg, weightfile)
self.transforms = build_transforms(cfg, is_train=False)
self.model.eval()
self.sub = rospy.Subscriber("preprocessed_image", Image,
self.object_detection)
self.pub = rospy.Publisher('object_detection_result',
ObjectDetectionResult,
queue_size=10)
def update_config(self,
config_file,
dataset_type,
weight,
score_threshold=0.5,
targets=["person"]):
if dataset_type == "voc":
self.class_names = VOCDataset.class_names
elif dataset_type == "coco":
self.class_names = COCODataset.class_names
else:
raise NotImplementedError('Not implemented now.')
self.target_labels = []
if targets is None:
self.target_labels = [i for i in range(len(self.class_names))]
else:
for idx in range(len(self.class_names)):
if self.class_names[idx] in targets:
self.target_labels.append(idx)
self.cfg = cfg
self.cfg.merge_from_file(config_file)
self.cfg.freeze()
print("Loaded configuration file {}".format(config_file))
with open(config_file, "r") as cf:
config_str = "\n" + cf.read()
# print(config_str)
# print("Running SSD with config:\n{}".format(self.cfg))
self.device = torch.device(self.cfg.MODEL.DEVICE)
self.model = build_detection_model(self.cfg)
self.model = self.model.to(self.device)
self.checkpointer = CheckPointer(self.model)
self.checkpointer.load(weight)
self.cpu_device = torch.device("cpu")
self.score_threshold = score_threshold
self.transforms = build_transforms(self.cfg, is_train=False)
self.model.eval()
def run_demo(cfg, ckpt, score_threshold, images_dir, dataset_type):
if dataset_type == "voc":
class_names = VOCDataset.class_names
elif dataset_type == "pick":
class_names = PICKDataset.class_names
elif dataset_type == "cotb":
class_names = COTBDataset.class_names
elif dataset_type == 'coco':
class_names = COCODataset.class_names
else:
raise NotImplementedError('Not implemented now.')
device = torch.device(cfg.MODEL.DEVICE)
model = build_detection_model(cfg)
model = model.to(device)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
train_epoch = weight_file.split('/')[2]
train_epoch = train_epoch.split('.')[0].split('_')[1]
save_path = os.path.join('demo', dataset_type, cfg.MODEL.BACKBONE.NAME,
train_epoch)
image_paths = glob.glob(os.path.join(images_dir, '*.jpg')) + glob.glob(
os.path.join(images_dir, '*.jpeg'))
mkdir(save_path)
cpu_device = torch.device("cpu")
transforms = build_transforms(cfg, is_train=False)
model.eval()
for i, image_path in enumerate(image_paths):
start = time.time()
image_name = os.path.basename(image_path)
image = np.array(Image.open(image_path).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
load_time = time.time() - start
start = time.time()
result = model(images.to(device))[0]
inference_time = time.time() - start
result = result.resize((width, height)).to(cpu_device).numpy()
boxes, labels, scores = result['boxes'], result['labels'], result[
'scores']
indices = scores > score_threshold
boxes = boxes[indices]
labels = labels[indices]
scores = scores[indices]
meters = ' | '.join([
'objects {:02d}'.format(len(boxes)),
'load {:03d}ms'.format(round(load_time * 1000)),
'inference {:03d}ms'.format(round(inference_time * 1000)),
'FPS {}'.format(round(1.0 / inference_time))
])
print('({:04d}/{:04d}) {}: {}'.format(i + 1, len(image_paths),
image_name, meters))
drawn_image = draw_boxes(image, boxes, labels, scores,
class_names).astype(np.uint8)
Image.fromarray(drawn_image).save(os.path.join(save_path, image_name))
class_name = {
'__background__', 'lubang', 'retak aligator', 'retak melintang',
'retak memanjang'
}
cfg.merge_from_file(config)
cfg.freeze()
ckpt = None
device = torch.device('cpu')
model = build_detection_model(cfg)
model.to(device)
checkpoint = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpoint.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpoint.get_checkpoint_file()
transforms = build_transforms(cfg, is_train=False)
model.eval()
conv_layers = []
model_children = list(model.children())
print(len(model_children))
print(type(model_children[0]))
print(type(model_children[1]))
counter = 0
for i in range(len(model_children)):
if type(model_children[i]) == VGG:
counter += 1
conv_layers.append(model_children[i])
def active_train(cfg, args):
logger = logging.getLogger("SSD.trainer")
raw_model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
raw_model.to(device)
lr = cfg.SOLVER.LR * args.num_gpus
optimizer = make_optimizer(cfg, raw_model, lr)
milestones = [step // args.num_gpus for step in cfg.SOLVER.LR_STEPS]
scheduler = make_lr_scheduler(cfg, optimizer, milestones)
arguments = {"iteration": 0}
checkpointer = None
save_to_disk = dist_util.get_rank() == 0
checkpointer = CheckPointer(raw_model, optimizer, scheduler,
args.model_dir, save_to_disk, logger)
max_iter = cfg.SOLVER.MAX_ITER // args.num_gpus
is_train = True
train_transform = build_transforms(cfg, is_train=is_train)
target_transform = build_target_transform(cfg) if is_train else None
dataset_list = cfg.DATASETS.TRAIN if is_train else cfg.DATASETS.TEST
datasets = build_dataset(dataset_list,
transform=train_transform,
target_transform=target_transform,
is_train=is_train)
logger.info(f'Creating query loader...')
query_loader = QueryLoader(datasets[0], args, cfg)
logger.info(f'Creating al model...')
strategy = get_strategy(args.strategy)
model = ALModel(raw_model, strategy, optimizer, device, scheduler,
arguments, args, checkpointer, cfg)
logger.info(f'Training on initial data with size {args.init_size}...')
n_bbox = query_loader.len_annotations()
t1 = time.time()
model.fit(query_loader.get_labeled_loader())
init_time = time.time() - t1
logger.info(f'Scoring after initial training...')
score = model.score()
logger.info(f'SCORE : {score:.4f}')
fields = [
args.strategy, {}, 0, score, init_time, 0, init_time,
len(query_loader), n_bbox
]
save_to_csv(args.filename, fields)
for step in range(args.query_step):
logger.info(f'STEP NUMBER {step}')
logger.info('Querying assets to label')
t1 = time.time()
query_idx = model.query(
unlabeled_loader=query_loader.get_unlabeled_loader(),
cfg=cfg,
args=args,
step=step,
n_instances=args.query_size,
length_ds=len(datasets[0]))
logger.info('Adding labeled samples to train dataset')
query_loader.add_to_labeled(query_idx, step + 1)
t2 = time.time()
logger.info('Fitting with new data...')
model.fit(query_loader.get_labeled_loader())
total_time = time.time() - t1
train_time = time.time() - t2
active_time = total_time - train_time
logger.info('Scoring model...')
score = model.score()
n_bbox = query_loader.len_annotations()
fields = [
args.strategy, {}, step + 1, score, train_time, active_time,
total_time,
len(query_loader), n_bbox
]
save_to_csv(args.filename, fields)
logger.info(f'SCORE : {score:.4f}')
return model.model
def _read_image(self, image_id):
image_file = os.path.join(self.data_dir, "JPEGImages",
"%s.jpg" % image_id)
image = Image.open(image_file).convert("RGB")
image = np.array(image)
return image
if __name__ == '__main__':
from ssd.config import cfg
from ssd.data.transforms import build_transforms, build_target_transform
from ssd.data.datasets import build_dataset
is_train = True
train_transform = build_transforms(cfg, is_train=is_train)
target_transform = build_target_transform(cfg) if is_train else None
dataset_list = cfg.DATASETS.TRAIN if is_train else cfg.DATASETS.TEST
datasets = build_dataset(dataset_list,
transform=train_transform,
target_transform=target_transform,
is_train=is_train)
image, targets, index = datasets[0].__getitem__(200)
boxes = targets['boxes']
labels = targets['labels']
print(image.shape)
print(boxes.shape)
print(labels.shape)
print(index)
def run_demo(cfg, ckpt, score_threshold, images_dir, output_dir, dataset_type):
if dataset_type == "voc":
class_names = VOCDataset.class_names
elif dataset_type == 'coco':
class_names = COCODataset.class_names
else:
raise NotImplementedError('Not implemented now.')
device = torch.device(cfg.MODEL.DEVICE)
smoke_name_dic = ('__background__', '一次性快餐盒', '书籍纸张', '充电宝', '剩饭剩菜', '包',
'垃圾桶', '塑料器皿', '塑料玩具', '塑料衣架', '大骨头', '干电池', '快递纸袋',
'插头电线', '旧衣服', '易拉罐', '枕头', '果皮果肉', '毛绒玩具', '污损塑料',
'污损用纸', '洗护用品', '烟蒂', '牙签', '玻璃器皿', '砧板', '筷子', '纸盒纸箱',
'花盆', '茶叶渣', '菜帮菜叶', '蛋壳', '调料瓶', '软膏', '过期药物', '酒瓶',
'金属厨具', '金属器皿', '金属食品罐', '锅', '陶瓷器皿', '鞋', '食用油桶', '饮料瓶',
'鱼骨')
model = build_detection_model(cfg)
cpu_device = torch.device("cpu")
model = model.to(device)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
image_paths = glob.glob(os.path.join(images_dir, '*.jpg'))
mkdir(output_dir)
transforms = build_transforms(cfg, is_train=False)
model.eval()
miss = 0
for i, image_path in enumerate(image_paths):
start = time.time()
image_name = os.path.basename(image_path)
cv_image = cv2.imread(image_path)
PIL_image = Image.open(image_path)
image = np.array(Image.open(image_path).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
load_time = time.time() - start
start = time.time()
result = model(images.to(device))[0]
inference_time = time.time() - start
result = result.resize((width, height)).to(cpu_device).numpy()
boxes, labels, scores = result['boxes'], result['labels'], result[
'scores']
indices = scores > score_threshold
boxes = boxes[indices]
labels = labels[indices]
scores = scores[indices]
miss = miss + (1 - len(boxes))
meters = ' | '.join([
'objects {:02d}'.format(len(boxes)),
'load {:03d}ms'.format(round(load_time * 1000)),
'inference {:03d}ms'.format(round(inference_time * 1000)),
'FPS {}'.format(round(1.0 / inference_time))
])
print('({:04d}/{:04d}) {}: {}'.format(i + 1, len(image_paths),
image_name, meters))
draw_ = ImageDraw.Draw(PIL_image)
for c in range(len(scores)):
text = smoke_name_dic[labels[c]]
font = ImageFont.truetype(
'/usr/share/fonts/truetype/arphic/uming.ttc', 40)
draw_.text((int(boxes[c][0]) + 2, int(boxes[c][1]) - 2),
text, (255, 0, 0),
font=font)
cv_image = cv2.cvtColor(np.asarray(PIL_image), cv2.COLOR_RGB2BGR)
for c in range(len(scores)):
cv2.rectangle(cv_image, (int(boxes[c][0]), int(boxes[c][1])),
(int(boxes[c][2]), int(boxes[c][3])), (0, 0, 255), 4)
cv2.imwrite(os.path.join(output_dir, image_name), cv_image)
smoke_count = len(image_paths)
print("出现：%d 漏掉： %d 漏检率：%.2f" % (smoke_count, miss, miss / smoke_count))
def run_demo(cfg, model, score_threshold, images_dir, output_dir):
device = torch.device(cfg.MODEL.DEVICE)
class_names = VOCDataset.class_names
mkdir(output_dir)
cpu_device = torch.device("cpu")
transforms = build_transforms(cfg, is_train=False)
model.eval()
start = time.time()
image_name = os.path.basename(images_dir)
image = np.array(Image.open(images_dir).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
load_time = time.time() - start
start = time.time()
result = model(images.to(device))[0]
inference_time = time.time() - start
result = result.resize((width, height)).to(cpu_device).numpy()
boxes, labels, scores = result['boxes'], result['labels'], result['scores']
indices = scores > score_threshold
boxes = boxes[indices]
labels = labels[indices]
meters = ' | '.join([
'objects {:02d}'.format(len(boxes)),
'load {:03d}ms'.format(round(load_time * 1000)),
'inference {:03d}ms'.format(round(inference_time * 1000)),
'FPS {}'.format(round(1.0 / inference_time))
])
print('({:04d}) {}: {}'.format(len(images_dir), image_name, meters))
text = ['__background__']
resDic = {}
for j in range(len(boxes)):
xmin = int(boxes[j, 0])
ymin = int(boxes[j, 1])
xmax = int(boxes[j, 2])
ymax = int(boxes[j, 3])
if labels[j] == 1:
xmin += 140
xmax -= 130
elif labels[j] == 2:
xmin += 130
elif labels[j] == 4:
xmin += 40
hight = ymax - ymin
width = xmax - xmin
cropImg = image[ymin:ymin + hight, xmin:xmin + width]
cropImg = local_threshold(cropImg)
text_tmp = crnnOcr(Image.fromarray(cropImg))
if labels[j] == 2:
text_tmp = re.sub('[^\x00-\xff]', '/', text_tmp)
text.append(text_tmp)
resDic[class_names[labels[j]]] = text_tmp
return json.dumps(resDic, ensure_ascii=False).encode('utf-8')
def main():
st.title('Pavement Distress Detector')
st.markdown(get_file_content_as_string('./introduction.md'))
st.sidebar.markdown(get_file_content_as_string('./documentation.md'))
caching.clear_cache()
video = video_uploader('./input')
config = config_uploader('./configs')
output_dir = checkpoint_folder('./outputs')
filename = f"{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}_{os.path.splitext(os.path.basename(config))[0]}"
output_file = './results'
#score_threshold = st.slider('Confidence Threshold', 0.0, 1.0, 0.5)
#fps_threshold = st.slider('Counting Every (frames)', 10, 30, 20)
score_threshold = 0.5
fps_threshold = 20
video_filename = f'{output_file}/{filename}.mp4'
labels_filename = f'{output_file}/{filename}.txt'
if st.button('Click here to run'):
if (os.path.isdir(video) == False and os.path.isdir(config) == False
and output_dir != './outputs/'):
class_name = ('__background__', 'lubang', 'retak aligator',
'retak melintang', 'retak memanjang')
cfg.merge_from_file(config)
cfg.freeze()
ckpt = None
device = torch.device(cfg.MODEL.DEVICE)
model = build_detection_model(cfg)
model.to(device)
checkpoint = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpoint.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpoint.get_checkpoint_file()
st.write(f'Loading weight from {weight_file}')
cpu_device = torch.device('cpu')
transforms = build_transforms(cfg, is_train=False)
model.eval()
clip = VideoFileClip(video)
with tempfile.NamedTemporaryFile(
suffix='.avi'
) as temp: #using temporary file because streamlit can't read opencv video result
temp_name = temp.name
pavement_distress(video, clip, fps_threshold, score_threshold,
temp_name, labels_filename, transforms,
model, device, cpu_device, class_name)
result_clip = VideoFileClip(temp_name)
st.write('Please wait, prepraring result...')
result_clip.write_videofile(video_filename)
video_file = open(video_filename, 'rb')
video_bytes = video_file.read()
st.video(video_bytes)
elif (os.path.isdir(video) == True and os.path.isdir(config) == False
and output_dir != './outputs/'):
st.warning('Please select video file')
elif (os.path.isdir(video) == True and os.path.isdir(config) == True
and output_dir != './outputs/'):
st.warning('Please select video file and config file')
elif (os.path.isdir(video) == False and os.path.isdir(config) == True
and output_dir != './outputs/'):
st.warning('Please select config file')
elif (os.path.isdir(video) == True and os.path.isdir(config) == False
and output_dir == './outputs/'):
st.warning('Please select video file and checkpoint folder')
elif (os.path.isdir(video) == False and os.path.isdir(config) == False
and output_dir == './outputs/'):
st.warning('Please select checkpoint folder')
elif (os.path.isdir(video) == False and os.path.isdir(config) == True
and output_dir == './outputs/'):
st.warning('Please select config file and checkpoint folder')
else:
st.warning(
'Please select video file, config file, and checkpoint folder')
def preparingModel(self):
print('Loaded weights from {}'.format(self.weight_file))
self.transforms = build_transforms(self.cfg, is_train=False)
self.model.eval()
self.isReady = True
def run_demo(cfg, ckpt, score_threshold, images_dir, output_dir, dataset_type):
if dataset_type == "voc":
class_names = VOCDataset.class_names
elif dataset_type == 'coco':
class_names = COCODataset.class_names
else:
raise NotImplementedError('Not implemented now.')
device = torch.device(cfg.MODEL.DEVICE)
model = build_detection_model(cfg)
model = model.to(device)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
image_paths = glob.glob(os.path.join(images_dir, '*.jpg'))
mkdir(output_dir)
cpu_device = torch.device("cpu")
transforms = build_transforms(cfg, is_train=False)
model.eval()
_t = {'im_detect': Timer()}
timer = Timer()
timer.tic()
inference_time_list=[]
load_time_list = []
for image_path in image_paths:
start = time.time()
image_name = os.path.basename(image_path)
image = np.array(Image.open(image_path).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
load_time = time.time() - start
load_time_list.append(1000*load_time)
_t['im_detect'].tic()
#start = time.time()
#print('1')
result = model(images.to(device))[0]
#print('2')
result = result.resize((width, height)).to(cpu_device).numpy()
boxes, labels, scores = result['boxes'], result['labels'], result['scores']
indices = scores > score_threshold
boxes = boxes[indices]
labels = labels[indices]
scores = scores[indices]
#inference_time = time.time() - start
inference_time = _t['im_detect'].toc()
#print(1000*(inference_time))
inference_time_list.append(1000*inference_time)
meters = ' | '.join(
[
'objects {:02d}'.format(len(boxes)),
'load {:03d}ms'.format(round(load_time * 1000)),
'inference {:03d}ms'.format(round(inference_time * 1000)),
'FPS {}'.format(round(1.0 / inference_time))
]
)
# print('({:04d}/{:04d}) {}: {}'.format(i + 1, len(image_paths), image_name, meters))
#drawn_image = draw_boxes(image, boxes, labels, scores, class_names).astype(np.uint8)
#Image.fromarray(drawn_image).save(os.path.join(output_dir, image_name))
_t['im_detect'].clear()
N = len(inference_time_list)//2
total_time_list = np.array(inference_time_list) + np.array(load_time_list)
total_time_list.sort()
inference_time_list.sort()
det_time = np.mean(total_time_list[:N])#/BATCH_SIZE
best_det_time = np.min(total_time_list)#/BATCH_SIZE
print("Total test time: %.2f s" % (timer.toc()))
print("\nTotal detection speed: %.1f FPS" % (len(inference_time_list)/timer.toc()))
print("\nAvg detection speed: %.1f FPS" % (1000./det_time))
print("Best detection speed: %.1f FPS" % (1000./best_det_time))
def run_demo(cfg, ckpt, score_threshold, images_dir, output_dir, dataset_type):
if dataset_type == "voc":
class_names = VOCDataset.class_names
elif dataset_type == 'coco':
class_names = COCODataset.class_names
else:
raise NotImplementedError('Not implemented now.')
device = torch.device(cfg.MODEL.DEVICE)
cpu_device = torch.device("cpu")
model = build_detection_model(cfg)
model = model.to(cpu_device)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
image_paths = glob.glob(os.path.join(images_dir, '*.jpg'))
mkdir(output_dir)
transforms = build_transforms(cfg, is_train=False)
model.eval()
for i, image_path in enumerate(image_paths):
start = time.time()
image_name = os.path.basename(image_path)
image = np.array(Image.open(image_path).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
load_time = time.time() - start
start = time.time()
result = model(images.to(cpu_device))[0]
inference_time = time.time() - start
result = result.resize((width, height)).numpy()
boxes, labels, scores = result['boxes'], result['labels'], result[
'scores']
indices = scores > score_threshold
boxes = boxes[indices]
labels = labels[indices]
scores = scores[indices]
meters = ' | '.join([
'objects {:02d}'.format(len(boxes)),
'load {:03d}ms'.format(round(load_time * 1000)),
'inference {:03d}ms'.format(round(inference_time * 1000)),
'FPS {}'.format(round(1.0 / inference_time))
])
print('({:04d}/{:04d}) {}: {}'.format(i + 1, len(image_paths),
image_name, meters))
for i in range(len(labels)):
text = str(label_name[labels[i]]) + str(round(scores[i], 2))
cv2.rectangle(image, tuple(boxes[i][:2]), tuple(boxes[i][2:]),
color, 3)
image = Image.fromarray(image)
draw = ImageDraw.Draw(image)
draw.text(tuple([boxes[i][0], boxes[i][1] - 40]),
text,
color,
font=fontStyle)
image = np.asarray(image)
cv2.imshow('drawn_image', image)
# drawn_image = draw_boxes(image, boxes, labels, scores, class_names).astype(np.uint8)
Image.fromarray(image).save(os.path.join(output_dir, image_name))
def run_demo(cfg, ckpt, score_threshold, images_dir, output_dir):
class_names = VOCDataset.class_names
device = torch.device(cfg.MODEL.DEVICE)
model = build_detection_model(cfg)
model = model.to(device)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
image_paths = glob.glob(os.path.join(images_dir, '*.bmp'))
mkdir(output_dir)
cpu_device = torch.device("cpu")
transforms = build_transforms(cfg, is_train=False)
model.eval()
for i, image_path in enumerate(image_paths):
start = time.time()
image_name = os.path.basename(image_path)
image = np.array(Image.open(image_path).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
load_time = time.time() - start
start = time.time()
result = model(images.to(device))[0]
inference_time = time.time() - start
result = result.resize((width, height)).to(cpu_device).numpy()
boxes, labels, scores = result['boxes'], result['labels'], result[
'scores']
indices = scores > score_threshold
boxes = boxes[indices]
labels = labels[indices]
meters = ' | '.join([
'objects {:02d}'.format(len(boxes)),
'load {:03d}ms'.format(round(load_time * 1000)),
'inference {:03d}ms'.format(round(inference_time * 1000)),
'FPS {}'.format(round(1.0 / inference_time))
])
print('({:04d}/{:04d}) {}: {}'.format(i + 1, len(image_paths),
image_name, meters))
text = ['__background__']
resDic = {}
for j in range(len(boxes)):
xmin = int(boxes[j, 0])
ymin = int(boxes[j, 1])
xmax = int(boxes[j, 2])
ymax = int(boxes[j, 3])
if labels[j] == 1:
xmin += 140
xmax -= 130
elif labels[j] == 2:
xmin += 130
elif labels[j] == 4:
xmin += 40
hight = ymax - ymin
width = xmax - xmin
cropImg = image[ymin:ymin + hight, xmin:xmin + width]
cropImg = local_threshold(cropImg)
boxes[j, 0] = xmin
boxes[j, 1] = ymin
boxes[j, 2] = xmax
boxes[j, 3] = ymax
text_tmp = crnnOcr(Image.fromarray(cropImg))
if labels[j] == 2:
text_tmp = re.sub('[^\x00-\xff]', '/', text_tmp)
text.append(text_tmp)
resDic[class_names[labels[j]]] = text_tmp
result = json.dumps(resDic, ensure_ascii=False)
print(result)
def run_demo(cfg, ckpt, score_threshold, images_dir, output_dir, dataset_type, gen_heatmap):
if dataset_type == "voc":
class_names = VOCDataset.class_names
elif dataset_type == 'coco':
class_names = COCODataset.class_names
else:
raise NotImplementedError('Not implemented now.')
if torch.cuda.is_available():
device = torch.device(cfg.MODEL.DEVICE)
else:
device = torch.device("cpu")
model = build_detection_model(cfg)
model = model.to(device)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
mkdir(output_dir)
cpu_device = torch.device("cpu")
transforms = build_transforms(cfg, is_train=False)
model.eval()
dist_regr_model = DistanceRegrNet(2)
dist_regr_model = load_model_weight(dist_regr_model, device) # load weights
dist_regr_model.eval()
X_scaler = load_standardizer(Standardizer())
person_label_idx = class_names.index('person')
centroid_tracker = CentroidTracker()
capture = cv2.VideoCapture(0)
while capture.isOpened():
ret, frame = capture.read()
single_frame_render_time = 0
if ret:
image = frame
height, width = image.shape[:2]
start_time = time.time()
images = transforms(frame)[0].unsqueeze(0)
result = model(images.to(device))[0]
result = result.resize((width, height)).to(cpu_device).numpy()
single_frame_render_time += round((time.time() - start_time) * 1000, 3)
print(f"MobileNet SSD Inference time {round((time.time() - start_time) * 1000, 3)}ms")
boxes, labels, scores = result['boxes'], result['labels'], result['scores']
# remove all non person class detections
indices = np.logical_and(scores > score_threshold,
labels == person_label_idx)
boxes = boxes[indices]
labels = labels[indices]
scores = scores[indices]
distances = None
# create gaussian mixture models and kde plots only if centers detected
if len(boxes) != 0:
centers = np.apply_along_axis(get_mid_point, 1, boxes)
image = draw_points(image, centers) # draw center points on image
# Distance Regression
start_time = time.time()
# As boxes is in (xmin, ymin, xmax, ymax) format
# X should always have width, height format
width = boxes[:, 2] - boxes[:, 0]
height = boxes[:, 3] - boxes[:, 1]
X = np.column_stack((width, height))
X_scaled = X_scaler.transform(X)
distances = dist_regr_model(torch.Tensor(X_scaled).to(device)).to(cpu_device).numpy()
single_frame_render_time += round((time.time() - start_time) * 1000, 3)
print(f"Distance Regression Inference time {round(time.time() - start_time, 4) * 1000}ms")
# object tracking with centroids
start_time = time.time()
objects = centroid_tracker.update(centers, distances)
# loop over the tracked objects
# for (objectID, centroid) in objects.items():
# print("Center Distances tracked overtime")
# print(centroid_tracker.obj_distance_counts[objectID])
single_frame_render_time += round((time.time() - start_time) * 1000, 3)
print(f"Centroid Tracking Update time {round(time.time() - start_time, 4) * 1000}ms")
if len(centers) > 1:
# reset center point ranges to a min of 0 and max of 100
_x = centers[:, 0]
_y = centers[:, 1]
centers[:, 0] = reset_range(max(_x), min(_x), 100, 0, _x)
centers[:, 1] = reset_range(max(_y), min(_y), 100, 0, _y)
# DBSCAN Clustering
start_time = time.time()
dbscan_center = DBSCAN(eps=18)
dbscan_center.fit(centers)
# print("DBSCAN Clusters", dbscan_center._labels)
# print("Unique number of clusters", len(set(dbscan_center._labels)))
single_frame_render_time += round((time.time() - start_time) * 1000, 3)
print(f"DBSCAN Clustering time {round((time.time() - start_time) * 1000, 3)}ms")
if gen_heatmap:
image = generate_cv2_heatmap(centers, dbscan_center._labels, None, None,
len(set(dbscan_center._labels)),
covariance_type='diag')
cv2.imshow("frame", image)
if not gen_heatmap:
drawn_image = draw_boxes(image, boxes, labels, scores, distances, class_names).astype(np.uint8)
cv2.imshow("frame", drawn_image)
print(f"Total time to render one frame {single_frame_render_time}." +
f"FPS {round(1 / (single_frame_render_time / 1000))}")
key = cv2.waitKey(1)
if key & 0xFF == ord('x'):
break
else:
break
print("Distance counts for tracked objects")
print(centroid_tracker.obj_distance_counts)
write_file = f'{output_dir}/dist_regr_results/{round(time.time())}.txt'
print(f"Writing the distance values to file {write_file}")
os.makedirs(f'{output_dir}/dist_regr_results', exist_ok=True)
with open(write_file, 'w') as fw:
for key, arr in centroid_tracker.obj_distance_counts.items():
arr = [str(v) for v in arr]
fw.write(str(key) + ',' + ','.join(arr))
fw.write('\n')
capture.release()
cv2.destroyAllWindows()
def run_demo(cfg,
ckpt,
score_threshold,
images_dir,
output_dir,
dataset_type,
model_path=None):
if dataset_type == "voc":
class_names = VOCDataset.class_names
elif dataset_type == 'coco':
class_names = COCODataset.class_names
else:
class_names = TxtDataset(dataset_name=dataset_type).class_names
# else:
# raise NotImplementedError('Not implemented now.')
device = torch.device(cfg.MODEL.DEVICE)
model = build_detection_model(cfg)
model = model.to(device)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
##
# model.backbone.bn_fuse()#需要修改demo.py 要bn_fuse 因为fpga端没有bn
# model.to(device)
# ##
if model_path is None:
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
else:
model.load_state_dict(torch.load(model_path))
if cfg.TEST.BN_FUSE is True:
print('BN_FUSE.')
model.backbone.bn_fuse()
model.to(device)
image_paths = glob.glob(os.path.join(images_dir, '*.jpg')) #.png
mkdir(output_dir)
cpu_device = torch.device("cpu")
transforms = build_transforms(cfg, is_train=False)
model.eval()
for i, image_path in enumerate(image_paths):
start = time.time()
image_name = os.path.basename(image_path)
image = np.array(Image.open(image_path).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
load_time = time.time() - start
start = time.time()
result = model(images.to(device))[0]
inference_time = time.time() - start
result = result.resize((width, height)).to(cpu_device).numpy()
boxes, labels, scores = result['boxes'], result['labels'], result[
'scores']
indices = scores > score_threshold
boxes = boxes[indices]
labels = labels[indices]
scores = scores[indices]
meters = ' | '.join([
'objects {:02d}'.format(len(boxes)),
'load {:03d}ms'.format(round(load_time * 1000)),
'inference {:03d}ms'.format(round(inference_time * 1000)),
'FPS {}'.format(round(1.0 / inference_time))
])
print('({:04d}/{:04d}) {}: {}'.format(i + 1, len(image_paths),
image_name, meters))
drawn_image = draw_boxes(image, boxes, labels, scores,
class_names).astype(np.uint8)
Image.fromarray(drawn_image).save(os.path.join(output_dir, image_name))
def run_demo(cfg, ckpt, score_threshold, output_dir, dataset_type):
if dataset_type == "voc":
class_names = VOCDataset.class_names
elif dataset_type == 'coco':
class_names = COCODataset.class_names
else:
raise NotImplementedError('Not implemented now.')
device = torch.device(cfg.MODEL.DEVICE)
model = build_detection_model(cfg)
model = model.to(device)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
cpu_device = torch.device("cpu")
transforms = build_transforms(cfg, is_train=False)
model.eval()
cap = cv2.VideoCapture('parking_lot/13.mp4')
sz = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
fourcc = cv2.VideoWriter_fourcc(*'DIVX')
fps = 50
vout = cv2.VideoWriter('ssd.avi', fourcc, fps, sz, True)
count = 0
# cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
break
else:
count += 1
# if count % 3 == 1:
start = time.time()
image = frame
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
load_time = time.time() - start
start = time.time()
result = model(images.to(device))[0]
inference_time = time.time() - start
result = result.resize((width, height)).to(cpu_device).numpy()
boxes, labels, scores = result['boxes'], result['labels'], result[
'scores']
indices = scores > score_threshold
boxes = boxes[indices]
labels = labels[indices]
obj_dict = Counter(labels)
scores = scores[indices]
meters = ' | '.join([
'objects {:02d}'.format(len(boxes)),
'load {:03d}ms'.format(round(load_time * 1000)),
'inference {:03d}ms'.format(round(inference_time * 1000)),
'FPS {}'.format(round(1.0 / inference_time))
])
print(meters)
# drawn_image = draw_boxes(image, boxes, labels, scores, class_names).astype(np.uint8)
for i in range(len(labels)):
if labels[i] == 3:
text = 'car:' + str(round(scores[i], 2))
cv2.rectangle(image, tuple(boxes[i][:2]),
tuple(boxes[i][2:]), color, 3)
image = Image.fromarray(image)
draw = ImageDraw.Draw(image)
draw.text(tuple([boxes[i][0], boxes[i][1] - 40]),
text,
color,
font=fontStyle)
image = np.asarray(image)
cv2.imshow('drawn_image', image)
vout.write(image)
if count >= 800 or cv2.waitKey(1) & 0xFF == ord('q'):
break
def run_demo(cfg, ckpt, score_threshold, images_dir, output_dir, dataset_type):
if dataset_type == "voc":
class_names = VOCDataset.class_names
elif dataset_type == 'coco':
class_names = COCODataset.class_names
else:
raise NotImplementedError('Not implemented now.')
if torch.cuda.is_available():
device = torch.device(cfg.MODEL.DEVICE)
else:
device = torch.device("cpu")
model = build_detection_model(cfg)
model = model.to(device)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
images_dir = 'datasets/MOT16/train/MOT16-02/img1'
image_paths = sorted(glob.glob(os.path.join(images_dir, '*.jpg')))
mkdir(output_dir)
cpu_device = torch.device("cpu")
transforms = build_transforms(cfg, is_train=False)
model.eval()
person_label_idx = class_names.index('person')
centroid_tracker = CentroidTracker()
wfile = open('py-motmetrics/motmetrics/data/MOT16/predicted/MOT16-02.txt',
'w')
inference_times = []
for i, image_path in enumerate(image_paths):
image_name = os.path.basename(image_path)
start_time = time.time()
image = np.array(Image.open(image_path).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
result = model(images.to(device))[0]
result = result.resize((width, height)).to(cpu_device).numpy()
boxes, labels, scores = result['boxes'], result['labels'], result[
'scores']
# remove all non person class detections
indices = np.logical_and(scores > score_threshold,
labels == person_label_idx)
boxes = boxes[indices]
distances = None
inference_times.append(time.time() - start_time)
print(time.time() - start_time)
if len(boxes) != 0:
centers = np.apply_along_axis(get_mid_point, 1, boxes)
# object tracking with centroids
centroid_tracker.update(centers, distances, boxes)
fnum = int(image_name.split('.')[0])
# loop over the tracked objects
for (objID, bbox_) in centroid_tracker.obj_bbox.items():
xm, ym = bbox_[0], bbox_[1]
w, h = bbox_[2] - bbox_[0], bbox_[3] - bbox_[1]
output = f"{fnum},{objID},{xm},{ym},{w},{h},-1,-1,-1\n"
wfile.write(output)
# drawn_image = draw_boxes(image, boxes, labels, scores, distances, class_names).astype(np.uint8)
# Image.fromarray(drawn_image).save(os.path.join(output_dir, image_name))
framerates = [1 / tm for tm in inference_times]
print(
f"Avg frame rate is {sum(framerates) / len(framerates)} for {len(framerates)} frames"
)
wfile.close()
def run_demo(cfg, ckpt, score_threshold, images_dir, output_dir, onnx_dir, dataset_type):
if dataset_type == "voc":
class_names = VOCDataset.class_names
elif dataset_type == 'coco':
class_names = COCODataset.class_names
else:
raise NotImplementedError('Not implemented now.')
device = torch.device(cfg.MODEL.DEVICE)
device = "cpu" if not torch.cuda.is_available() else device
model = build_detection_model(cfg)
model = model.to(device)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
image_paths = glob.glob(os.path.join(images_dir, '*.jpg'))
mkdir(output_dir)
cpu_device = torch.device("cpu")
transforms = build_transforms(cfg, is_train=False)
model.eval()
# get model ready for onnx export
mkdir(onnx_dir)
model_onnx = build_detection_model(cfg)
model_onnx = model_onnx.to(device)
checkpointer_onnx = CheckPointer(model_onnx, save_dir=cfg.OUTPUT_DIR)
checkpointer_onnx.load(ckpt, use_latest=ckpt is None)
# replace the SSD box head postprocessor with the onnx version for exporting
model_onnx.box_head.post_processor = PostProcessorOnnx(cfg)
model_onnx.eval()
# export with ONNX
# onnx modle takes the name of the pth ckpt file
model_onnx_name = os.path.basename(ckpt).split('.')[0] + ".onnx"
model_onnx_path = os.path.join(onnx_dir, model_onnx_name)
if not os.path.exists(model_onnx_path):
print(f'Model exported as onnx to {model_onnx_path}')
dummy_input = torch.zeros(
[1, 3, cfg.INPUT.IMAGE_SIZE, cfg.INPUT.IMAGE_SIZE]).to(device)
torch.onnx.export(model_onnx,
dummy_input,
model_onnx_path,
export_params=True,
do_constant_folding=True,
opset_version=11,
input_names=['input'],
output_names=['boxes', 'scores', 'labels'],
dynamic_axes={
'input': {0: 'batch_size', 2: "height", 3: "width"}},
verbose=False)
# load exported onnx model for inference test
print(
f'Loading exported onnx model from {model_onnx_path} for inference comparison test')
onnx_runtime_sess = onnxruntime.InferenceSession(model_onnx_path)
for i, image_path in enumerate(image_paths):
start = time.time()
image_name = os.path.basename(image_path)
image = np.array(Image.open(image_path).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
load_time = time.time() - start
start = time.time()
result = model(images.to(device))[0]
inference_time = time.time() - start
result = result.resize((width, height)).to(cpu_device).numpy()
boxes, labels, scores = result['boxes'], result['labels'], result['scores']
indices = scores > score_threshold
boxes, labels, scores = boxes[indices], labels[indices], scores[indices]
meters = ' | '.join(
[
'objects {:02d}'.format(len(boxes)),
'load {:03d}ms'.format(round(load_time * 1000)),
'inference {:03d}ms'.format(round(inference_time * 1000)),
'FPS {}'.format(round(1.0 / inference_time))
]
)
print('Pytorch: ({:04d}/{:04d}) {}: {}'.format(i + 1,
len(image_paths), image_name, meters))
drawn_image = draw_boxes(image, boxes, labels,
scores, class_names).astype(np.uint8)
Image.fromarray(drawn_image).save(
os.path.join(output_dir, "pytorch_" + image_name))
"""
Compute ONNX Runtime output prediction
"""
start = time.time()
ort_inputs = {onnx_runtime_sess.get_inputs()[0].name: np.array(images)}
boxes, scores, labels = onnx_runtime_sess.run(None, ort_inputs)
inference_time = time.time() - start
indices = scores > score_threshold
boxes, labels, scores = boxes[indices], labels[indices], scores[indices]
# resize bounding boxes to size of the original image
boxes[:, 0::2] *= (width)
boxes[:, 1::2] *= (height)
meters = ' | '.join(
[
'objects {:02d}'.format(len(boxes)),
'load {:03d}ms'.format(round(load_time * 1000)),
'inference {:03d}ms'.format(round(inference_time * 1000)),
'FPS {}'.format(round(1.0 / inference_time))
]
)
print('Onnx: ({:04d}/{:04d}) {}: {}'.format(i + 1,
len(image_paths),
image_name, meters))
drawn_image = draw_boxes(image, boxes, labels,
scores, class_names).astype(np.uint8)
Image.fromarray(drawn_image).save(
os.path.join(output_dir, "onnx_" + image_name))
def run_demo(cfg, ckpt, score_threshold, images_dir, output_dir, dataset_type,
gen_heatmap):
if dataset_type == "voc":
class_names = VOCDataset.class_names
elif dataset_type == 'coco':
class_names = COCODataset.class_names
else:
raise NotImplementedError('Not implemented now.')
if torch.cuda.is_available():
device = torch.device(cfg.MODEL.DEVICE)
else:
device = torch.device("cpu")
model = build_detection_model(cfg)
model = model.to(device)
checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(ckpt, use_latest=ckpt is None)
weight_file = ckpt if ckpt else checkpointer.get_checkpoint_file()
print('Loaded weights from {}'.format(weight_file))
image_paths = glob.glob(os.path.join(images_dir, '*.jpg'))
mkdir(output_dir)
cpu_device = torch.device("cpu")
transforms = build_transforms(cfg, is_train=False)
model.eval()
dist_regr_model = DistanceRegrNet(2)
dist_regr_model = load_model_weight(dist_regr_model,
device) # load weights
dist_regr_model.eval()
X_scaler = load_standardizer(Standardizer())
person_label_idx = class_names.index('person')
for i, image_path in enumerate(image_paths):
start = time.time()
image_name = os.path.basename(image_path)
image = np.array(Image.open(image_path).convert("RGB"))
height, width = image.shape[:2]
images = transforms(image)[0].unsqueeze(0)
load_time = time.time() - start
start = time.time()
result = model(images.to(device))[0]
inference_time = time.time() - start
result = result.resize((width, height)).to(cpu_device).numpy()
boxes, labels, scores = result['boxes'], result['labels'], result[
'scores']
# remove all non person class detections
indices = np.logical_and(scores > score_threshold,
labels == person_label_idx)
boxes = boxes[indices]
labels = labels[indices]
scores = scores[indices]
distances = None
# create gaussian mixture models and kde plots only if centers detected
if len(boxes) != 0:
centers = np.apply_along_axis(get_mid_point, 1, boxes)
image = draw_points(image, centers) # draw center points on image
# reset center point ranges to a min of 0 and max of 100
_x = centers[:, 0]
_y = centers[:, 1]
centers[:, 0] = reset_range(max(_x), min(_x), 100, 0, _x)
centers[:, 1] = reset_range(max(_y), min(_y), 100, 0, _y)
# DBSCAN Clustering
start = time.time()
dbscan_center = DBSCAN(eps=18)
dbscan_center.fit(centers)
# print("dbscan clusters", dbscan_center._labels)
# print("Unique number of clusters", len(set(dbscan_center._labels)))
print(
f"DBSCAN clustering time {round((time.time() - start) * 1000, 3)}ms"
)
# Distance Regression
start_time = time.time()
# As boxes is in (xmin, ymin, xmax, ymax) format
# X should always have width, height format
width = boxes[:, 2] - boxes[:, 0]
height = boxes[:, 3] - boxes[:, 1]
X = np.column_stack((width, height))
X_scaled = X_scaler.transform(X)
distances = dist_regr_model(torch.Tensor(X_scaled).to(device))
print(
f"Distance Regr Inference time {round(time.time() - start_time, 4) * 1000}ms"
)
if gen_heatmap:
generate_sns_kde_heatmap(centers[:, 0], centers[:, 1], i,
image_name)
generate_sk_gaussian_mixture(centers,
dbscan_center._labels,
i,
image_name,
len(set(dbscan_center._labels)),
covariance_type='diag')
generate_cv2_heatmap(centers,
dbscan_center._labels,
i,
image_name,
len(set(dbscan_center._labels)),
covariance_type='diag')
meters = ' | '.join([
'objects {:02d}'.format(len(boxes)),
'load {:03d}ms'.format(round(load_time * 1000)),
'inference {:03d}ms'.format(round(inference_time * 1000)),
'FPS {}'.format(round(1.0 / inference_time))
])
print('({:04d}/{:04d}) {}: {}'.format(i + 1, len(image_paths),
image_name, meters))
drawn_image = draw_boxes(image, boxes, labels, scores, distances,
class_names).astype(np.uint8)
Image.fromarray(drawn_image).save(os.path.join(output_dir, image_name))
