def load_image(image_path):
cfg = get_cfg()
# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
cfg.merge_from_file(
model_zoo.get_config_file(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model
# Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as well
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
predictor = DefaultPredictor(cfg)
object_classes = MetadataCatalog.get(cfg.DATASETS.TRAIN[0]).thing_classes
cnn = EncoderCNN().to(device)
image_for_cnn = Image.open(image_path).convert("RGB")
image_for_cnn = transform(image_for_cnn)
image_features = cnn(image_for_cnn.unsqueeze(0).to(device))
image_for_predictor = cv2.imread(image_path)
object_detectection = predictor(image_for_predictor)
object_label_class_idxs = object_detectection["instances"].pred_classes
object_labels = list(
set([object_classes[item] for item in object_label_class_idxs]))
split_object_labels = []
for label in object_labels:
# a label can be multiple words (e.g. sports ball). We want to have these as individal words (e.g. ["sports", "ball"])
split_label = label.split()
split_object_labels.extend(split_label)
split_object_labels = list(set(split_object_labels))
return image_features, " ".join(split_object_labels)
def main():
# Dataset
register_coco_instances('tiny-pascal', {},
DATA_ROOT + '/pascal_train.json',
DATA_ROOT + '/train_images/')
# Config
cfg = get_cfg()
cfg.merge_from_file(
model_zoo.get_config_file(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.DATASETS.TRAIN = ('tiny-pascal', )
cfg.DATASETS.TEST = ()
cfg.SOLVER.IMS_PER_BATCH = 4
cfg.SOLVER.BASE_LR = 0.001
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 20
cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 20
cfg.MODEL.META_ARCHITECTURE = 'PatchRCNN'
cfg.MODEL.WEIGHTS = './models/R-50.pkl'
# model weights: https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md
cfg.INPUT.MAX_SIZE_TRAIN = 500
cfg.INPUT.MIN_SIZE_TRAIN = (300, 500)
cfg.OUTPUT_DIR = './models'
# trainer
DefaultTrainer.build_train_loader = mapper_train_loader
trainer = DefaultTrainer(cfg)
trainer.resume_or_load()
trainer.train()
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args, project_id, data_dir=None):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
config_file = MODEL_NAME['Mask_RCNN']
cfg.merge_from_file(config_file)
cfg.merge_from_list(args.opts)
cfg.SOLVER.BASE_LR = 0.00025
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
cfg.MODEL.MASK_ON = True
cfg.OUTPUT_DIR = os.path.join('/media/tangyp/Data/model_file/OUTPUT_DIR',
'project' + project_id)
register_all_cityscapes(data_dir)
cfg.DATASETS.TEST = ["cityscapes_fine2_instance_seg_val"]
cfg.DATASETS.TRAIN = ["cityscapes_fine2_instance_seg_train"]
default_setup(cfg, args)
return cfg
def setup(args,project_id,model_name,num_classes=80, lr=0.00025,data_dir=None,pre_cfg=None):
"""
Create configs and perform basic setups.
"""
if data_dir is not None:
register_all_cityscapes(data_dir)
pre_cfg.DATASETS.TRAIN = ["cityscapes_fine2_instance_seg_train"]
pre_cfg.DATASETS.TEST = ["cityscapes_fine2_instance_seg_val"]
return pre_cfg
cfg = get_cfg()
config_file = MODEL_NAME[model_name]
cfg.merge_from_file(config_file)
cfg.merge_from_list(args.opts)
cfg.SOLVER.BASE_LR = lr
cfg.MODEL.ROI_HEADS.NUM_CLASSES = num_classes
cfg.MODEL.MASK_ON = True
cfg.OUTPUT_DIR = os.path.join('/media/tangyp/Data/model_file/OUTPUT_DIR','project'+project_id)
default_setup(cfg, args)
return cfg
def setup(args,
project_id,
coco_data,
model_config,
create_new_folder=True,
train_size=None):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
config_file = MODEL_NAME[model_config]
cfg.merge_from_file(config_file)
cfg.merge_from_list(args.opts)
cfg.SOLVER.BASE_LR = 0.00025
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
cfg.MODEL.MASK_ON = True
if create_new_folder:
folder_num = get_folder_num(project_id=project_id)
cfg.OUTPUT_DIR = os.path.join(OUTPUT_DIR, 'project_' + project_id,
folder_num)
else:
cfg.OUTPUT_DIR = os.path.join(OUTPUT_DIR, 'project_' + project_id)
register_coco_instances(name='coco_train',
json_file=coco_data[0]['json_file'],
image_root=coco_data[0]['image_root'])
register_hw_instances(name='hw_train', file_path=tiny_train, train=True)
register_hw_instances(name='hw_val', file_path=tiny_val, train=False)
register_coco_instances(name='coco_val',
json_file=coco_data[1]['json_file'],
image_root=coco_data[1]['image_root'])
# cfg.DATASETS.TEST = ['coco_val']
cfg.DATASETS.TEST = ['hw_val']
# cfg.DATASETS.TRAIN = ['coco_train']
cfg.DATASETS.TRAIN = ['hw_train']
if train_size is not None:
cfg.SOLVER.MAX_ITER = int((270000 * train_size) / 45174)
cfg.SOLVER.STEPS = (int(cfg.SOLVER.MAX_ITER * 0.78),
int(cfg.SOLVER.MAX_ITER * 0.925))
default_setup(cfg, args)
return cfg
def setup(args,
project_id,
model_name,
num_classes=80,
lr=0.00025,
data_dir=None):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
if data_dir is not None:
DatasetCatalog.register(
"custom", lambda data_dir=data_dir: get_custom_dicts(data_dir))
cfg.DATASETS.TRAIN = ("custom", )
config_file = MODEL_NAME[model_name]
cfg.merge_from_file(config_file)
cfg.merge_from_list(args.opts)
cfg.SOLVER.BASE_LR = lr
cfg.MODEL.ROI_HEADS.NUM_CLASSES = num_classes
cfg.OUTPUT_DIR = os.path.join('/media/tangyp/Data/model_file/OUTPUT_DIR',
'project' + project_id)
default_setup(cfg, args)
return cfg
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--model",
default='helmet_tracking/model_final_13000.pth',
type=str, #required=True,
help="path to base D2 model or s3 location")
parser.add_argument(
"--reid_model",
default='helmet_tracking/ResNet_iter_25137.pth',
type=str, #required=True,
help="path to reid model or s3 location")
parser.add_argument(
"--output_dir",
default='/home/model_results',
type=str, #required=True,
help=
"The output directory where the model checkpoints and predictions will be written."
)
parser.add_argument("--bucket",
default='privisaa-bucket-virginia',
type=str)
parser.add_argument(
"--img_paths",
default='nfl-data/live_video',
type=str,
#required=True,
help="path to images or image location in s3")
parser.add_argument(
"--conf_thresh",
default=.5,
type=float, #required=True,
help="base D2 model")
parser.add_argument("--use_mask", default=0, type=int)
# parser.add_argument("--video", default=None, type=str, required=True, help="path to video for tracking job")
parser.add_argument(
"--d2_config",
default=
'/home/detectron2-ResNeSt/configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml',
type=str,
help="Detectron2 config file")
args = parser.parse_args()
#COCO-Detection/faster_cascade_rcnn_ResNeSt_101_FPN_syncbn_range-scale_1x.yaml'
confidence_threshold = .55
cfg = get_cfg()
config_file = args.d2_config
model_pth = args.model
reid_pth = args.reid_model
try:
os.mkdir('/home/video')
except:
pass
if os.path.exists(args.model) == False:
print('\n')
print('downloading d2 model from s3')
s3.download_file(args.bucket, args.model, '/home/d2_final.pth')
model_pth = '/home/d2_final.pth'
if os.path.exists(args.reid_model) == False:
print('\n')
print('downloading reid model from s3')
s3.download_file(args.bucket, args.reid_model, '/home/reid_final.pth')
reid_pth = '/home/reid_final.pth'
if os.path.exists(args.img_paths) == False:
objs = s3.list_objects(Bucket='privisaa-bucket-virginia',
Prefix=args.img_paths)['Contents']
keys = []
for key in objs:
if key['Size'] > 0:
keys.append(key['Key'])
folders = []
for key in keys:
folders.append(key.split('/')[-2])
folder = list(np.unique(folders))[-1]
print('\n')
print('Loading images from this video: ', folder)
for key in tqdm(keys):
if key.split('/')[-2] == folder:
s3.download_file(args.bucket, key,
f"/home/video/{key.split('/')[-1]}")
cfg.merge_from_file(config_file)
cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.conf_thresh
cfg.MODEL.WEIGHTS = model_pth #'/home/ubuntu/finetuned-fasterrcnn-cascade-d2-resnest-13000imgs-005lr-1class-tune2/model_final.pth'
#finetuned-fasterrcnn-cascade-d2-resnest-13000imgs-02lr/model_final.pth'
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.conf_thresh
cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.conf_thresh
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 256
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
model = DefaultPredictor(cfg)
#model.model.load_state_dict(torch.load('finetuned-detectron2-maskrcnn_13000imgs_03lr_3rdRun/model_final.pth')['model'])
eval_results = model.model.eval(
) # finetuned-detectron2-maskrcnn_fixed/model_final.pth')['model'])
# 'finetuned-detectron2-maskrcnn_fixed/model_final.pth'
# finetuned-detectron2-maskrcnn_5700imgs_025lr_furthertuned/model_final.pth
if args.use_mask == 0:
obj_detect = MRCNN_FPN(model, num_classes=1)
else:
obj_detect = FRCNN_FPN(model, num_classes=1)
#obj_detect.to(device)
# reid
reid_network = resnet50(pretrained=False,
output_dim=128) # need to insert dictionary here
reid_network.load_state_dict(
torch.load(
reid_pth, #tracktor['reid_weights'],'/home/ubuntu/code/tracking_wo_bnw/output/tracktor/reid/test/ResNet_iter_25137.pth'
map_location=device)) #lambda storage, loc: storage))
reid_network.eval()
#reid_network.cuda()
reid_network.to(device)
tracker = {
# FRCNN score threshold for detections
'detection_person_thresh': 0.5,
# FRCNN score threshold for keeping the track alive
'regression_person_thresh': 0.5,
# NMS threshold for detection
'detection_nms_thresh': 0.3,
# NMS theshold while tracking
'regression_nms_thresh': 0.6,
# motion model settings
'motion_model': {
'enabled': True,
# average velocity over last n_steps steps
'n_steps': 2,
# if true, only model the movement of the bounding box center. If false, width and height are also modeled.
'center_only': True
},
# DPM or DPM_RAW or 0, raw includes the unfiltered (no nms) versions of the provided detections,
# 0 tells the tracker to use private detections (Faster R-CNN)
'public_detections': False,
# How much last appearance features are to keep
'max_features_num': 20,
# Do camera motion compensation
'do_align': False,
# Which warp mode to use (cv2.MOTION_EUCLIDEAN, cv2.MOTION_AFFINE, ...)
'warp_mode': str(cv2.MOTION_EUCLIDEAN),
# maximal number of iterations (original 50)
'number_of_iterations': 500,
# Threshold increment between two iterations (original 0.001)
'termination_eps': 0.00001,
# Use siamese network to do reid
'do_reid': True,
# How much timesteps dead tracks are kept and cosidered for reid
'inactive_patience': 15,
# How similar do image and old track need to be to be considered the same person
'reid_sim_threshold': 20.0,
# How much IoU do track and image need to be considered for matching
'reid_iou_threshold': 0.05
}
tracker = IJP_tracker(obj_detect, reid_network, tracker)
# tracker = Tracker(obj_detect, reid_network, tracker)
def transform_img(i, pth='/home/ubuntu/videos/'):
if (i < 10):
ind = f'0000{i}'
elif (i < 100):
ind = f'000{i}'
else:
ind = f'00{i}'
frame = Image.open(f'{pth}split_frames_{ind}.jpg')
frame_ten = torch.tensor(np.reshape(np.array(frame),
(1, 3, 720, 1280)),
device=device,
dtype=torch.float32)
blob = {'img': frame_ten}
return blob
# cannot make frame size assumption...
# need to make this an argparse argument
pth = '/home/video/'
# vid_name = args.video #'57675_003286_Endzone'
img_paths = glob(f'{pth}/*') # {vid_name}
img_paths.sort()
# print(img_paths)
tracker.reset()
# results = []
# print('Starting tracking!')
# for i in tqdm(range(1,len(img_paths)+1)):
# #tracker.reset()
# blob = transform_img(i ,pth=pth)
# with torch.no_grad():
# tracker.step(blob)
track_dict = {}
print("\n")
print("###########################################")
print("############## BEGIN TRACKING #############")
print("###########################################")
print('\n')
for i in tqdm(range(1, len(img_paths) + 1)):
track_dict[i] = {}
blob = transform_img(i, pth=pth)
with torch.no_grad():
tracker.step(blob)
for tr in tracker.tracks:
track_dict[i][tr.id] = tr.pos[0].detach().cpu().numpy()
iou_dict = {}
iou_dict2 = {}
new_tracks = {}
missing_tracks = {}
for track in tqdm(track_dict):
if track == 1: # set dictionaries with 1st frame
new_tracks[track] = list(track_dict[track].keys())
missing_tracks[track] = set()
iou_dict[track] = {}
iou_dict[track] = {}
for tr in track_dict[track]:
iou_dict[track][tr] = {}
else:
new_tracks[track] = set(list(track_dict[track].keys())) - set(
new_tracks[1]) # - set(list(track_dict[track-1].keys()))
missing_tracks[track] = set(new_tracks[1]) - set(
list(track_dict[track - 1].keys()))
iou_dict[track] = {}
iou_dict2[track] = {}
for tr in track_dict[track]:
iou_dict[track][tr] = {}
iou_dict2[track][tr] = {}
for t in track_dict[track - 1]:
iou = bbox_iou(track_dict[track][tr],
track_dict[track - 1][t])
if iou > 0.001:
iou_dict[track][tr][t] = iou
if track > 2:
for t in track_dict[track - 2]:
iou = bbox_iou(track_dict[track][tr],
track_dict[track - 2][t])
if iou > 0.001:
iou_dict2[track][tr][t] = iou
tracks_to_delete = {}
tracks_to_change = {}
momentum_dict = {}
for track in track_dict:
if track == 1:
for tr in track_dict[track]:
momentum_dict[tr] = 0 # initialize momentum dict
elif len(track_dict[track]
) > 22: # if there are more than 22 annotations
tracks_to_delete[track] = []
for tr in track_dict[track]:
try:
momentum_dict[tr] += 1
except:
momentum_dict[tr] = 0
for ind in iou_dict[track]:
if (iou_dict[track][ind] == {}) & (
ind > 22
): # need to adjust this, right now just looking if there is ANY IoU
tracks_to_delete[track].append(ind)
else: # if less than 22 annotations
tracks_to_change[track] = {}
if new_tracks[track] != set(
): # if there are new tracks, check them
for tr in track_dict[track]: # update momentum dict
try:
momentum_dict[tr] += 1
except:
momentum_dict[tr] = 0
for newt in new_tracks[track]: # cycle through new tracks
# print('For track ',track,"and ID ",newt,iou_dict[track][newt])
if newt > 22: # if there is a new track and it's greater than 22, change it, figure out what to change to
if (missing_tracks[track] != set()) | (
missing_tracks[track - 1] != set()
): # if there are missing tracks, cycle through them and compare
mis_iou = {}
if missing_tracks[track] != set():
for mis in missing_tracks[track]:
try:
# iou = bbox_iou(track_dict[track-1][mis], track_dict[track][newt])
dist = distance.euclidean(
track_dict[track - 1][mis],
track_dict[track][newt])
except:
try:
# iou = bbox_iou(track_dict[track-2][mis], track_dict[track][newt])
dist = distance.euclidean(
track_dict[track - 2][mis],
track_dict[track][newt])
except:
try:
# iou = bbox_iou(track_dict[track-3][mis], track_dict[track][newt])
dist = distance.euclidean(
track_dict[track - 3][mis],
track_dict[track][newt])
except:
pass
mis_iou[mis] = dist
# tracks_to_change[track][newt] =
tracks_to_change[track][newt] = mis_iou
# if missing_tracks[track-1]!=set():
# for mis in missing_tracks[track-1]:
# iou = bbox_iou(track_dict[track-2][mis], track_dict[track][newt])
# mis_iou[mis] = iou
# tracks_to_change[track][newt] = mis_iou
# try:
# if max(tracks_to_change[track][newt].values())>.2:
# for t in tracks_to_change[trc][tr]:
to_ind = np.argmin(
list(tracks_to_change[track]
[newt].values()))
to_id = list(tracks_to_change[track]
[newt].keys())[to_ind]
to_pos = track_dict[track][newt]
del track_dict[track][newt]
track_dict[track][to_id] = to_pos
# need to send results to s3
result = tracker.get_results()
file = 'tracking_results.json'
tracking_dict = {}
for res in result:
tracking_dict[res] = {}
for r in result[res]:
tracking_dict[res][r] = list(result[res][r][0:4])
with open(file, 'w') as f:
json.dump(tracking_dict, f)
# now = str(datetime.datetime.now()).replace(' ','').replace(':','-')
k = f'nfl-data/tracking_results_{folder}.json'
s3.upload_file(Filename=file, Bucket=args.bucket, Key=k)
print(f'Tracking finished and results saved to: s3://{args.bucket}/{k}')
os.makedirs('/home/labeled_frames')
# create labeled images
print('\n')
print("###########################################")
print("############ Generating Video! ############")
print("###########################################")
print('\n')
print('...')
for j, pth in tqdm(enumerate(img_paths)):
fig, ax = plt.subplots(1, figsize=(24, 14))
img = Image.open(pth)
# Display the image
ax.imshow(np.array(img))
# Create a Rectangle patch
label_list = {}
for r in track_dict[j + 1]:
try:
res = track_dict[j + 1][r]
label_list[r] = res
except:
pass
for i, r in enumerate(label_list):
labs = label_list[r]
rect = patches.Rectangle((labs[0], labs[1]),
labs[2] - labs[0],
labs[3] - labs[1],
linewidth=1,
edgecolor='r',
facecolor='none') # 50,100),40,30
ax.add_patch(rect)
plt.text(labs[0] - 10, labs[1] - 10, f'H:{r}', fontdict=None)
plt.savefig(
f"/home/labeled_frames/{pth.split('/')[-1].replace('.jpg','.png')}"
)
plt.close()
# create video of labels
os.system(
'ffmpeg -r 15 -f image2 -s 1280x720 -i /home/labeled_frames/split_frames_%05d.png -vcodec libx264 -crf 25 -pix_fmt yuv420p /home/labeled_frames.mp4'
)
k = f'nfl-data/tracking_results_{folder}.mp4'
s3.upload_file(Filename='/home/labeled_frames.mp4',
Bucket=args.bucket,
Key=k)
print('\n')
print(f'Video uploaded to: s3://{args.bucket}/{k}')
# for launching A2I job set a conditional here
s3_fname = f's3://{args.bucket}/{k}'
workteam = 'arn:aws:sagemaker:us-east-1:209419068016:workteam/private-crowd/ijp-private-workteam'
flowDefinitionName = 'ijp-video-flow-official'
humanTaskUiArn = 'arn:aws:sagemaker:us-east-1:209419068016:human-task-ui/ijp-video-task3'
#'s3://privisaa-bucket-virginia/nfl-data/nfl-frames/nfl-video-frame00001.png'
# create_workflow_definition_response = sagemaker_client.create_flow_definition(
# FlowDefinitionName= flowDefinitionName,
# RoleArn= role,
# HumanLoopConfig= {
# "WorkteamArn": workteam,
# "HumanTaskUiArn": humanTaskUiArn,
# "TaskCount": 1,
# "TaskDescription": "Identify if the labels in the video look correct.",
# "TaskTitle": "Video classification a2i demo"
# },
# OutputConfig={
# "S3OutputPath" : OUTPUT_PATH
# }
# )
# flowDefinitionArn = create_workflow_definition_response['FlowDefinitionArn']
inputContent = {
"initialValue": .2,
"taskObject":
s3_fname # the s3 object will be passed to the worker task UI to render
}
now = str(datetime.datetime.now()).replace(' ', '-').replace(':',
'-').replace(
'.', '-')
response = a2i.start_human_loop(
HumanLoopName=f'ijp-video-{now}',
FlowDefinitionArn=flowDefinitionArn,
HumanLoopInput={"InputContent": json.dumps(inputContent)},
DataAttributes={
'ContentClassifiers': ['FreeOfPersonallyIdentifiableInformation']
})
print(f'Launched A2I loop ijp-video-{now}')
sns.publish(
TopicArn='arn:aws:sns:us-east-1:209419068016:ijp-topic',
Message=
f'Your video inference is done! You can find the output here: s3://{args.bucket}/{k}',
Subject='Video labeling')
from tqdm import tqdm
import matplotlib.pyplot as plt
from pycocotools.mask import decode
def show_rle(rel):
m = decode(rel)
print(m.shape)
plt.imshow(m)
plt.show()
DATA_ROOT = '.'
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
register_coco_instances('tiny-pascal', {}, DATA_ROOT + '/pascal_train.json', DATA_ROOT + '/train_images/')
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 20
cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 20
DATA_ROOT = '/home/ccy-gpl/Datasets/HW3'
cfg.DATASETS.TRAIN = ('tiny-pascal', )
MetadataCatalog.get("tiny-pascal").thing_classes = ["aeroplane", "bicycle", 'bird', 'boat', 'bottle', 'bus', 'car',
'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
print(MetadataCatalog.get(cfg.DATASETS.TRAIN[0]))
cfg.MODEL.WEIGHTS = os.path.join("models/model_0199999.pth") # path to the model we just trained
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7 # set a custom testing threshold
predictor = DefaultPredictor(cfg)
# print(predictor.model)
