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import project_path
from lib.yolov5.utils.torch_utils import select_device
from lib.yolov5.utils.general import clip_boxes, scale_boxes
import argparse
from datetime import datetime
import torch
import os
from dataloader import create_dataloader_frames_only
from inference import setup_model, do_detection, do_suppression, do_confidence_boost, format_predictions, do_tracking
from visualizer import generate_video_batches
import json
from tqdm import tqdm
import numpy as np
def main(args, config={}, verbose=True):
"""
Main processing task to be run in gradio
- Writes aris frames to dirname(filepath)/frames/{i}.jpg
- Writes json output to dirname(filepath)/{filename}_results.json
- Writes manual marking to dirname(filepath)/{filename}_marking.txt
- Writes video output to dirname(filepath)/{filename}_results.mp4
- Zips all results to dirname(filepath)/{filename}_results.zip
Args:
filepath (str): path to aris file
TODO: Separate into subtasks in different queues; have a GPU-only queue.
"""
# setup config
if "conf_threshold" not in config: config['conf_threshold'] = 0.3#0.001
if "nms_iou" not in config: config['nms_iou'] = 0.3#0.6
if "min_length" not in config: config['min_length'] = 0.3
if "max_age" not in config: config['max_age'] = 20
if "iou_threshold" not in config: config['iou_threshold'] = 0.01
if "min_hits" not in config: config['min_hits'] = 11
if "use_associative" not in config: config['use_associative'] = False
if "boost_power" not in config: config['boost_power'] = 1
if "boost_decay" not in config: config['boost_decay'] = 1
print(config)
locations = [
"kenai-val"
]
for loc in locations:
in_loc_dir = os.path.join(args.detections, loc)
out_loc_dir = os.path.join(args.output, loc, args.tracker, "data")
os.makedirs(out_loc_dir, exist_ok=True)
metadata_path = os.path.join(args.metadata, loc + ".json")
print(in_loc_dir)
print(out_loc_dir)
print(metadata_path)
track_location(in_loc_dir, out_loc_dir, metadata_path, config, verbose)
def track_location(in_loc_dir, out_loc_dir, metadata_path, config, verbose):
seq_list = os.listdir(in_loc_dir)
with tqdm(total=len(seq_list), desc="...", ncols=0) as pbar:
for seq in seq_list:
pbar.update(1)
if (seq.startswith(".")): continue
pbar.set_description("Processing " + seq)
track(in_loc_dir, out_loc_dir, metadata_path, seq, config, verbose)
def track(in_loc_dir, out_loc_dir, metadata_path, seq, config, verbose):
json_path = os.path.join(in_loc_dir, seq, 'pred.json')
inference_path = os.path.join(in_loc_dir, seq, 'inference.pt')
out_path = os.path.join(out_loc_dir, seq + ".txt")
device_name = 'cuda:0' if torch.cuda.is_available() else 'cpu'
device = torch.device(device_name)
inference = torch.load(inference_path, map_location=device)
# read detection
with open(json_path, 'r') as f:
detection = json.load(f)
image_shapes = detection['image_shapes']
width = detection['width']
height = detection['height']
# read metadata
image_meter_width = -1
image_meter_height = -1
with open(metadata_path, 'r') as f:
json_object = json.loads(f.read())
for sequence in json_object:
if sequence['clip_name'] == seq:
image_meter_width = sequence['x_meter_stop'] - sequence['x_meter_start']
image_meter_height = sequence['y_meter_start'] - sequence['y_meter_stop']
outputs = do_suppression(inference, conf_thres=config['conf_threshold'], iou_thres=config['nms_iou'], verbose=verbose)
if config['use_associative']:
do_confidence_boost(inference, outputs, conf_power=config['boost_power'], conf_decay=config['boost_decay'], verbose=verbose)
outputs = do_suppression(inference, conf_thres=config['conf_threshold'], iou_thres=config['nms_iou'], verbose=verbose)
all_preds, real_width, real_height = format_predictions(image_shapes, outputs, width, height, verbose=verbose)
results = do_tracking(all_preds, image_meter_width, image_meter_height, min_length=config['min_length'], max_age=config['max_age'], iou_thres=config['iou_threshold'], min_hits=config['min_hits'], verbose=verbose)
mot_rows = []
for frame in results['frames']:
for fish in frame['fish']:
bbox = fish['bbox']
row = []
right = bbox[0]*real_width
top = bbox[1]*real_height
w = bbox[2]*real_width - bbox[0]*real_width
h = bbox[3]*real_height - bbox[1]*real_height
row.append(str(frame['frame_num'] + 1))
row.append(str(fish['fish_id'] + 1))
row.append(str(int(right)))
row.append(str(int(top)))
row.append(str(int(w)))
row.append(str(int(h)))
row.append("-1")
row.append("-1")
row.append("-1")
row.append("-1")
mot_rows.append(",".join(row))
mot_text = "\n".join(mot_rows)
with open(out_path, 'w') as f:
f.write(mot_text)
def argument_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--detections", required=True, help="Path to frame directory. Required.")
parser.add_argument("--output", required=True, help="Path to output directory. Required.")
parser.add_argument("--metadata", required=True, help="Path to output directory. Required.")
parser.add_argument("--tracker", default='tracker', help="Path to output directory. Required.")
return parser
if __name__ == "__main__":
args = argument_parser().parse_args()
main(args) |