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import project_path
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
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.
"""
print("In task...")
print("Cuda available in task?", torch.cuda.is_available())
# setup config
if "conf_threshold" not in config: config['conf_threshold'] = 0.3
if "nms_iou" not in config: config['nms_iou'] = 0.3
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
print(config)
dirname = args.frames
locations = ["kenai-val"]
for loc in locations:
in_loc_dir = os.path.join(dirname, loc)
out_dir = os.path.join(args.output, loc, "tracker", "data")
metadata_path = os.path.join(args.metadata, loc + ".json")
os.makedirs(out_dir, exist_ok=True)
print(in_loc_dir)
print(out_dir)
print(metadata_path)
# run detection + tracking
model, device = setup_model(args.weights)
seq_list = os.listdir(in_loc_dir)
idx = 1
with tqdm(total=len(seq_list), desc="...", ncols=0) as pbar:
for seq in seq_list:
pbar.update(1)
pbar.set_description("Processing " + seq)
if verbose:
print(" ")
print("(" + str(idx) + "/" + str(len(seq_list)) + ") " + seq)
print(" ")
idx += 1
in_seq_dir = os.path.join(in_loc_dir, seq)
infer_seq(in_seq_dir, out_dir, config, seq, model, device, metadata_path, verbose)
def infer_seq(in_dir, out_dir, config, seq_name, model, device, metadata_path, verbose):
#progress_log = lambda p, m: 0
image_meter_width = -1
image_meter_height = -1
with open(metadata_path, 'r') as f:
json_object = json.loads(f.read())
for seq in json_object:
if seq['clip_name'] == seq_name:
image_meter_width = seq['x_meter_stop'] - seq['x_meter_start']
image_meter_height = seq['y_meter_stop'] - seq['y_meter_start']
if (image_meter_height == -1):
print("No metadata found for file " + seq_name)
return
# create dataloader
dataloader = create_dataloader_frames_only(in_dir)
try:
inference, image_shapes, width, height = do_detection(dataloader, model, device, verbose=verbose)
except:
print("Error in " + seq_name)
with open(os.path.join(out_dir, "ERROR_" + seq_name + ".txt"), 'w') as f:
f.write("ERROR")
return
outputs = do_suppression(inference, conf_thres=config['conf_threshold'], iou_thres=config['nms_iou'], verbose=verbose)
do_confidence_boost(inference, outputs, verbose=verbose)
new_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, new_outputs, width, height)
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(os.path.join(out_dir, seq_name + ".txt"), 'w') as f:
f.write(mot_text)
return
def argument_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--frames", required=True, help="Path to frame directory. Required.")
parser.add_argument("--metadata", required=True, help="Path to metadata directory. Required.")
parser.add_argument("--output", required=True, help="Path to output directory. Required.")
parser.add_argument("--weights", default='models/v5m_896_300best.pt', help="Path to saved YOLOv5 weights. Default: ../models/v5m_896_300best.pt")
return parser
if __name__ == "__main__":
args = argument_parser().parse_args()
main(args) |