Spaces:
Runtime error
Runtime error
File size: 4,891 Bytes
fbb3995 a697d26 fbb3995 ee9362f fbb3995 ee9362f a697d26 fbb3995 a697d26 fbb3995 ee9362f fbb3995 193f172 214eb25 193f172 ee9362f 193f172 a697d26 193f172 a697d26 fbb3995 a697d26 fbb3995 a697d26 fbb3995 193f172 fbb3995 193f172 fbb3995 ee9362f 193f172 fbb3995 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
import project_path
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.
"""
print("In task...")
print("Cuda available in task?", torch.cuda.is_available())
# setup config
if "conf_threshold" not in config: config['conf_threshold'] = 0.001
if "nms_iou" not in config: config['nms_iou'] = 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
print(config)
model, device = setup_model(args.weights)
locations = [
"kenai-val"
]
for loc in locations:
in_loc_dir = os.path.join(args.frames, loc)
out_loc_dir = os.path.join(args.output, loc)
print(in_loc_dir)
print(out_loc_dir)
detect_location(in_loc_dir, out_loc_dir, config, model, device, verbose)
def detect_location(in_loc_dir, out_loc_dir, config, model, device, 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)
in_seq_dir = os.path.join(in_loc_dir, seq)
out_seq_dir = os.path.join(out_loc_dir, seq)
os.makedirs(out_seq_dir, exist_ok=True)
detect_seq(in_seq_dir, out_seq_dir, config, model, device, verbose)
def detect_seq(in_seq_dir, out_seq_dir, config, model, device, verbose):
ann_list = []
frame_list = detect(in_seq_dir, config, model, device, verbose)
for frame in frame_list:
if frame is not None:
for ann in frame:
ann_list.append({
'image_id': ann[5],
'category_id': 0,
'bbox': [ann[0], ann[1], ann[2] - ann[0], ann[3] - ann[1]],
'score': ann[4]
})
result = json.dumps(ann_list)
with open(os.path.join(out_seq_dir, 'pred.json'), 'w') as f:
f.write(result)
def detect(in_dir, config, model, device, verbose):
#progress_log = lambda p, m: 0
# create dataloader
dataloader = create_dataloader_frames_only(in_dir)
inference, image_shapes, width, height = do_detection(dataloader, model, device, verbose=verbose)
outputs = do_suppression(inference, conf_thres=config['conf_threshold'], iou_thres=config['nms_iou'], verbose=verbose)
file_names = dataloader.files
frame_list = []
for batch_i, batch in enumerate(outputs):
batch_shapes = image_shapes[batch_i]
# Format results
for si, pred in enumerate(batch):
(image_shape, original_shape) = batch_shapes[si]
# Clip boxes to image bounds and resize to input shape
clip_boxes(pred, (height, width))
boxes = pred[:, :4].clone() # xyxy
confs = pred[:, 4].clone().tolist()
scale_boxes(image_shape, boxes, original_shape[0], original_shape[1]) # to original shape
frame = [ [*bb, conf] for bb, conf in zip(boxes.tolist(), confs) ]
file_name = file_names[batch_i*32 + si]
for ann in frame:
ann.append(file_name)
frame_list.append(frame)
return frame_list
def argument_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--frames", required=True, help="Path to frame 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) |