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6452a62 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | """Candidate bounding box detector runner (supports remote gpu3 and local execution)."""
from __future__ import annotations
import argparse
import hashlib
import json
import os
from pathlib import Path
import subprocess
import sys
import time
from typing import Dict, List, Optional
from PIL import Image, ImageDraw, ImageFont
# Add repo root to sys.path
REPO_ROOT = Path(__file__).resolve().parents[3]
if str(REPO_ROOT) not in sys.path:
sys.path.append(str(REPO_ROOT))
def draw_proposal_overlay(
image: Image.Image,
proposals: List[Dict],
out_path: Path,
box_color: str = "#2563eb",
label_bg_color: str = "#2563eb",
text_color: str = "#ffffff",
):
"""Render a clean neutral overlay showing bounding boxes and stable proposal IDs."""
overlay = image.copy()
draw = ImageDraw.Draw(overlay)
try:
font = ImageFont.load_default()
except Exception:
font = None
for p in proposals:
l, t, r, b = p["bbox"]
pid = p["id"]
draw.rectangle([l, t, r, b], outline=box_color, width=2)
# Background badge for label
badge_w = 48
badge_h = 16
draw.rectangle([l, max(0, t - badge_h), l + badge_w, t], fill=label_bg_color)
draw.text((l + 3, max(0, t - badge_h) + 2), pid, fill=text_color, font=font)
overlay.save(out_path, format="PNG")
def run_detector_local(
image_path: Path,
weights_path: Path,
device: str = "cpu",
conf: float = 0.10,
iou: float = 0.10,
) -> Dict:
"""Run local detector using YOLOv9Detector or Ultralytics."""
omni_dir = REPO_ROOT / "vlm_testing/click_omniparser/OmniParser"
if str(omni_dir) not in sys.path:
sys.path.append(str(omni_dir))
from util.yolov9 import YOLOv9Detector
im = Image.open(image_path).convert("RGB")
w, h = im.size
weights_hash = hashlib.sha256(weights_path.read_bytes()).hexdigest()
detector = YOLOv9Detector(str(weights_path), device=device)
res = detector.predict(im, imgsz=1280, conf=conf, iou=iou)[0]
proposals = []
for idx, (box, score) in enumerate(zip(res.boxes.xyxy.cpu().tolist(), res.boxes.conf.cpu().tolist()), start=1):
pid = f"d_{idx:04d}"
proposals.append({
"id": pid,
"bbox": [round(float(v), 2) for v in box],
"label": "icon",
"confidence": round(float(score), 4),
})
return {
"detector": "OmniParser-v2.0-icon_detect",
"device": device,
"weights_path": str(weights_path),
"weights_sha256": weights_hash,
"image_size": [w, h],
"conf_threshold": conf,
"iou_threshold": iou,
"proposal_count": len(proposals),
"proposals": proposals,
}
def run_detector_remote_gpu3(
image_path: Path,
remote_host: str = "gpu3",
remote_workdir: str = "/data/harshitj23/screen-understanding",
weights_name: str = "omniparser-detector.pt",
conf: float = 0.10,
iou: float = 0.10,
) -> Dict:
"""Run detector on remote gpu3 via SSH."""
im = Image.open(image_path).convert("RGB")
w, h = im.size
img_hash = hashlib.sha256(image_path.read_bytes()).hexdigest()[:16]
remote_img = f"{remote_workdir}/runs/tmp_{img_hash}.png"
remote_out = f"{remote_workdir}/runs/tmp_{img_hash}_out.json"
# Copy image to remote
subprocess.run(["scp", str(image_path), f"{remote_host}:{remote_img}"], check=True)
remote_script = f"""
source /data/harshitj23/miniforge3/etc/profile.d/conda.sh
conda activate /data/harshitj23/envs/perception-py311
python -c '
import json, hashlib, time
from pathlib import Path
from ultralytics import YOLO
from PIL import Image
weights_file = Path("{remote_workdir}/models/{weights_name}")
weights_hash = hashlib.sha256(weights_file.read_bytes()).hexdigest()
model = YOLO(str(weights_file))
im = Image.open("{remote_img}").convert("RGB")
res = model.predict(im, imgsz=1280, conf={conf}, iou={iou}, device=0, verbose=False)[0]
proposals = []
for idx, (b, s, c) in enumerate(zip(res.boxes.xyxy.cpu().tolist(), res.boxes.conf.cpu().tolist(), res.boxes.cls.cpu().tolist()), start=1):
proposals.append({{
"id": f"d_{{idx:04d}}",
"bbox": [round(float(v), 2) for v in b],
"label": model.names[int(c)],
"confidence": round(float(s), 4)
}})
output = {{
"detector": "{weights_name}",
"device": "cuda:0",
"weights_sha256": weights_hash,
"image_size": list(im.size),
"conf_threshold": {conf},
"iou_threshold": {iou},
"proposal_count": len(proposals),
"proposals": proposals
}}
Path("{remote_out}").write_text(json.dumps(output, indent=2))
'
"""
subprocess.run(["ssh", remote_host, remote_script], check=True)
# Copy back output
import tempfile
with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as tmp:
tmp_path = Path(tmp.name)
try:
subprocess.run(["scp", f"{remote_host}:{remote_out}", str(tmp_path)], check=True)
data = json.loads(tmp_path.read_text())
finally:
if tmp_path.exists():
tmp_path.unlink()
subprocess.run(["ssh", remote_host, f"rm -f {remote_img} {remote_out}"])
return data
def detect_proposals(
image_path: Path,
out_dir: Path,
use_remote: bool = True,
local_weights: Optional[Path] = None,
) -> Dict:
"""Main entry to run detector and save raw.json and overlay.png."""
out_dir.mkdir(parents=True, exist_ok=True)
raw_json_path = out_dir / "raw.json"
overlay_path = out_dir / "overlay.png"
result = None
if use_remote:
try:
result = run_detector_remote_gpu3(image_path)
except Exception as e:
print(f"[Warning] Remote gpu3 detector failed: {e}. Falling back to local execution.")
if result is None:
if local_weights is None:
local_weights = REPO_ROOT / "vlm_testing/click_omniparser/OmniParser/weights/icon_detect_v3/model.pt"
result = run_detector_local(image_path, local_weights, device="cpu")
raw_json_path.write_text(json.dumps(result, indent=2) + "\n")
# Render overlay
im = Image.open(image_path).convert("RGB")
draw_proposal_overlay(im, result["proposals"], overlay_path)
return result
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--image", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--local", action="store_true")
args = parser.parse_args()
res = detect_proposals(args.image, args.output_dir, use_remote=not args.local)
print(f"Detected {res['proposal_count']} proposals. Saved to {args.output_dir}")
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