File size: 9,163 Bytes
105944b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 | import argparse
import json
from pathlib import Path
import cv2
import numpy as np
import torch
from ultralytics import YOLO
def evaluate_model(model_path, data_path, split, image_size, batch_size, device, output_path, plots):
model = YOLO(model_path)
metrics = model.val(
data=data_path,
split=split,
imgsz=image_size,
batch=batch_size,
device=device,
plots=plots,
verbose=False,
)
box_metrics = metrics.box
class_names = [model.names[index] for index in sorted(model.names)]
report = {
"model": str(model_path),
"data": str(data_path),
"split": split,
"classes": class_names,
"aggregate": {
"precision": round(float(box_metrics.mp), 6),
"recall": round(float(box_metrics.mr), 6),
"f1": round(float(2 * box_metrics.mp * box_metrics.mr / (box_metrics.mp + box_metrics.mr)), 6),
"map50": round(float(box_metrics.map50), 6),
"map50_95": round(float(box_metrics.map), 6),
"map75": round(float(box_metrics.map75), 6),
},
"per_class": [],
}
for index, class_name in enumerate(class_names):
report["per_class"].append(
{
"class": class_name,
"precision": round(float(box_metrics.p[index]), 6),
"recall": round(float(box_metrics.r[index]), 6),
"f1": round(float(box_metrics.f1[index]), 6),
"map50": round(float(box_metrics.ap50[index]), 6),
"map50_95": round(float(box_metrics.ap[index]), 6),
}
)
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(json.dumps(report, indent=2), encoding="utf-8")
print(f"Model: {model_path}")
print(f"Split: {split}")
print("\nAggregate metrics")
for metric_name, value in report["aggregate"].items():
print(f"{metric_name}: {value:.4f} ({value * 100:.2f}%)")
print("\nPer-class metrics")
print("Class | Precision | Recall | F1 | mAP50 | mAP50-95")
for class_metrics in report["per_class"]:
print(
f"{class_metrics['class']} | "
f"{class_metrics['precision']:.4f} | "
f"{class_metrics['recall']:.4f} | "
f"{class_metrics['f1']:.4f} | "
f"{class_metrics['map50']:.4f} | "
f"{class_metrics['map50_95']:.4f}"
)
print(f"\nJSON report saved to: {output_path}")
def _make_gradcam(model, image, image_size, device):
"""Create a Grad-CAM overlay from the last feature map before Detect."""
network = model.model
target_layer = network.model[-2]
activations = []
gradients = []
def save_activation(_module, _inputs, output):
activations.append(output)
def save_gradient(gradient):
gradients.append(gradient)
output.register_hook(save_gradient)
handle = target_layer.register_forward_hook(save_activation)
try:
with torch.enable_grad():
resized = cv2.resize(image, (image_size, image_size), interpolation=cv2.INTER_LINEAR)
tensor = torch.from_numpy(resized[:, :, ::-1].copy()).permute(2, 0, 1).float().unsqueeze(0) / 255.0
tensor = tensor.to(device).requires_grad_(True)
network.zero_grad(set_to_none=True)
prediction = network(tensor)
prediction_tensor = prediction[0] if isinstance(prediction, (tuple, list)) else prediction
score = prediction_tensor.max()
score.backward()
activation = activations[0][0]
gradient = gradients[0][0]
weights = gradient.mean(dim=(1, 2), keepdim=True)
cam = torch.relu((weights * activation).sum(dim=0)).detach().cpu().numpy()
cam = cv2.resize(cam, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_LINEAR)
cam -= cam.min()
maximum = cam.max()
if maximum > 0:
cam /= maximum
heatmap = cv2.applyColorMap(np.uint8(cam * 255), cv2.COLORMAP_JET)
overlay = cv2.addWeighted(image, 0.55, heatmap, 0.45, 0)
return overlay
finally:
handle.remove()
def analyze_images(model_path, image_dir, output_dir, count, skip, image_size, device, confidence):
model = YOLO(model_path)
cam_model = YOLO(model_path)
output_dir.mkdir(parents=True, exist_ok=True)
originals_dir = output_dir / "original"
results_dir = output_dir / "result"
gradcam_dir = output_dir / "gradcam"
for directory in (originals_dir, results_dir, gradcam_dir):
directory.mkdir(exist_ok=True)
image_paths = sorted(
path for path in Path(image_dir).iterdir()
if path.suffix.lower() in {".jpg", ".jpeg", ".png", ".bmp", ".webp"}
)[skip:skip + count]
if not image_paths:
raise ValueError(f"No supported images found in {image_dir}")
analysis = []
cam_device = torch.device("cuda" if str(device).isdigit() and torch.cuda.is_available() else "cpu")
cam_model.model.to(cam_device)
cam_model.model.eval()
for image_path in image_paths:
image = cv2.imread(str(image_path))
if image is None:
continue
prediction = model.predict(source=image, conf=confidence, imgsz=image_size, device=device, verbose=False)[0]
result_path = results_dir / image_path.name
cv2.imwrite(str(result_path), prediction.plot())
cv2.imwrite(str(originals_dir / image_path.name), image)
gradcam_path = gradcam_dir / image_path.name
cv2.imwrite(str(gradcam_path), _make_gradcam(cam_model, image, image_size, cam_device))
detections = []
if prediction.boxes is not None:
for box in prediction.boxes:
class_id = int(box.cls.item())
detections.append({
"class": prediction.names[class_id],
"confidence": round(float(box.conf.item()), 6),
})
analysis.append({
"image": str(image_path),
"original": str(originals_dir / image_path.name),
"result": str(result_path),
"gradcam": str(gradcam_path),
"detections": detections,
})
report_path = output_dir / "results.json"
report_path.write_text(json.dumps(analysis, indent=2), encoding="utf-8")
print(f"Analyzed {len(analysis)} images")
print(f"Results saved to: {output_dir}")
print(f"JSON summary saved to: {report_path}")
def main():
parser = argparse.ArgumentParser(description="Evaluate a trained Ultralytics YOLO detection model.")
parser.add_argument("--model", default="runs/plastic_detection/weights/best.pt")
parser.add_argument("--data", default="data.yaml")
parser.add_argument("--split", choices=("train", "val", "test"), default="test")
parser.add_argument("--imgsz", type=int, default=640)
parser.add_argument("--batch", type=int, default=16)
parser.add_argument("--device", default="cpu", help="Use cpu, 0, 1, or another Ultralytics device value.")
parser.add_argument("--output", type=Path, default=Path("runs/plastic_detection/metrics_test.json"))
parser.add_argument("--plots", action="store_true", help="Save confusion matrix and PR/F1 curve plots.")
parser.add_argument("--gradcam", action="store_true", help="Run per-image detection and Grad-CAM analysis.")
parser.add_argument("--image-dir", type=Path, default=Path("test/images"))
parser.add_argument("--num-images", type=int, default=10)
parser.add_argument("--skip-images", type=int, default=0, help="Skip this many sorted images before analysis.")
parser.add_argument("--confidence", type=float, default=0.25)
parser.add_argument("--gradcam-output", type=Path, default=Path("runs/plastic_detection/gradcam_10"))
args = parser.parse_args()
model_path = Path(args.model)
data_path = Path(args.data)
if not model_path.exists():
parser.error(f"Model file not found: {model_path}")
if not data_path.exists():
parser.error(f"Dataset file not found: {data_path}")
if args.gradcam:
if not args.image_dir.exists():
parser.error(f"Image directory not found: {args.image_dir}")
analyze_images(
model_path=model_path,
image_dir=args.image_dir,
output_dir=args.gradcam_output,
count=args.num_images,
skip=args.skip_images,
image_size=args.imgsz,
device=args.device,
confidence=args.confidence,
)
return
evaluate_model(
model_path=model_path,
data_path=data_path,
split=args.split,
image_size=args.imgsz,
batch_size=args.batch,
device=args.device,
output_path=args.output,
plots=args.plots,
)
if __name__ == "__main__":
main()
|