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3a66575 | 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 | import argparse
import csv
import json
import os
import cv2
import numpy as np
import torch
import torchvision.transforms as standard_transforms
from PIL import Image
from scipy.optimize import linear_sum_assignment
from scipy.spatial import cKDTree
from models import build_model
class Args:
backbone = "vgg16_bn"
row = 2
line = 2
def load_model(weight_path):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = build_model(Args()).to(device).eval()
if os.path.exists(weight_path):
checkpoint = torch.load(weight_path, map_location=device)
model.load_state_dict(checkpoint["model"])
transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
return model, device, transform
def infer_points(image, model, device, transform, confidence=0.5, magnification=1.5, batch_size=8):
orig_w, orig_h = image.size
patch_size = 512
pad = 256
work_w, work_h = int(orig_w * magnification), int(orig_h * magnification)
scale = min(1.0, 3840 / float(max(work_w, work_h)))
work_w, work_h = int(work_w * scale), int(work_h * scale)
magnification = work_w / float(orig_w)
resample_filter = getattr(Image, "Resampling", Image).LANCZOS if hasattr(Image, "Resampling") else getattr(Image, "ANTIALIAS", 1)
image = image.resize((work_w, work_h), resample_filter)
padded_w = ((work_w + pad * 2 + patch_size - 1) // patch_size) * patch_size
padded_h = ((work_h + pad * 2 + patch_size - 1) // patch_size) * patch_size
padded = Image.new("RGB", (padded_w, padded_h), (0, 0, 0))
padded.paste(image, (pad, pad))
stride = patch_size // 2
jobs = []
for y in range(0, padded_h - stride + 1, stride):
for x in range(0, padded_w - stride + 1, stride):
if x + patch_size <= padded_w and y + patch_size <= padded_h:
jobs.append((x, y, padded.crop((x, y, x + patch_size, y + patch_size))))
all_points = []
for start in range(0, len(jobs), batch_size):
batch = jobs[start:start + batch_size]
samples = torch.stack([transform(patch) for _, _, patch in batch]).to(device)
with torch.inference_mode():
if device.type == "cuda":
with torch.cuda.amp.autocast():
out = model(samples)
else:
out = model(samples)
scores = torch.nn.functional.softmax(out["pred_logits"].float(), -1)[:, :, 1]
pred = out["pred_points"].float()
for idx, (x, y, _) in enumerate(batch):
pts = pred[idx][scores[idx] > confidence].detach().cpu().numpy()
if len(pts):
pts[:, 0] += x - pad
pts[:, 1] += y - pad
pts /= float(magnification)
all_points.extend([p.tolist() for p in pts if 0 <= p[0] < orig_w and 0 <= p[1] < orig_h])
if not all_points:
return []
pts = np.array(all_points, dtype=np.float32)
tree = cKDTree(pts)
suppressed = set()
for i, j in tree.query_pairs(r=8.0):
if i not in suppressed and j not in suppressed:
suppressed.add(j)
return [pts[i].tolist() for i in range(len(pts)) if i not in suppressed]
def load_gt(path):
with open(path, "r", encoding="utf-8") as f:
data = json.load(f)
if isinstance(data, dict) and "annotations" in data:
data = data["annotations"]
if isinstance(data, dict):
return [{"image": image, "points": points} for image, points in data.items()]
return data
def precision_recall(pred_points, gt_points, radius):
pred = np.array(pred_points, dtype=np.float32)
gt = np.array(gt_points, dtype=np.float32)
if len(pred) == 0 and len(gt) == 0:
return 1.0, 1.0, 0, 0, 0
if len(pred) == 0:
return 0.0, 0.0, 0, 0, len(gt)
if len(gt) == 0:
return 0.0, 0.0, 0, len(pred), 0
dist = np.linalg.norm(pred[:, None, :] - gt[None, :, :], axis=2)
rows, cols = linear_sum_assignment(dist)
matches = sum(1 for r, c in zip(rows, cols) if dist[r, c] <= radius)
fp = len(pred) - matches
fn = len(gt) - matches
precision = matches / (matches + fp) if matches + fp else 0.0
recall = matches / (matches + fn) if matches + fn else 0.0
return precision, recall, matches, fp, fn
def draw_visual(image_path, gt_points, pred_points, output_path):
img = cv2.imread(image_path)
for x, y in gt_points:
cv2.circle(img, (int(x), int(y)), 4, (0, 255, 0), -1)
for x, y in pred_points:
cv2.circle(img, (int(x), int(y)), 3, (0, 0, 255), 1)
cv2.imwrite(output_path, img)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--images_dir", required=True)
parser.add_argument("--gt_json", required=True)
parser.add_argument("--weights", default=os.path.join("weights", "SHTechA.pth"))
parser.add_argument("--output_dir", default="eval_results")
parser.add_argument("--confidence", type=float, default=0.5)
args = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
vis_dir = os.path.join(args.output_dir, "visualizations")
os.makedirs(vis_dir, exist_ok=True)
model, device, transform = load_model(args.weights)
rows = []
errors = []
squared_errors = []
for item in load_gt(args.gt_json):
image_name = item["image"]
gt_points = item.get("points", [])
image_path = image_name if os.path.isabs(image_name) else os.path.join(args.images_dir, image_name)
pred_points = infer_points(Image.open(image_path).convert("RGB"), model, device, transform, args.confidence)
err = abs(len(pred_points) - len(gt_points))
errors.append(err)
squared_errors.append(err ** 2)
row = {"image": os.path.basename(image_path), "gt_count": len(gt_points), "pred_count": len(pred_points), "abs_error": err, "sq_error": err ** 2}
for radius in [5, 10, 15, 20]:
p, r, m, fp, fn = precision_recall(pred_points, gt_points, radius)
row[f"precision_{radius}px"] = round(p, 4)
row[f"recall_{radius}px"] = round(r, 4)
row[f"matches_{radius}px"] = m
row[f"fp_{radius}px"] = fp
row[f"fn_{radius}px"] = fn
rows.append(row)
draw_visual(image_path, gt_points, pred_points, os.path.join(vis_dir, os.path.splitext(os.path.basename(image_path))[0] + "_eval.png"))
summary = {"mae": round(float(np.mean(errors)), 4) if errors else 0, "mse": round(float(np.mean(squared_errors)), 4) if squared_errors else 0, "images": len(rows)}
csv_path = os.path.join(args.output_dir, "evaluation.csv")
json_path = os.path.join(args.output_dir, "evaluation_summary.json")
with open(csv_path, "w", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=list(rows[0].keys()) if rows else ["image"])
writer.writeheader()
writer.writerows(rows)
with open(json_path, "w", encoding="utf-8") as f:
json.dump({"summary": summary, "rows": rows}, f, indent=2)
print(json.dumps({"csv": csv_path, "json": json_path, "visualizations": vis_dir, "summary": summary}, indent=2))
if __name__ == "__main__":
main()
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