File size: 2,641 Bytes
f89df01 |
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 |
import os, json
import numpy as np
from sklearn import metrics
from tqdm import tqdm
# --- Utilities ---
def final_auc(data):
thresholds = [0.05 * i for i in range(21)]
cious = [np.mean(np.array(data) >= t) for t in thresholds]
return metrics.auc(thresholds, cious)
def final_ciou(data):
return np.mean(data) if data else 0.0
def parse_task_flags(annotations):
flags = {"Single-Sound": False, "Mixed-Sound": False, "Multi-Entity": False, "Off-Screen": False}
for ann in annotations:
task = ann["task"]
if task not in flags:
raise ValueError(f"Unknown task: {task}")
flags[task] = True
return flags
# --- Parameters ---
heatmap_threshold = 0.1
width, height = 640, 360
folder = "AVATAR"
file = "evaluation_results.json"
model = "your_model_name" # Replace with your model name
data_path = os.path.join("your_heatmap_root", model, folder, file)
benchmark_path = "AVATAR/metadata" # Replace with your benchmark path
# --- Initialization ---
ciou_by_task = {
"Total": [],
"Single-Sound": [],
"Mixed-Sound": [],
"Multi-Entity": []
}
off_screen_tn, off_screen_fp = 0, 0
# --- Load Evaluation Results ---
with open(data_path, 'r') as f:
data = json.load(f)
# --- Process Each Frame ---
for frame_key, result in tqdm(data.items()):
video_id = "_".join(frame_key.split("_")[:-1])
frame_num = int(frame_key.split("_")[-1])
metadata_file = os.path.join(benchmark_path, video_id, f"{frame_num:05d}.json")
with open(metadata_file, 'r') as f:
annotations = json.load(f)["annotations"]
flags = parse_task_flags(annotations)
ciou = result["cious"][str(heatmap_threshold)]
ciou_by_task["Total"].append(ciou)
for task in ["Single-Sound", "Mixed-Sound", "Multi-Entity"]:
if flags[task]:
ciou_by_task[task].append(ciou)
if flags["Off-Screen"]:
stats = result["pixel_statistics"][str(heatmap_threshold)]
off_screen_tn += width * height - stats["fp"]
off_screen_fp += stats["fp"]
# --- Compute Final Metrics ---
summary = {}
for task, values in ciou_by_task.items():
summary[task] = {
"ciou": final_ciou(values),
"auc": final_auc(values)
}
# --- Print Results ---
print(f"model: {model}, file: {file}\n")
for task in ["Total", "Single-Sound", "Mixed-Sound", "Multi-Entity"]:
print(f"--- {task.lower()} ---")
print(f"final ciou: {summary[task]['ciou']:.4f}")
print(f"final auc : {summary[task]['auc']:.4f}\n")
print("--- off-screen pixel statistics ---")
print("tn pixels \t fp pixels")
print(f"{off_screen_tn} \t {off_screen_fp}")
|