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import os |
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import json |
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import torch |
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import numpy as np |
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from tqdm import tqdm |
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from typing import List |
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from utils import heatmap_interpolation, video_interpolation |
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class HeatmapAnalyzer: |
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def __init__(self, data_root: str, model: str, heatmap_thresholds: List, benchmark_path: str, \ |
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height: int=360, width: int=640, frame_sample_rate: int=8, video: bool=True): |
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self.data_root = data_root |
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self.model = model |
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self.benchmark_path = benchmark_path |
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self.height = height |
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self.width = width |
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self.frame_sample_rate = frame_sample_rate |
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self.heatmap_thresholds = heatmap_thresholds |
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self.video = video |
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def compute(self): |
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data_path = os.path.join(self.data_root, self.model, "heatmap") |
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save_path = os.path.join(self.data_root, self.model, "heatmap_threshold.json") |
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print(f"Calculating {'Video' if self.video else 'Image'} Heatmap") |
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total_val = self.compute_for_video(data_path) if self.video else self.compute_for_image(data_path) |
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heatmap_threshold = self.cal_heatmap_threshold(np.array(total_val)) |
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self.save_heatmap_threshold(save_path, heatmap_threshold) |
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def compute_for_image(self, data_path): |
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total_val = [] |
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for data_file in tqdm(os.listdir(data_path)): |
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if not data_file.endswith(".npy"): |
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continue |
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infer = torch.tensor(np.load(os.path.join(data_path, data_file))).unsqueeze(0).unsqueeze(0) |
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infer = heatmap_interpolation(infer, self.height, self.width) |
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total_val.append(infer) |
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return total_val |
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def compute_for_video(self, data_path): |
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total_val = [] |
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for data_file in tqdm(os.listdir(data_path)): |
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if not data_file.endswith(".npy"): |
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continue |
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video_id = data_file.split(".")[0] |
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metadata_path = os.path.join(self.benchmark_path, video_id) |
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infer = torch.tensor(np.load(os.path.join(data_path, data_file))) |
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infer = video_interpolation(infer, self.frame_sample_rate) |
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for frame_data in os.listdir(metadata_path): |
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if frame_data.endswith(".jpg"): |
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continue |
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frame_num = int(frame_data.split(".")[0].split("_")[-1]) |
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infer_map = infer[frame_num].unsqueeze(0) |
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infer_map = heatmap_interpolation(infer_map, self.height, self.width) |
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total_val.append(infer_map) |
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return total_val |
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def cal_heatmap_threshold(self, total_heatmap: np.ndarray): |
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sorted_data = np.sort(total_heatmap, axis=None)[::-1] |
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result = dict() |
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for thr in self.heatmap_thresholds: |
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result[thr] = float(sorted_data[int(len(sorted_data) * thr)]) |
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return result |
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def save_heatmap_threshold(self, save_path: str, result: dict): |
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with open(save_path, 'w') as f: |
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json.dump(result, f, indent=4) |
