gazelle/utils.py

import torch
from PIL import Image, ImageDraw
import numpy as np
import matplotlib.pyplot as plt

def repeat_tensors(tensor, repeat_counts):
    repeated_tensors = [tensor[i:i+1].repeat(repeat, *[1] * (tensor.ndim - 1)) for i, repeat in enumerate(repeat_counts)]
    return torch.cat(repeated_tensors, dim=0)

def split_tensors(tensor, split_counts):
    indices = torch.cumsum(torch.tensor([0] + split_counts), dim=0)
    return [tensor[indices[i]:indices[i+1]] for i in range(len(split_counts))]

def visualize_heatmap(pil_image, heatmap, bbox=None):
    if isinstance(heatmap, torch.Tensor):
        heatmap = heatmap.detach().cpu().numpy()
    heatmap = Image.fromarray((heatmap * 255).astype(np.uint8)).resize(pil_image.size, Image.Resampling.BILINEAR)
    heatmap = plt.cm.jet(np.array(heatmap) / 255.)
    heatmap = (heatmap[:, :, :3] * 255).astype(np.uint8)
    heatmap = Image.fromarray(heatmap).convert("RGBA")
    heatmap.putalpha(128)
    overlay_image = Image.alpha_composite(pil_image.convert("RGBA"), heatmap)

    if bbox is not None:
        width, height = pil_image.size
        xmin, ymin, xmax, ymax = bbox
        draw = ImageDraw.Draw(overlay_image)
        draw.rectangle([xmin * width, ymin * height, xmax * width, ymax * height], outline="green", width=3)
    return overlay_image

def stack_and_pad(tensor_list):
    max_size = max([t.shape[0] for t in tensor_list])
    padded_list = []
    for t in tensor_list:
        if t.shape[0] == max_size:
            padded_list.append(t)
        else:
            padded_list.append(torch.cat([t, torch.zeros(max_size - t.shape[0], *t.shape[1:])], dim=0))
    return torch.stack(padded_list)