from matplotlib import pyplot as plt
import torch
import torch.nn.functional as F


from constants import COLORS
from utils import fig2img


def visualize_prediction(
    pil_img, output_dict, threshold=0.7, id2label=None
):
    keep = output_dict["scores"] > threshold
    boxes = output_dict["boxes"][keep].tolist()
    scores = output_dict["scores"][keep].tolist()
    labels = output_dict["labels"][keep].tolist()
    if id2label is not None:
        labels = [id2label[x] for x in labels]

    fig, ax = plt.subplots(figsize=(12, 12))
    ax.imshow(pil_img)
    colors = COLORS * 100
    for score, (xmin, ymin, xmax, ymax), label, color in zip(
        scores, boxes, labels, colors
    ):
        ax.add_patch(
            plt.Rectangle(
                (xmin, ymin),
                xmax - xmin,
                ymax - ymin,
                fill=False,
                color=color,
                linewidth=2,
            )
        )
        ax.text(
            xmin,
            ymin,
            f"{label}: {score:0.2f}",
            fontsize=10,
            bbox=dict(facecolor="yellow", alpha=0.5),
        )
    ax.axis("off")
    return fig2img(fig)


def visualize_attention_map(pil_img, attention_map):
    attention_map = attention_map[-1].detach().cpu()
    avg_attention_weight = torch.mean(attention_map, dim=1).squeeze()
    avg_attention_weight_resized = (
        F.interpolate(
            avg_attention_weight.unsqueeze(0).unsqueeze(0),
            size=pil_img.size[::-1],
            mode="bicubic",
        )
        .squeeze()
        .numpy()
    )

    plt.imshow(pil_img)
    plt.imshow(avg_attention_weight_resized, alpha=0.7, cmap="viridis")
    plt.axis("off")
    fig = plt.gcf()
    return fig2img(fig)