utils.py

from io import BytesIO
import base64
import numpy as np
import matplotlib.pyplot as plt
import torch


def fig_to_base64(fig):
    buf = BytesIO()
    fig.savefig(buf, format='png', bbox_inches='tight')
    plt.close(fig)
    buf.seek(0)
    return base64.b64encode(buf.getvalue()).decode()

def show_mask(mask, ax, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        color = np.array([30/255, 144/255, 255/255, 0.6])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    ax.imshow(mask_image)

def show_box(box, ax):
    x0, y0 = box[0], box[1]
    w, h = box[2] - box[0], box[3] - box[1]
    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))

def show_points(coords, labels, ax, marker_size=375):
    pos_points = coords[labels==1]
    neg_points = coords[labels==0]
    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)

def show_boxes_on_image_base64(raw_image, boxes):
    fig, ax = plt.subplots(figsize=(10,10))
    ax.imshow(raw_image)
    for box in boxes:
        show_box(box, ax)
    ax.axis('off')
    return fig_to_base64(fig)

def show_points_on_image_base64(raw_image, input_points, input_labels=None):
    fig, ax = plt.subplots(figsize=(10,10))
    ax.imshow(raw_image)
    input_points = np.array(input_points)
    labels = np.ones_like(input_points[:, 0]) if input_labels is None else np.array(input_labels)
    show_points(input_points, labels, ax)
    ax.axis('off')
    return fig_to_base64(fig)

def show_points_and_boxes_on_image_base64(raw_image, boxes, input_points, input_labels=None):
    fig, ax = plt.subplots(figsize=(10,10))
    ax.imshow(raw_image)
    input_points = np.array(input_points)
    labels = np.ones_like(input_points[:, 0]) if input_labels is None else np.array(input_labels)
    show_points(input_points, labels, ax)
    for box in boxes:
        show_box(box, ax)
    ax.axis('off')
    return fig_to_base64(fig)

def show_masks_on_image_base64(raw_image, masks, scores):
    if len(masks.shape) == 4:
        masks = masks.squeeze()
    if scores.shape[0] == 1:
        scores = scores.squeeze()

    nb_predictions = scores.shape[-1]
    print(f"Number of predictions: {nb_predictions}")
    fig, axes = plt.subplots(1, nb_predictions, figsize=(5 * nb_predictions, 5))

    if nb_predictions == 1:
        axes = [axes]

    for i, (mask, score) in enumerate(zip(masks, scores)):
        print(i)
        mask = mask.cpu().detach().numpy()
        axes[i].imshow(np.array(raw_image))
        show_mask(mask, axes[i])
        axes[i].title.set_text(f"Mask {i+1}, Score: {score.item():.3f}")
        axes[i].axis("off")

    return fig_to_base64(fig)

def show_first_mask_on_image_base64(raw_image, masks, scores):
    if masks.ndim == 4:
        mask = masks[0, 0]
    elif masks.ndim == 3:
        mask = masks[0]
    else:
        mask = masks

    if isinstance(mask, torch.Tensor):
        mask = mask.cpu().detach().numpy()

    score_text = ""
    if scores is not None:
        if isinstance(scores, torch.Tensor):
            scores = scores.flatten()
            score = scores[0].item()
        else:
            score = float(np.array(scores).flatten()[0])
        score_text = f"Score: {score:.3f}"

    fig, ax = plt.subplots(figsize=(5, 5))
    ax.imshow(np.array(raw_image))
    show_mask(mask, ax)
    ax.set_title(score_text)
    ax.axis("off")

    return fig_to_base64(fig)

def show_all_annotations_on_image_base64(raw_image, masks=None, scores=None, boxes=None, input_points=None, input_labels=None, model_name=None):
    fig, ax = plt.subplots(figsize=(10, 10))
    ax.imshow(np.array(raw_image))

    if masks is not None:
        if masks.ndim == 4:
            mask = masks[0, 0]
        elif masks.ndim == 3:
            mask = masks[0]
        else:
            mask = masks
        if isinstance(mask, torch.Tensor):
            mask = mask.cpu().detach().numpy()
        show_mask(mask, ax)

        if scores is not None:
            if isinstance(scores, torch.Tensor):
                scores = scores.flatten()
                score = scores[0].item()
            else:
                score = float(np.array(scores).flatten()[0])
            #ax.set_title(f"{model_name} - Score: {score:.3f}")
            ax.set_title(f"{model_name}")

    
    if input_points is not None:
        input_points = np.array(input_points)
        labels = np.ones_like(input_points[:, 0]) if input_labels is None else np.array(input_labels)
        show_points(input_points, labels, ax)

    if boxes is not None:
        for box in boxes:
            show_box(box, ax)

    ax.axis("off")
    return fig_to_base64(fig)