Grounding DINO

Define the visualization utilities (from SAM2)

import matplotlib.pyplot as plt
import numpy as np


np.random.seed(3)


def show_mask(mask, ax, random_color=False, borders=True):
    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 = mask.astype(np.uint8)
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    if borders:
        import cv2

        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        # Try to smooth contours
        contours = [cv2.approxPolyDP(contour, epsilon=0.01, closed=True) for contour in contours]
        mask_image = cv2.drawContours(mask_image, contours, -1, (1, 1, 1, 0.5), thickness=2)
    ax.imshow(mask_image)


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_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_masks(image, masks, scores, point_coords=None, box_coords=None, input_labels=None, borders=True):
    for i, (mask, score) in enumerate(zip(masks, scores)):
        plt.figure(figsize=(10, 10))
        plt.imshow(image)
        show_mask(mask, plt.gca(), borders=borders)
        if point_coords is not None:
            assert input_labels is not None
            show_points(point_coords, input_labels, plt.gca())
        if box_coords is not None:
            # boxes
            show_box(box_coords, plt.gca())
        if len(scores) > 1:
            plt.title(f"Mask {i+1}, Score: {score:.3f}", fontsize=18)
        plt.axis("off")
        plt.show()

Instantiate the model

from pixano_inference.providers.sam2 import Sam2Provider


provider = Sam2Provider()
model = provider.load_model(
    "sam",
    "video_mask_generation",
    torch.device("cuda") if torch.cuda.is_available() else "cpu",
    "facebook/sam2-hiera-tiny",
)

Call the model

from pathlib import Path

from pixano_inference.utils import convert_string_to_image


image = convert_string_to_image(Path("./docs/assets/examples/sam2/truck.jpg"))
points = [[[300, 375]], [[500, 375], [3, 3]]]
labels = [[1], [1, 0]]
generation_output = model.image_mask_generation(
    image=image, points=points, labels=labels, boxes=None, multimask_output=True
)

Display the result

plt.figure(figsize=(10, 10))
masks = generation_output.masks
scores = generation_output.scores.to_numpy()
for pred_points, pred_labels, pred_masks, score in zip(points, labels, masks, scores):
    np_points = np.array(pred_points)
    np_labels = np.array(pred_labels)
    show_masks(
        image,
        np.array([mask.to_mask() for mask in pred_masks]),
        score,
        point_coords=np_points,
        input_labels=np_labels,
        borders=True,
    )
plt.axis("on")