app.py

import gradio as gr
import torch
from matplotlib import pyplot as plt
import numpy as np
from groundingdino.util.inference import load_model, load_image, predict
from segment_anything import SamPredictor, sam_model_registry
from torchvision.ops import box_convert

model_type = "vit_b"
sam_checkpoint = "weights/sam_vit_b.pth"
config = "groundingdino/config/GroundingDINO_SwinT_OGC.py"
dino_checkpoint = "weights/groundingdino_swint_ogc.pth"
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
predictor = SamPredictor(sam)
device = "cpu"
model = load_model(config, dino_checkpoint, device)
box_threshold = 0.35
text_threshold = 0.25


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, label=None):
    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='red', facecolor=(0, 0, 0, 0), lw=2))
    if label is not None:
        ax.text(x0, y0, label, fontsize=12, color='white', backgroundcolor='red', ha='left', va='top')


def extract_object_with_transparent_background(image, masks):
    mask_expanded = np.expand_dims(masks[0], axis=-1)
    mask_expanded = np.repeat(mask_expanded, 3, axis=-1)
    segment = image * mask_expanded
    rgba_segment = np.zeros((segment.shape[0], segment.shape[1], 4), dtype=np.uint8)
    rgba_segment[:, :, :3] = segment
    rgba_segment[:, :, 3] = masks[0] * 255
    return rgba_segment


def extract_remaining_image(image, masks):
    inverse_mask = np.logical_not(masks[0])
    inverse_mask_expanded = np.expand_dims(inverse_mask, axis=-1)
    inverse_mask_expanded = np.repeat(inverse_mask_expanded, 3, axis=-1)
    remaining_image = image * inverse_mask_expanded
    return remaining_image


def overlay_masks_boxes_on_image(image, masks, boxes, labels, show_masks, show_boxes):
    fig, ax = plt.subplots()
    ax.imshow(image)
    if show_masks:
        for mask in masks:
            show_mask(mask, ax, random_color=False)

    if show_boxes:
        for input_box, label in zip(boxes, labels):
            show_box(input_box, ax, label)

    ax.axis('off')
    plt.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0)
    plt.margins(0, 0)

    fig.canvas.draw()
    output_image = np.array(fig.canvas.buffer_rgba())

    plt.close(fig)
    return output_image


def detect_objects(image, prompt, show_masks=True, show_boxes=True, crop_options="No crop"):
    image_source, image = load_image(image)
    predictor.set_image(image_source)

    boxes, logits, phrases = predict(
        model=model,
        image=image,
        caption=prompt,
        box_threshold=box_threshold,
        text_threshold=text_threshold,
        device=device
    )

    h, w, _ = image_source.shape
    boxes = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy") * torch.Tensor([w, h, w, h])
    boxes = np.round(boxes.numpy()).astype(int)

    labels = [f"{phrase} {logit:.2f}" for phrase, logit in zip(phrases, logits)]

    masks_list = []
    res_json = {"prompt": prompt, "objects": []}

    output_image_paths = []

    for i, (input_box, label) in enumerate(zip(boxes, labels)):
        x1, y1, x2, y2 = input_box
        width = x2 - x1
        height = y2 - y1
        avg_size = (width + height) / 2
        d = avg_size * 0.1

        center_point = np.array([(x1 + x2) / 2, (y1 + y2) / 2])
        points = []
        points.append([center_point[0], center_point[1] - d])
        points.append([center_point[0], center_point[1] + d])
        points.append([center_point[0] - d, center_point[1]])
        points.append([center_point[0] + d, center_point[1]])
        input_point = np.array(points)
        input_label = np.array([1] * len(input_point))

        masks, scores, logits = predictor.predict(
            point_coords=input_point,
            point_labels=input_label,
            multimask_output=True,
        )
        mask_input = logits[np.argmax(scores), :, :]

        masks, _, _ = predictor.predict(
            point_coords=input_point,
            point_labels=input_label,
            mask_input=mask_input[None, :, :],
            multimask_output=False
        )
        masks_list.append(masks)

        composite_image = np.zeros_like(image_source)
        rgba_segment = extract_object_with_transparent_background(image_source, masks)
        composite_image = np.maximum(composite_image, rgba_segment[:, :, :3])
        cropped_image = composite_image[y1:y2, x1:x2, :]
        output_image = overlay_masks_boxes_on_image(cropped_image, [], [], [], False, False)

        output_image_path = f'output_image_{i}.jpeg'
        plt.imsave(output_image_path, output_image)

        output_image_paths.append(output_image_path)

        # save object information in json
        res_json["objects"].append(
            {"label": label, "score": np.max(scores).item(), "box": input_box.tolist(),
             "center": center_point.tolist(),
             "avg_size": avg_size})

    return [res_json, output_image_paths]


app = gr.Interface(
    detect_objects,
    inputs=[gr.Image(type='filepath', label="Upload Image"),
            gr.Textbox(
                label="Object to Detect",
                placeholder="Enter any text, comma separated if multiple objects needed",
                show_label=True,
                lines=1,
            )],
    outputs=[
        gr.JSON(label="Output JSON"),
        gr.Gallery(label="Result"),
    ],
    examples=[
        ["images/tiger.jpeg", "animal from cat family"],
        ["images/car.jpeg", "a blue sports car"],
        ["images/bags.jpeg", "black bag next to the red bag"],
        ["images/deer.jpeg", "deer jumping and running across the road"],
        ["images/penn.jpeg", "sign board"]
    ],
    title="Segment Anything",
    description="Zero-Shot Object Detection, Segmentation and Cropping",
    article="https://segment-anything.com",
)

app.launch()