app.py

import os
import gradio as gr
import numpy as np
import cv2
from PIL import Image, ImageOps
import torch
from inference import SegmentPredictor, DepthPredictor
from utils import generate_PCL, PCL3, point_cloud


sam = SegmentPredictor()
sam_cpu = SegmentPredictor(device="cpu")
dpt = DepthPredictor()
red = (255, 0, 0)
blue = (0, 0, 255)
annos = []


block = gr.Blocks()
with block:
    # States
    def point_coords_empty():
        return []

    def point_labels_empty():
        return []

    image_edit_trigger = gr.State(True)
    point_coords = gr.State(point_coords_empty)
    point_labels = gr.State(point_labels_empty)
    masks = gr.State([])
    cutout_idx = gr.State(set())
    pred_masks = gr.State([])
    prompt_masks = gr.State([])
    embedding = gr.State()

    # UI
    with gr.Column():
        gr.Markdown(
            """# Segment Anything Model (SAM)
            ## a new AI model from Meta AI that can "cut out" any object, in any image, with a single click 🚀
            SAM is a promptable segmentation system with zero-shot generalization to unfamiliar objects and images, without the need for additional training. [**Official Project**](https://segment-anything.com/) [**Code**](https://github.com/facebookresearch/segment-anything).
            """
        )
        with gr.Row():
            with gr.Column():
                with gr.Tab("Upload Image"):
                    # mirror_webcam = False
                    upload_image = gr.Image(label="Input", type="pil", tool=None)
                with gr.Tab("Webcam"):
                    # mirror_webcam = False
                    input_image = gr.Image(
                        label="Input", type="pil", tool=None, source="webcam"
                    )
                with gr.Row():
                    sam_encode_btn = gr.Button("Encode", variant="primary")
                    sam_sgmt_everything_btn = gr.Button(
                        "Segment Everything!", variant="primary"
                    )
                # sam_encode_status = gr.Label('Not encoded yet')
        with gr.Row():
            prompt_image = gr.Image(label="Segments")
            # prompt_lbl_image = gr.AnnotatedImage(label='Segment Labels')
            lbl_image = gr.AnnotatedImage(label="Everything")
        with gr.Row():
            point_label_radio = gr.Radio(label="Point Label", choices=[1, 0], value=1)
            text = gr.Textbox(label="Mask Name")
            reset_btn = gr.Button("New Mask")
        selected_masks_image = gr.AnnotatedImage(label="Selected Masks")
        with gr.Row():
            with gr.Column():
                pcl_figure = gr.Model3D(
                    label="3-D Reconstruction", clear_color=[1.0, 1.0, 1.0, 1.0]
                )
                with gr.Row():
                    max_depth = gr.Slider(
                        minimum=0, maximum=10, value=3, step=0.01, label="Max Depth"
                    )
                    min_depth = gr.Slider(
                        minimum=0, maximum=10, step=0.01, value=1, label="Min Depth"
                    )
                    n_samples = gr.Slider(
                        minimum=1e3,
                        maximum=1e6,
                        step=1e3,
                        value=1e5,
                        label="Number of Samples",
                    )
                    cube_size = gr.Slider(
                        minimum=0.00001,
                        maximum=0.001,
                        step=0.000001,
                        default=0.00001,
                        label="Cube size",
                    )
                    depth_reconstruction_btn = gr.Button(
                        "3D Reconstruction", variant="primary"
                    )
                    depth_reconstruction_mask_btn = gr.Button(
                        "Mask Reconstruction", variant="primary"
                    )

                sam_decode_btn = gr.Button("Predict using points!", variant="primary")

    # components
    components = {
        point_coords,
        point_labels,
        image_edit_trigger,
        masks,
        cutout_idx,
        input_image,
        embedding,
        point_label_radio,
        text,
        reset_btn,
        sam_sgmt_everything_btn,
        sam_decode_btn,
        depth_reconstruction_btn,
        prompt_image,
        lbl_image,
        n_samples,
        max_depth,
        min_depth,
        cube_size,
        selected_masks_image,
    }

    def on_upload_image(input_image, upload_image):
        # Mirror because gradio.image webcam has mirror = True
        upload_image_mirror = ImageOps.mirror(upload_image)
        return [upload_image_mirror, upload_image]

    upload_image.upload(
        on_upload_image, [input_image, upload_image], [input_image, upload_image]
    )

    # event - init coords
    def on_reset_btn_click(input_image):
        return input_image, point_coords_empty(), point_labels_empty(), None, []

    reset_btn.click(
        on_reset_btn_click,
        [input_image],
        [prompt_image, point_coords, point_labels],
        queue=False,
    )

    def on_prompt_image_select(
        input_image,
        prompt_image,
        point_coords,
        point_labels,
        point_label_radio,
        text,
        pred_masks,
        embedding,
        evt: gr.SelectData,
    ):
        sam_cpu.dummy_encode(input_image)
        x, y = evt.index
        color = red if point_label_radio == 0 else blue
        if prompt_image is None:
            prompt_image = np.array(input_image.copy())

        cv2.circle(prompt_image, (x, y), 5, color, -1)
        point_coords.append([x, y])
        point_labels.append(point_label_radio)
        sam_masks = sam_cpu.cond_pred(
            pts=np.array(point_coords), lbls=np.array(point_labels), embedding=embedding
        )
        return [
            prompt_image,
            (input_image, sam_masks),
            point_coords,
            point_labels,
            sam_masks,
        ]

    prompt_image.select(
        on_prompt_image_select,
        [
            input_image,
            prompt_image,
            point_coords,
            point_labels,
            point_label_radio,
            text,
            pred_masks,
            embedding,
        ],
        [prompt_image, lbl_image, point_coords, point_labels, pred_masks],
        queue=True,
    )

    def on_everything_image_select(
        input_image, pred_masks, masks, text, evt: gr.SelectData
    ):
        i = evt.index
        mask = pred_masks[i][0]
        print(mask)
        print(type(mask))
        masks.append((mask, text))
        anno = (input_image, masks)
        return [masks, anno]

    lbl_image.select(
        on_everything_image_select,
        [input_image, pred_masks, masks, text],
        [masks, selected_masks_image],
        queue=False,
    )

    def on_selected_masks_image_select(input_image, masks, evt: gr.SelectData):
        i = evt.index
        del masks[i]
        anno = (input_image, masks)
        return [masks, anno]

    selected_masks_image.select(
        on_selected_masks_image_select,
        [input_image, masks],
        [masks, selected_masks_image],
        queue=False,
    )
    # prompt_lbl_image.select(on_everything_image_select,
    #                   [input_image, prompt_masks, masks, text],
    #                   [masks, selected_masks_image], queue=False)

    def on_click_sam_encode_btn(inputs):
        print("encoding")
        # encode image on click
        embedding = sam.encode(inputs[input_image]).cpu()
        sam_cpu.dummy_encode(inputs[input_image])
        print("encoding done")
        return [inputs[input_image], embedding]

    sam_encode_btn.click(
        on_click_sam_encode_btn, components, [prompt_image, embedding], queue=False
    )

    def on_click_sam_dencode_btn(inputs):
        print("inferencing")
        image = inputs[input_image]
        generated_mask, _, _ = sam.cond_pred(
            pts=np.array(inputs[point_coords]), lbls=np.array(inputs[point_labels])
        )
        inputs[masks].append((generated_mask, inputs[text]))
        print(inputs[masks][0])
        return {prompt_image: (image, inputs[masks])}

    sam_decode_btn.click(
        on_click_sam_dencode_btn,
        components,
        [prompt_image, masks, cutout_idx],
        queue=True,
    )

    def on_depth_reconstruction_btn_click(inputs):
        print("depth reconstruction")
        path = dpt.generate_obj_rgb(
            image=inputs[input_image],
            cube_size=inputs[cube_size],
            n_samples=inputs[n_samples],
            # masks=inputs[masks],
            min_depth=inputs[min_depth],
            max_depth=inputs[max_depth],
        )
        return {pcl_figure: path}

    depth_reconstruction_btn.click(
        on_depth_reconstruction_btn_click, components, [pcl_figure], queue=False
    )

    def on_depth_reconstruction_mask_btn_click(inputs):
        print("depth reconstruction")
        path = dpt.generate_obj_masks2(
            image=inputs[input_image],
            cube_size=inputs[cube_size],
            n_samples=inputs[n_samples],
            masks=inputs[masks],
            min_depth=inputs[min_depth],
            max_depth=inputs[max_depth],
        )
        return {pcl_figure: path}

    depth_reconstruction_mask_btn.click(
        on_depth_reconstruction_mask_btn_click, components, [pcl_figure], queue=False
    )

    def on_sam_sgmt_everything_btn_click(inputs):
        print("segmenting everything")
        image = inputs[input_image]
        sam_masks = sam.segment_everything(image)
        print(image)
        print(sam_masks)
        return [(image, sam_masks), sam_masks]

    sam_sgmt_everything_btn.click(
        on_sam_sgmt_everything_btn_click,
        components,
        [lbl_image, pred_masks],
        queue=True,
    )


if __name__ == "__main__":
    block.queue()
    block.launch(auth=("novouser", "bstad2023"))