inference.py

from transformers import DPTImageProcessor, DPTForDepthEstimation
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry, SamPredictor
import gradio as gr
import supervision as sv
import torch
import numpy as np
from PIL import Image
import requests
import open3d as o3d
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt


def map_image_range(depth, min_value, max_value):
    """
    Maps the values of a numpy image array to a specified range.

    Args:
        image (numpy.ndarray): Input image array with values ranging from 0 to 1.
        min_value (float): Minimum value of the new range.
        max_value (float): Maximum value of the new range.

    Returns:
        numpy.ndarray: Image array with values mapped to the specified range.
    """
    # Ensure the input image is a numpy array
    print(np.min(depth))
    print(np.max(depth))
    depth = np.array(depth)
    # map the depth values are between 0 and 1
    depth = (depth - depth.min()) / (depth.max() - depth.min())
    # invert
    depth = 1 - depth
    print(np.min(depth))
    print(np.max(depth))
    # Map the values to the specified range
    mapped_image = (depth - 0) * (max_value - min_value) / (1 - 0) + min_value
    print(np.min(mapped_image))
    print(np.max(mapped_image))
    return mapped_image


def PCL(mask, depth):
    assert mask.shape == depth.shape
    assert type(mask) == np.ndarray
    assert type(depth) == np.ndarray
    rgb_mask = np.zeros((mask.shape[0], mask.shape[1], 3)).astype("uint8")
    rgb_mask[mask] = (255, 0, 0)
    print(np.unique(rgb_mask))
    depth_o3d = o3d.geometry.Image(depth)
    image_o3d = o3d.geometry.Image(rgb_mask)
    # print(len(depth_o3d))
    # print(len(image_o3d))
    rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
        image_o3d, depth_o3d, convert_rgb_to_intensity=False
    )
    # Step 3: Create a PointCloud from the RGBD image
    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
        rgbd_image,
        o3d.camera.PinholeCameraIntrinsic(
            o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault
        ),
    )
    # Step 4: Convert PointCloud data to a NumPy array
    # print(len(pcd))
    points = np.asarray(pcd.points)
    colors = np.asarray(pcd.colors)
    print(np.unique(colors, axis=0))
    print(np.unique(colors, axis=1))
    print(np.unique(colors))
    mask = colors[:, 0] == 1.0
    print(mask.sum())
    print(colors.shape)
    points = points[mask]
    colors = colors[mask]
    return points, colors


def PCL_rgb(rgb, depth):
    # assert rgb.shape == depth.shape
    assert type(rgb) == np.ndarray
    assert type(depth) == np.ndarray
    depth_o3d = o3d.geometry.Image(depth)
    image_o3d = o3d.geometry.Image(rgb)
    rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
        image_o3d, depth_o3d, convert_rgb_to_intensity=False
    )
    # Step 3: Create a PointCloud from the RGBD image
    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
        rgbd_image,
        o3d.camera.PinholeCameraIntrinsic(
            o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault
        ),
    )
    # Step 4: Convert PointCloud data to a NumPy array
    points = np.asarray(pcd.points)
    colors = np.asarray(pcd.colors)
    return points, colors


class DepthPredictor:
    def __init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
        self.model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
        self.model.eval()

    def predict(self, image):
        # prepare image for the model
        encoding = self.feature_extractor(image, return_tensors="pt")
        # forward pass
        with torch.no_grad():
            outputs = self.model(**encoding)
            predicted_depth = outputs.predicted_depth
            # interpolate to original size
            prediction = torch.nn.functional.interpolate(
                predicted_depth.unsqueeze(1),
                size=image.size[::-1],
                mode="bicubic",
                align_corners=False,
            ).squeeze()

        output = prediction.cpu().numpy()
        # output = 1 - (output/np.max(output))
        return output

    def generate_pcl(self, image):
        print(np.array(image).shape)
        depth = self.predict(image)
        print(depth.shape)
        # Step 2: Create an RGBD image from the RGB and depth image
        depth_o3d = o3d.geometry.Image(depth)
        image_o3d = o3d.geometry.Image(np.array(image))
        rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
            image_o3d, depth_o3d, convert_rgb_to_intensity=False
        )
        # Step 3: Create a PointCloud from the RGBD image
        pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
            rgbd_image,
            o3d.camera.PinholeCameraIntrinsic(
                o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault
            ),
        )
        # Step 4: Convert PointCloud data to a NumPy array
        points = np.asarray(pcd.points)
        colors = np.asarray(pcd.colors)
        print(points.shape, colors.shape)
        return points, colors

    def generate_fig(self, image):
        points, colors = self.generate_pcl(image)
        data = {
            "x": points[:, 0],
            "y": points[:, 1],
            "z": points[:, 2],
            "red": colors[:, 0],
            "green": colors[:, 1],
            "blue": colors[:, 2],
        }
        df = pd.DataFrame(data)
        size = np.zeros(len(df))
        size[:] = 0.01
        # Step 6: Create a 3D scatter plot using Plotly Express
        fig = px.scatter_3d(df, x="x", y="y", z="z", color="red", size=size)
        return fig

    def generate_fig2(self, image):
        points, colors = self.generate_pcl(image)
        # Step 6: Create a 3D scatter plot using Plotly Express
        fig = plt.figure()
        ax = fig.add_subplot(111, projection="3d")
        ax.scatter(points, size=0.01, c=colors, marker="o")
        return fig

    def generate_obj_rgb(self, image, n_samples, cube_size, max_depth, min_depth):
        # Step 1: Create a point cloud
        depth = self.predict(image)
        image = np.array(image)
        depth = map_image_range(depth, min_depth, max_depth)
        point_cloud, color_array = PCL_rgb(image, depth)
        idxs = np.random.choice(len(point_cloud), int(n_samples))
        point_cloud = point_cloud[idxs]
        color_array = color_array[idxs]
        # Create a mesh to hold the colored cubes
        mesh = o3d.geometry.TriangleMesh()
        # Create cubes and add them to the mesh
        for point, color in zip(point_cloud, color_array):
            cube = o3d.geometry.TriangleMesh.create_box(
                width=cube_size, height=cube_size, depth=cube_size
            )
            cube.translate(-point)
            cube.paint_uniform_color(color)
            mesh += cube
        # Save the mesh to an .obj file
        output_file = "./cloud.obj"
        o3d.io.write_triangle_mesh(output_file, mesh)
        return output_file

    def generate_obj_masks(self, image, n_samples, masks, cube_size):
        # Generate a point cloud
        point_cloud, color_array = self.generate_pcl(image)
        print(point_cloud.shape)
        mesh = o3d.geometry.TriangleMesh()
        # Create cubes and add them to the mesh
        cs = [(255, 0, 0), (0, 255, 0), (0, 0, 255)]
        for c, (mask, _) in zip(cs, masks):
            mask = mask.ravel()
            point_cloud_subset, color_array_subset = (
                point_cloud[mask],
                color_array[mask],
            )
            idxs = np.random.choice(len(point_cloud_subset), int(n_samples))
            point_cloud_subset = point_cloud_subset[idxs]
            for point in point_cloud_subset:
                cube = o3d.geometry.TriangleMesh.create_box(
                    width=cube_size, height=cube_size, depth=cube_size
                )
                cube.translate(-point)
                cube.paint_uniform_color(c)
                mesh += cube
        # Save the mesh to an .obj file
        output_file = "./cloud.obj"
        o3d.io.write_triangle_mesh(output_file, mesh)
        return output_file

    def generate_obj_masks2(
        self, image, masks, cube_size, n_samples, min_depth, max_depth
    ):
        # Generate a point cloud
        depth = self.predict(image)
        # depth = map_image_range(depth, min_depth, max_depth)
        image = np.array(image)
        mesh = o3d.geometry.TriangleMesh()
        # Create cubes and add them to the mesh
        print(len(masks))
        cs = [(255, 0, 0), (0, 255, 0), (0, 0, 255)]
        for c, (mask, _) in zip(cs, masks):
            points, _ = PCL(mask, depth)
            # idxs = np.random.choice(len(points), int(n_samples))
            # points = points[idxs]
            for point in points:
                cube = o3d.geometry.TriangleMesh.create_box(
                    width=cube_size, height=cube_size, depth=cube_size
                )
                cube.translate(-point)
                cube.paint_uniform_color(c)
                mesh += cube
        # Save the mesh to an .obj file
        output_file = "./cloud.obj"
        o3d.io.write_triangle_mesh(output_file, mesh)
        return output_file


import numpy as np
from typing import Optional, Tuple


class CustomSamPredictor(SamPredictor):
    def __init__(
        self,
        sam_model,
    ) -> None:
        super().__init__(sam_model)

    def encode_image(
        self,
        image: np.ndarray,
        image_format: str = "RGB",
    ) -> None:
        """
        Calculates the image embeddings for the provided image, allowing
        masks to be predicted with the 'predict' method.

        Arguments:
          image (np.ndarray): The image for calculating masks. Expects an
            image in HWC uint8 format, with pixel values in [0, 255].
          image_format (str): The color format of the image, in ['RGB', 'BGR'].
        """
        assert image_format in [
            "RGB",
            "BGR",
        ], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
        if image_format != self.model.image_format:
            image = image[..., ::-1]

        # Transform the image to the form expected by the model
        input_image = self.transform.apply_image(image)
        input_image_torch = torch.as_tensor(input_image, device=self.device)
        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[
            None, :, :, :
        ]
        self.set_torch_image(input_image_torch, image.shape[:2])
        return self.get_image_embedding()

    def decode_and_predict(
        self,
        embedding: torch.Tensor,
        point_coords: Optional[np.ndarray] = None,
        point_labels: Optional[np.ndarray] = None,
        box: Optional[np.ndarray] = None,
        mask_input: Optional[np.ndarray] = None,
        multimask_output: bool = True,
        return_logits: bool = False,
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Decodes the provided image embedding and makes mask predictions based on prompts.

        Arguments:
          embedding (torch.Tensor): The image embedding to decode.
          ... (other arguments from the predict function)

        Returns:
          (np.ndarray): The output masks in CxHxW format.
          (np.ndarray): An array of quality predictions for each mask.
          (np.ndarray): Low resolution mask logits for subsequent iterations.
        """
        self.features = embedding
        self.is_image_set = True
        return self.predict(
            point_coords=point_coords,
            point_labels=point_labels,
            box=box,
            mask_input=mask_input,
            multimask_output=multimask_output,
            return_logits=return_logits,
        )

    def dummy_set_torch_image(
        self,
        transformed_image: torch.Tensor,
        original_image_size: Tuple[int, ...],
    ) -> None:
        """
        Calculates the image embeddings for the provided image, allowing
        masks to be predicted with the 'predict' method. Expects the input
        image to be already transformed to the format expected by the model.

        Arguments:
          transformed_image (torch.Tensor): The input image, with shape
            1x3xHxW, which has been transformed with ResizeLongestSide.
          original_image_size (tuple(int, int)): The size of the image
            before transformation, in (H, W) format.
        """
        assert (
            len(transformed_image.shape) == 4
            and transformed_image.shape[1] == 3
            and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size
        ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}."
        self.reset_image()

        self.original_size = original_image_size
        self.input_size = tuple(transformed_image.shape[-2:])
        input_image = self.model.preprocess(transformed_image)
        # The following line is commented out to avoid encoding on cpu
        # self.features = self.model.image_encoder(input_image)
        self.is_image_set = True

    def dummy_set_image(
        self,
        image: np.ndarray,
        image_format: str = "RGB",
    ) -> None:
        """
        Calculates the image embeddings for the provided image, allowing
        masks to be predicted with the 'predict' method.

        Arguments:
          image (np.ndarray): The image for calculating masks. Expects an
            image in HWC uint8 format, with pixel values in [0, 255].
          image_format (str): The color format of the image, in ['RGB', 'BGR'].
        """
        assert image_format in [
            "RGB",
            "BGR",
        ], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
        if image_format != self.model.image_format:
            image = image[..., ::-1]

        # Transform the image to the form expected by the model
        input_image = self.transform.apply_image(image)
        input_image_torch = torch.as_tensor(input_image, device=self.device)
        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[
            None, :, :, :
        ]

        self.dummy_set_torch_image(input_image_torch, image.shape[:2])


class SegmentPredictor:
    def __init__(self, device=None):
        MODEL_TYPE = "vit_h"
        checkpoint = "sam_vit_h_4b8939.pth"
        sam = sam_model_registry[MODEL_TYPE](checkpoint=checkpoint)
        # Select device
        if device is None:
            self.device = "cuda" if torch.cuda.is_available() else "cpu"
        else:
            self.device = device
        sam.to(device=self.device)
        self.mask_generator = SamAutomaticMaskGenerator(sam)
        self.conditioned_pred = CustomSamPredictor(sam)

    def encode(self, image):
        image = np.array(image)
        return self.conditioned_pred.encode_image(image)

    def dummy_encode(self, image):
        image = np.array(image)
        self.conditioned_pred.dummy_set_image(image)

    def cond_pred(self, embedding, pts, lbls):
        lbls = np.array(lbls)
        pts = np.array(pts)
        masks, _, _ = self.conditioned_pred.decode_and_predict(
            embedding, point_coords=pts, point_labels=lbls, multimask_output=True
        )
        idxs = np.argsort(-masks.sum(axis=(1, 2)))
        sam_masks = []
        for n, i in enumerate(idxs):
            sam_masks.append((masks[i], str(n)))
        return sam_masks

    def segment_everything(self, image):
        image = np.array(image)
        sam_result = self.mask_generator.generate(image)
        sam_masks = []
        for i, mask in enumerate(sam_result):
            sam_masks.append((mask["segmentation"], str(i)))
        return sam_masks