files van zoedepth toegevoegd

geometry.py

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+import numpy as np
+
+def get_intrinsics(H,W):
+    """
+    Intrinsics for a pinhole camera model.
+    Assume fov of 55 degrees and central principal point.
+    """
+    f = 0.5 * W / np.tan(0.5 * 55 * np.pi / 180.0)
+    cx = 0.5 * W
+    cy = 0.5 * H
+    return np.array([[f, 0, cx],
+                     [0, f, cy],
+                     [0, 0, 1]])
+
+def depth_to_points(depth, R=None, t=None):
+
+    K = get_intrinsics(depth.shape[1], depth.shape[2])
+    Kinv = np.linalg.inv(K)
+    if R is None:
+        R = np.eye(3)
+    if t is None:
+        t = np.zeros(3)
+
+    # M converts from your coordinate to PyTorch3D's coordinate system
+    M = np.eye(3)
+    M[0, 0] = -1.0
+    M[1, 1] = -1.0
+
+    height, width = depth.shape[1:3]
+
+    x = np.arange(width)
+    y = np.arange(height)
+    coord = np.stack(np.meshgrid(x, y), -1)
+    coord = np.concatenate((coord, np.ones_like(coord)[:, :, [0]]), -1)  # z=1
+    coord = coord.astype(np.float32)
+    # coord = torch.as_tensor(coord, dtype=torch.float32, device=device)
+    coord = coord[None]  # bs, h, w, 3
+
+    D = depth[:, :, :, None, None]
+    # print(D.shape, Kinv[None, None, None, ...].shape, coord[:, :, :, :, None].shape )
+    pts3D_1 = D * Kinv[None, None, None, ...] @ coord[:, :, :, :, None]
+    # pts3D_1 live in your coordinate system. Convert them to Py3D's
+    pts3D_1 = M[None, None, None, ...] @ pts3D_1
+    # from reference to targe tviewpoint
+    pts3D_2 = R[None, None, None, ...] @ pts3D_1 + t[None, None, None, :, None]
+    # pts3D_2 = pts3D_1
+    # depth_2 = pts3D_2[:, :, :, 2, :]  # b,1,h,w
+    return pts3D_2[:, :, :, :3, 0][0]
+
+
+def create_triangles(h, w, mask=None):
+    """Creates mesh triangle indices from a given pixel grid size.
+        This function is not and need not be differentiable as triangle indices are
+        fixed.
+    Args:
+    h: (int) denoting the height of the image.
+    w: (int) denoting the width of the image.
+    Returns:
+    triangles: 2D numpy array of indices (int) with shape (2(W-1)(H-1) x 3)
+    """
+    x, y = np.meshgrid(range(w - 1), range(h - 1))
+    tl = y * w + x
+    tr = y * w + x + 1
+    bl = (y + 1) * w + x
+    br = (y + 1) * w + x + 1
+    triangles = np.array([tl, bl, tr, br, tr, bl])
+    triangles = np.transpose(triangles, (1, 2, 0)).reshape(
+        ((w - 1) * (h - 1) * 2, 3))
+    if mask is not None:
+        mask = mask.reshape(-1)
+        triangles = triangles[mask[triangles].all(1)]
+    return triangles

gradio_depth_pred.py

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+import gradio as gr
+from utils import colorize
+from PIL import Image
+import tempfile
+
+def predict_depth(model, image):
+    depth = model.infer_pil(image)
+    return depth
+
+def create_demo(model):
+    gr.Markdown("### Depth Prediction demo")
+    with gr.Row():
+        input_image = gr.Image(label="Input Image", type='pil', elem_id='img-display-input').style(height="auto")
+        depth_image = gr.Image(label="Depth Map", elem_id='img-display-output')
+    raw_file = gr.File(label="16-bit raw depth, multiplier:256")
+    submit = gr.Button("Submit")
+
+    def on_submit(image):
+        depth = predict_depth(model, image)
+        colored_depth = colorize(depth, cmap='gray_r')
+        tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
+        raw_depth = Image.fromarray((depth*256).astype('uint16'))
+        raw_depth.save(tmp.name)
+        return [colored_depth, tmp.name]
+    
+    submit.click(on_submit, inputs=[input_image], outputs=[depth_image, raw_file])
+    examples = gr.Examples(examples=["examples/person_1.jpeg", "examples/person_2.jpeg", "examples/person-leaves.png", "examples/living-room.jpeg"],
+                           inputs=[input_image])

gradio_im_to_3d.py

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+import gradio as gr
+import numpy as np
+import trimesh
+from geometry import depth_to_points, create_triangles
+from functools import partial
+import tempfile
+
+
+def depth_edges_mask(depth):
+    """Returns a mask of edges in the depth map.
+    Args:
+    depth: 2D numpy array of shape (H, W) with dtype float32.
+    Returns:
+    mask: 2D numpy array of shape (H, W) with dtype bool.
+    """
+    # Compute the x and y gradients of the depth map.
+    depth_dx, depth_dy = np.gradient(depth)
+    # Compute the gradient magnitude.
+    depth_grad = np.sqrt(depth_dx ** 2 + depth_dy ** 2)
+    # Compute the edge mask.
+    mask = depth_grad > 0.05
+    return mask
+
+
+def predict_depth(model, image):
+    depth = model.infer_pil(image)
+    return depth
+
+def get_mesh(model, image, keep_edges=False):
+    image.thumbnail((1024,1024))  # limit the size of the input image
+    depth = predict_depth(model, image)
+    pts3d = depth_to_points(depth[None])
+    pts3d = pts3d.reshape(-1, 3)
+
+    # Create a trimesh mesh from the points
+    # Each pixel is connected to its 4 neighbors
+    # colors are the RGB values of the image
+
+    verts = pts3d.reshape(-1, 3)
+    image = np.array(image)
+    if keep_edges:
+        triangles = create_triangles(image.shape[0], image.shape[1])
+    else:
+        triangles = create_triangles(image.shape[0], image.shape[1], mask=~depth_edges_mask(depth))
+    colors = image.reshape(-1, 3)
+    mesh = trimesh.Trimesh(vertices=verts, faces=triangles, vertex_colors=colors)
+
+    # Save as glb
+    glb_file = tempfile.NamedTemporaryFile(suffix='.glb', delete=False)
+    glb_path = glb_file.name
+    mesh.export(glb_path)
+    return glb_path
+
+def create_demo(model):
+
+    gr.Markdown("### Image to 3D mesh")
+    gr.Markdown("Convert a single 2D image to a 3D mesh")
+
+    with gr.Row():
+        image = gr.Image(label="Input Image", type='pil')
+        result = gr.Model3D(label="3d mesh reconstruction", clear_color=[
+                                                 1.0, 1.0, 1.0, 1.0])
+    
+    checkbox = gr.Checkbox(label="Keep occlusion edges", value=False)
+    submit = gr.Button("Submit")
+    submit.click(partial(get_mesh, model), inputs=[image, checkbox], outputs=[result])
+    examples = gr.Examples(examples=["examples/aerial_beach.jpeg", "examples/mountains.jpeg", "examples/person_1.jpeg", "examples/ancient-carved.jpeg"],
+                            inputs=[image])
+

utils.py

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+# MIT License
+
+# Copyright (c) 2022 Intelligent Systems Lab Org
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# File author: Shariq Farooq Bhat
+
+import matplotlib
+import matplotlib.cm
+import numpy as np
+import torch
+
+def colorize(value, vmin=None, vmax=None, cmap='magma_r', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None):
+    """Converts a depth map to a color image.
+
+    Args:
+        value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed
+        vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None.
+        vmax (float, optional):  vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None.
+        cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'.
+        invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99.
+        invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None.
+        background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255).
+        gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False.
+        value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None.
+
+    Returns:
+        numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4)
+    """
+    if isinstance(value, torch.Tensor):
+        value = value.detach().cpu().numpy()
+
+    value = value.squeeze()
+    if invalid_mask is None:
+        invalid_mask = value == invalid_val
+    mask = np.logical_not(invalid_mask)
+
+    # normalize
+    vmin = np.percentile(value[mask],2) if vmin is None else vmin
+    vmax = np.percentile(value[mask],85) if vmax is None else vmax
+    if vmin != vmax:
+        value = (value - vmin) / (vmax - vmin)  # vmin..vmax
+    else:
+        # Avoid 0-division
+        value = value * 0.
+
+    # squeeze last dim if it exists
+    # grey out the invalid values
+
+    value[invalid_mask] = np.nan
+    cmapper = matplotlib.cm.get_cmap(cmap)
+    if value_transform:
+        value = value_transform(value)
+        # value = value / value.max()
+    value = cmapper(value, bytes=True)  # (nxmx4)
+
+    # img = value[:, :, :]
+    img = value[...]
+    img[invalid_mask] = background_color
+
+    #     return img.transpose((2, 0, 1))
+    if gamma_corrected:
+        # gamma correction
+        img = img / 255
+        img = np.power(img, 2.2)
+        img = img * 255
+        img = img.astype(np.uint8)
+    return img
+