Add files using upload-large-folder tool

Orient_Anything/assets/axis.obj

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+vt 0.480195 0.239671
+s 0
+usemtl MI_YamahaMSP3_MatteBlack.010
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Orient_Anything/render/canvas.py

@@ -0,0 +1,49 @@
+import typing as t
+
+from PIL import Image, ImageColor, ImageOps, ImageChops, ImageFilter
+import numpy as np
+
+class Canvas:
+    def __init__(self, filename=None, height=500, width=500):
+        self.filename = filename
+        self.height, self.width = height, width
+        self.img = Image.new("RGBA", (self.height, self.width), (0, 0, 0, 0))
+
+    def draw(self, dots, color: t.Union[tuple, str]):
+        if isinstance(color, str):
+            color = ImageColor.getrgb(color)
+        if isinstance(dots, tuple):
+            dots = [dots]
+        for dot in dots:
+            if dot[0]>=self.height or dot[1]>=self.width or dot[0]<0 or dot[1]<0:
+                # print(dot)
+                continue
+            self.img.putpixel(dot, color + (255,))
+
+    def add_white_border(self, border_size=5):
+        # 确保输入图像是 RGBA 模式
+        if self.img.mode != "RGBA":
+            self.img = self.img.convert("RGBA")
+        
+        # 提取 alpha 通道
+        alpha = self.img.getchannel("A")
+        # print(alpha.size)
+        dilated_alpha = alpha.filter(ImageFilter.MaxFilter(size=5))
+        # # print(dilated_alpha.size)
+        white_area = Image.new("RGBA", self.img.size, (255, 255, 255, 255))
+        white_area.putalpha(dilated_alpha)
+        
+        # 合并膨胀后的白色区域与原图像
+        result = Image.alpha_composite(white_area, self.img)
+        # expanded_alpha = ImageOps.expand(alpha, border=border_size, fill=255)
+        # white_border = Image.new("RGBA", image.size, (255, 255, 255, 255))
+        # white_border.putalpha(alpha)
+        return result
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, type, value, traceback):
+        # self.img = add_white_border(self.img)
+        self.img.save(self.filename)
+        pass

Orient_Anything/render/model.py

@@ -0,0 +1,31 @@
+import numpy
+from PIL import Image
+from .core import Vec4d
+
+
+class Model:
+    def __init__(self, filename, texture_filename):
+        """
+        https://en.wikipedia.org/wiki/Wavefront_.obj_file#Vertex_normal_indices
+        """
+        self.vertices = []
+        self.uv_vertices = []
+        self.uv_indices = []
+        self.indices = []
+
+        texture = Image.open(texture_filename)
+        self.texture_array = numpy.array(texture)
+        self.texture_width, self.texture_height = texture.size
+
+        with open(filename) as f:
+            for line in f:
+                if line.startswith("v "):
+                    x, y, z = [float(d) for d in line.strip("v").strip().split(" ")]
+                    self.vertices.append(Vec4d(x, y, z, 1))
+                elif line.startswith("vt "):
+                    u, v = [float(d) for d in line.strip("vt").strip().split(" ")]
+                    self.uv_vertices.append([u, v])
+                elif line.startswith("f "):
+                    facet = [d.split("/") for d in line.strip("f").strip().split(" ")]
+                    self.indices.append([int(d[0]) for d in facet])
+                    self.uv_indices.append([int(d[1]) for d in facet])

Orient_Anything/render/speedup.py

@@ -0,0 +1,101 @@
+# import cython
+import numpy as np
+from math import sqrt
+
+
+def normalize(x, y, z):
+    unit = sqrt(x * x + y * y + z * z)
+    if unit == 0:
+        return 0, 0, 0
+    return x / unit, y / unit, z / unit
+
+
+def get_min_max(a, b, c):
+    min = a
+    max = a
+    if min > b:
+        min = b
+    if min > c:
+        min = c
+    if max < b:
+        max = b
+    if max < c:
+        max = c
+    return int(min), int(max)
+
+def dot_product(a0, a1, a2, b0, b1, b2):
+    r = a0 * b0 + a1 * b1 + a2 * b2
+    return r
+
+
+def cross_product(a0, a1, a2, b0, b1, b2):
+    x = a1 * b2 - a2 * b1
+    y = a2 * b0 - a0 * b2
+    z = a0 * b1 - a1 * b0
+    return x,y,z
+
+
+# @cython.boundscheck(False)
+def generate_faces(triangles, width, height):
+    """ draw the triangle faces with z buffer
+
+    Args:
+        triangles: groups of vertices
+
+    FYI:
+        * zbuffer, https://github.com/ssloy/tinyrenderer/wiki/Lesson-3:-Hidden-faces-removal-(z-buffer)
+        * uv mapping and perspective correction
+    """
+    i, j, k, length     = 0, 0, 0, 0
+    bcy, bcz, x, y, z   = 0.,0.,0.,0.,0.
+    a, b, c             = [0.,0.,0.],[0.,0.,0.],[0.,0.,0.]
+    m, bc               = [0.,0.,0.],[0.,0.,0.]
+    uva, uvb, uvc       = [0.,0.],[0.,0.],[0.,0.]
+    minx, maxx, miny, maxy = 0,0,0,0
+    length = triangles.shape[0]
+    zbuffer = {}
+    faces = []
+
+    for i in range(length):
+        a = triangles[i, 0, 0], triangles[i, 0, 1], triangles[i, 0, 2]
+        b = triangles[i, 1, 0], triangles[i, 1, 1], triangles[i, 1, 2]
+        c = triangles[i, 2, 0], triangles[i, 2, 1], triangles[i, 2, 2]
+        uva = triangles[i, 0, 3], triangles[i, 0, 4]
+        uvb = triangles[i, 1, 3], triangles[i, 1, 4]
+        uvc = triangles[i, 2, 3], triangles[i, 2, 4]
+        minx, maxx = get_min_max(a[0], b[0], c[0])
+        miny, maxy = get_min_max(a[1], b[1], c[1])
+        pixels = []
+        for j in range(minx, maxx + 2):
+            for k in range(miny - 1, maxy + 2):
+                # 必须显式转换成 double 参与底下的运算，不然结果是错的
+                x = j
+                y = k
+
+                m[0], m[1], m[2] = cross_product(c[0] - a[0], b[0] - a[0], a[0] - x, c[1] - a[1], b[1] - a[1], a[1] - y)
+                if abs(m[2]) > 0:
+                    bcy = m[1] / m[2]
+                    bcz = m[0] / m[2]
+                    bc = (1 - bcy - bcz, bcy, bcz)
+                else:
+                    continue
+
+                # here, -0.00001 because of the precision lose
+                if bc[0] < -0.00001 or bc[1] < -0.00001 or bc[2] < -0.00001:
+                    continue
+
+                z = 1 / (bc[0] / a[2] + bc[1] / b[2] + bc[2] / c[2])
+
+                # Blender 导出来的 uv 数据，跟之前的顶点数据有一样的问题，Y轴是个反的，
+                # 所以这里的纹理图片要旋转一下才能 work
+                v = (uva[0] * bc[0] / a[2] + uvb[0] * bc[1] / b[2] + uvc[0] * bc[2] / c[2]) * z * width
+                u = height - (uva[1] * bc[0] / a[2] + uvb[1] * bc[1] / b[2] + uvc[1] * bc[2] / c[2]) * z * height
+
+                # https://en.wikipedia.org/wiki/Pairing_function
+                idx = ((x + y) * (x + y + 1) + y) / 2
+                if zbuffer.get(idx) is None or zbuffer[idx] < z:
+                    zbuffer[idx] = z
+                    pixels.append((i, j, k, int(u) - 1, int(v) - 1))
+
+        faces.append(pixels)
+    return faces

processor/wrappers/grounding_dino.py

@@ -0,0 +1,23 @@
+import os
+import sys
+
+GSA_PATH = "/mnt/prev_nas/qhy_1/GenSpace/osdsynth/external/Grounded-Segment-Anything"
+sys.path.append(GSA_PATH)
+
+from GroundingDINO.groundingdino.util.inference import Model
+
+# GroundingDINO config and checkpoint
+GROUNDING_DINO_CONFIG_PATH = os.path.abspath(
+    os.path.join(GSA_PATH, "GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py")
+)
+# GROUNDING_DINO_CHECKPOINT_PATH = os.path.abspath(os.path.join(GSA_PATH, "./groundingdino_swint_ogc.pth"))
+GROUNDING_DINO_CHECKPOINT_PATH = "/mnt/prev_nas/qhy_1/GenSpace/osdsynth/external/Grounded-Segment-Anything/groundingdino_swint_ogc.pth"
+
+def get_grounding_dino_model(cfg, device):
+    grounding_dino_model = Model(
+        model_config_path=GROUNDING_DINO_CONFIG_PATH,
+        model_checkpoint_path=GROUNDING_DINO_CHECKPOINT_PATH,
+        device=device,
+    )
+
+    return grounding_dino_model

processor/wrappers/metric3d_v2.py

@@ -0,0 +1,250 @@
+import cv2
+import matplotlib
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+import trimesh
+from PIL import Image
+
+
+def get_depth_model(device):
+    depth_model = torch.hub.load("osdsynth/external/Metric3D", "metric3d_vit_giant2", pretrain=True, source='local')
+    return depth_model.to(device)
+
+
+def inference_depth(rgb_origin, intrinsic, depth_model):
+    # Code from # https://github.com/YvanYin/Metric3D/blob/main/hubconf.py, assume rgb_origin is in RGB
+    intrinsic = [intrinsic[0, 0], intrinsic[1, 1], intrinsic[0, 2], intrinsic[1, 2]]
+
+    #### ajust input size to fit pretrained model
+    # keep ratio resize
+    input_size = (616, 1064)  # for vit model
+    # input_size = (544, 1216) # for convnext model
+    h, w = rgb_origin.shape[:2]
+    scale = min(input_size[0] / h, input_size[1] / w)
+    rgb = cv2.resize(rgb_origin, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_LINEAR)
+    # remember to scale intrinsic, hold depth
+    intrinsic = [intrinsic[0] * scale, intrinsic[1] * scale, intrinsic[2] * scale, intrinsic[3] * scale]
+
+    # padding to input_size
+    padding = [123.675, 116.28, 103.53]
+    h, w = rgb.shape[:2]
+    pad_h = input_size[0] - h
+    pad_w = input_size[1] - w
+    pad_h_half = pad_h // 2
+    pad_w_half = pad_w // 2
+    rgb = cv2.copyMakeBorder(
+        rgb, pad_h_half, pad_h - pad_h_half, pad_w_half, pad_w - pad_w_half, cv2.BORDER_CONSTANT, value=padding
+    )
+    pad_info = [pad_h_half, pad_h - pad_h_half, pad_w_half, pad_w - pad_w_half]
+
+    #### normalize
+    mean = torch.tensor([123.675, 116.28, 103.53]).float()[:, None, None]
+    std = torch.tensor([58.395, 57.12, 57.375]).float()[:, None, None]
+    rgb = torch.from_numpy(rgb.transpose((2, 0, 1))).float()
+    rgb = torch.div((rgb - mean), std)
+    rgb = rgb[None, :, :, :].cuda()
+
+    with torch.no_grad():
+        pred_depth, confidence, output_dict = depth_model.inference({"input": rgb})
+
+    # un pad
+    pred_depth = pred_depth.squeeze()
+    pred_depth = pred_depth[
+        pad_info[0] : pred_depth.shape[0] - pad_info[1], pad_info[2] : pred_depth.shape[1] - pad_info[3]
+    ]
+
+    # upsample to original size
+    pred_depth = torch.nn.functional.interpolate(
+        pred_depth[None, None, :, :], rgb_origin.shape[:2], mode="bilinear"
+    ).squeeze()
+
+    #### de-canonical transform
+    canonical_to_real_scale = intrinsic[0] / 1000.0  # 1000.0 is the focal length of canonical camera
+    pred_depth = pred_depth * canonical_to_real_scale  # now the depth is metric
+    pred_depth = torch.clamp(pred_depth, 0, 300)
+    return pred_depth.detach().cpu().numpy()
+
+
+def depth_to_mesh(points, depth, image_rgb):
+    triangles = create_triangles(image_rgb.shape[0], image_rgb.shape[1], mask=~depth_edges_mask(depth))
+    mesh = trimesh.Trimesh(
+        vertices=points.reshape(-1, 3),
+        faces=triangles,
+        vertex_colors=image_rgb.reshape(-1, 3),
+    )
+    # mesh_t.export(save_pcd_dir+f'/{filename}_t_mesh.obj')
+    return mesh
+
+
+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 create_triangles(h, w, mask=None):
+    """
+    Reference: https://github.com/google-research/google-research/blob/e96197de06613f1b027d20328e06d69829fa5a89/infinite_nature/render_utils.py#L68
+    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
+
+
+def get_intrinsics(H, W, fov):
+    """Intrinsics for a pinhole camera model.
+
+    Assume fov of 55 degrees and central principal point.
+    """
+    # fy = 0.5 * H / np.tan(0.5 * fov * np.pi / 180.0)
+    # fx = 0.5 * W / np.tan(0.5 * fov * np.pi / 180.0)
+
+    focal = H / 2 / np.tan(np.radians(fov) / 2)
+
+    cx = 0.5 * W
+    cy = 0.5 * H
+    return np.array([[focal, 0, cx], [0, focal, cy], [0, 0, 1]])
+
+
+def depth_to_points(depth, R=None, t=None, fov=None, intrinsic=None):
+    if intrinsic is None:
+        K = get_intrinsics(depth.shape[1], depth.shape[2], fov)
+    else:
+        K = intrinsic
+    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
+
+    # G converts from your coordinate to PyTorch3D's coordinate system
+    G = np.eye(3)
+    G[0, 0] = -1.0
+    G[1, 1] = -1.0
+
+    return pts3D_2[:, :, :, :3, 0][0] @ G.T
+
+    # return (G[None, None, None, ...] @ pts3D_2)[:, :, :, :3, 0][0]
+
+    # return pts3D_2[:, :, :, :3, 0][0]
+
+
+def colorize_depth(
+    value,
+    vmin=None,
+    vmax=None,
+    cmap="inferno_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 * 0.0
+
+    # 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

processor/wrappers/perspective_fields.py

@@ -0,0 +1,135 @@
+import os
+import sys
+
+PPF_PATH = "/mnt/prev_nas/qhy_1/GenSpace/osdsynth/external/PerspectiveFields"
+sys.path.append(PPF_PATH)  # This is needed for the following imports in this file
+
+PPF_PATH_ABS = os.path.abspath(PPF_PATH)
+
+import copy
+import os
+
+import cv2
+import numpy as np
+import torch
+from perspective2d import PerspectiveFields
+from perspective2d.utils import draw_from_r_p_f_cx_cy, draw_perspective_fields
+
+
+def create_rotation_matrix(
+    roll: float,
+    pitch: float,
+    yaw: float,
+    degrees: bool = False,
+) -> np.ndarray:
+    r"""Create rotation matrix from extrinsic parameters
+    Args:
+        roll (float): camera rotation about camera frame z-axis
+        pitch (float): camera rotation about camera frame x-axis
+        yaw (float): camera rotation about camera frame y-axis
+
+    Returns:
+        np.ndarray: rotation R_z @ R_x @ R_y
+    """
+    if degrees:
+        roll = np.radians(roll)
+        pitch = np.radians(pitch)
+        yaw = np.radians(yaw)
+    # calculate rotation about the x-axis
+    R_x = np.array(
+        [
+            [1.0, 0.0, 0.0],
+            [0.0, np.cos(pitch), np.sin(pitch)],
+            [0.0, -np.sin(pitch), np.cos(pitch)],
+        ]
+    )
+    # calculate rotation about the y-axis
+    R_y = np.array(
+        [
+            [np.cos(yaw), 0.0, -np.sin(yaw)],
+            [0.0, 1.0, 0.0],
+            [np.sin(yaw), 0.0, np.cos(yaw)],
+        ]
+    )
+    # calculate rotation about the z-axis
+    R_z = np.array(
+        [
+            [np.cos(roll), np.sin(roll), 0.0],
+            [-np.sin(roll), np.cos(roll), 0.0],
+            [0.0, 0.0, 1.0],
+        ]
+    )
+
+    return R_z @ R_x @ R_y
+
+
+def resize_fix_aspect_ratio(img, field, target_width=None, target_height=None):
+    height = img.shape[0]
+    width = img.shape[1]
+    if target_height is None:
+        factor = target_width / width
+    elif target_width is None:
+        factor = target_height / height
+    else:
+        factor = max(target_width / width, target_height / height)
+    if factor == target_width / width:
+        target_height = int(height * factor)
+    else:
+        target_width = int(width * factor)
+
+    img = cv2.resize(img, (target_width, target_height))
+    for key in field:
+        if key not in ["up", "lati"]:
+            continue
+        tmp = field[key].numpy()
+        transpose = len(tmp.shape) == 3
+        if transpose:
+            tmp = tmp.transpose(1, 2, 0)
+        tmp = cv2.resize(tmp, (target_width, target_height))
+        if transpose:
+            tmp = tmp.transpose(2, 0, 1)
+        field[key] = torch.tensor(tmp)
+    return img, field
+
+
+def run_perspective_fields_model(model, image_bgr):
+
+    pred = model.inference(img_bgr=image_bgr)
+    field = {
+        "up": pred["pred_gravity_original"].cpu().detach(),
+        "lati": pred["pred_latitude_original"].cpu().detach(),
+    }
+    img, field = resize_fix_aspect_ratio(image_bgr[..., ::-1], field, 640)
+
+    # Draw perspective field from ParamNet predictions
+    param_vis = draw_from_r_p_f_cx_cy(
+        img,
+        pred["pred_roll"].item(),
+        pred["pred_pitch"].item(),
+        pred["pred_general_vfov"].item(),
+        pred["pred_rel_cx"].item(),
+        pred["pred_rel_cy"].item(),
+        "deg",
+        up_color=(0, 1, 0),
+    ).astype(np.uint8)
+    param_vis = cv2.cvtColor(param_vis, cv2.COLOR_RGB2BGR)
+
+    param = {
+        "roll": pred["pred_roll"].cpu().item(),
+        "pitch": pred["pred_pitch"].cpu().item(),
+    }
+
+    return param_vis, param
+
+
+def get_perspective_fields_model(cfg, device):
+    MODEL_ID = "Paramnet-360Cities-edina-centered"
+    # MODEL_ID = 'Paramnet-360Cities-edina-uncentered'
+    # MODEL_ID = 'PersNet_Paramnet-GSV-centered'
+    # MODEL_ID = 'PersNet_Paramnet-GSV-uncentered'
+    # MODEL_ID = 'PersNet-360Cities'
+    # feel free to test with uncentered or centered depending on your data
+
+    PerspectiveFields.versions()
+    pf_model = PerspectiveFields(MODEL_ID).eval().cuda()
+    return pf_model

processor/wrappers/ram.py

@@ -0,0 +1,64 @@
+import os
+import sys
+sys.path.append("/mnt/prev_nas/qhy_1/GenSpace/osdsynth/external/Grounded-Segment-Anything/recognize-anything")
+from typing import List
+
+import torchvision.transforms as TS
+from ram import inference_ram
+from ram.models import ram
+
+
+def run_tagging_model(cfg, raw_image, tagging_model):
+    res = inference_ram(raw_image, tagging_model)
+    caption = "NA"
+    tags = res[0].strip(" ").replace("  ", " ").replace(" |", ",")
+    print("Tags: ", tags)
+
+    # Currently ", " is better for detecting single tags
+    # while ". " is a little worse in some case
+    text_prompt = res[0].replace(" |", ",")
+
+    if cfg.rm_bg_classes:
+        cfg.remove_classes += cfg.bg_classes
+
+    classes = process_tag_classes(
+        text_prompt,
+        add_classes=cfg.add_classes,
+        remove_classes=cfg.remove_classes,
+    )
+    print("Tags (Final): ", classes)
+    return classes
+
+
+def process_tag_classes(text_prompt: str, add_classes: List[str] = [], remove_classes: List[str] = []) -> list[str]:
+    """Convert a text prompt from Tag2Text to a list of classes."""
+    classes = text_prompt.split(",")
+    classes = [obj_class.strip() for obj_class in classes]
+    classes = [obj_class for obj_class in classes if obj_class != ""]
+
+    for c in add_classes:
+        if c not in classes:
+            classes.append(c)
+
+    for c in remove_classes:
+        classes = [obj_class for obj_class in classes if c not in obj_class.lower()]
+
+    return classes
+
+
+def get_tagging_model(cfg, device):
+    RAM_CHECKPOINT_PATH = os.path.abspath(
+        "osdsynth/external/Grounded-Segment-Anything/recognize-anything/ram_swin_large_14m.pth"
+    )
+    tagging_model = ram(pretrained=RAM_CHECKPOINT_PATH, image_size=384, vit="swin_l")
+
+    tagging_model = tagging_model.eval().to(device)
+    tagging_transform = TS.Compose(
+        [
+            TS.Resize((384, 384)),
+            TS.ToTensor(),
+            TS.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ]
+    )
+
+    return tagging_transform, tagging_model

processor/wrappers/sam.py

@@ -0,0 +1,324 @@
+import os
+import sys
+from typing import Any, Dict, Generator, ItemsView, List, Tuple
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+
+GSA_PATH = "/mnt/prev_nas/qhy_1/GenSpace/osdsynth/external/Grounded-Segment-Anything"
+sys.path.append(GSA_PATH)
+
+from segment_anything.segment_anything import SamAutomaticMaskGenerator, SamPredictor, sam_hq_model_registry, sam_model_registry
+
+# Segment-Anything checkpoint
+SAM_ENCODER_VERSION = "vit_h"
+SAM_CHECKPOINT_PATH = os.path.join(GSA_PATH, "./sam_vit_h_4b8939.pth")
+
+# Segment-Anything checkpoint
+SAM_HQ_ENCODER_VERSION = "vit_h"
+SAM_HQ_CHECKPOINT_PATH = os.path.join(GSA_PATH, "./sam_hq_vit_h.pth")
+
+# Prompting SAM with detected boxes
+def get_sam_segmentation_from_xyxy(sam_predictor: SamPredictor, image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:
+    sam_predictor.set_image(image)
+    result_masks = []
+    for box in xyxy:
+        masks, scores, logits = sam_predictor.predict(box=box, multimask_output=True)
+        index = np.argmax(scores)
+        result_masks.append(masks[index])
+    return np.array(result_masks)
+
+
+def get_sam_predictor(variant: str, device: str | int) -> SamPredictor:
+    if variant == "sam":
+        sam = sam_model_registry[SAM_ENCODER_VERSION](checkpoint=SAM_CHECKPOINT_PATH)
+        sam.to(device)
+        sam_predictor = SamPredictor(sam)
+        return sam_predictor
+
+    if variant == "sam-hq":
+        print("Using SAM-HQ")
+        sam = sam_hq_model_registry[SAM_HQ_ENCODER_VERSION](checkpoint=SAM_HQ_CHECKPOINT_PATH)
+        sam.to(device)
+        sam_predictor = SamPredictor(sam)
+        return sam_predictor
+
+    else:
+        raise NotImplementedError
+
+
+def get_sam_mask_generator(variant: str, device: str | int) -> SamAutomaticMaskGenerator:
+    if variant == "sam":
+        sam = sam_model_registry[SAM_ENCODER_VERSION](checkpoint=SAM_CHECKPOINT_PATH)
+        sam.to(device)
+        mask_generator = SamAutomaticMaskGenerator(
+            model=sam,
+            points_per_side=12,
+            points_per_batch=144,
+            pred_iou_thresh=0.88,
+            stability_score_thresh=0.95,
+            crop_n_layers=0,
+            min_mask_region_area=100,
+        )
+        return mask_generator
+    elif variant == "fastsam":
+        raise NotImplementedError
+    else:
+        raise NotImplementedError
+
+
+def convert_detections_to_list(detections_dict, classes):
+    detection_list = []
+    for i in range(len(detections_dict["xyxy"])):
+        detection = {
+            "class_name": classes[detections_dict["class_id"][i]],  # Lookup class name using class_id
+            "xyxy": detections_dict["xyxy"][i],  # Assuming detections.xyxy is a numpy array
+            "confidence": detections_dict["confidence"][i].item(),  # Convert numpy scalar to Python scalar
+            "class_id": detections_dict["class_id"][i].item(),
+            "box_area": detections_dict["box_area"][i].item(),
+            "mask": detections_dict["mask"][i],
+            "subtracted_mask": detections_dict["subtracted_mask"][i],
+            "rle": detections_dict["rle"][i],
+            "area": detections_dict["area"][i],
+        }
+        detection_list.append(detection)
+    return detection_list
+
+
+def convert_detections_to_dict(detections, classes, image_crops=None, image_feats=None, text_feats=None):
+    # Convert the detections to a dict. The elements are in np.array
+    results = {
+        "xyxy": detections.xyxy,
+        "confidence": detections.confidence,
+        "class_id": detections.class_id,
+        "box_area": detections.box_area,
+        "mask": detections.mask,
+        "area": detections.area,
+        "classes": classes,
+    }
+    return results
+
+
+def mask_subtract_contained(xyxy: np.ndarray, mask: np.ndarray, th1=0.8, th2=0.7):
+    """Compute the containing relationship between all pair of bounding boxes. For each mask, subtract the mask of
+    bounding boxes that are contained by it.
+
+    Args:
+        xyxy: (N, 4), in (x1, y1, x2, y2) format
+        mask: (N, H, W), binary mask
+        th1: float, threshold for computing intersection over box1
+        th2: float, threshold for computing intersection over box2
+
+    Returns:
+        mask_sub: (N, H, W), binary mask
+    """
+    N = xyxy.shape[0]  # number of boxes
+
+    # Get areas of each xyxy
+    areas = (xyxy[:, 2] - xyxy[:, 0]) * (xyxy[:, 3] - xyxy[:, 1])  # (N,)
+
+    # Compute intersection boxes
+    lt = np.maximum(xyxy[:, None, :2], xyxy[None, :, :2])  # left-top points (N, N, 2)
+    rb = np.minimum(xyxy[:, None, 2:], xyxy[None, :, 2:])  # right-bottom points (N, N, 2)
+
+    inter = (rb - lt).clip(min=0)  # intersection sizes (dx, dy), if no overlap, clamp to zero (N, N, 2)
+
+    # Compute areas of intersection boxes
+    inter_areas = inter[:, :, 0] * inter[:, :, 1]  # (N, N)
+
+    inter_over_box1 = inter_areas / areas[:, None]  # (N, N)
+    # inter_over_box2 = inter_areas / areas[None, :] # (N, N)
+    inter_over_box2 = inter_over_box1.T  # (N, N)
+
+    # if the intersection area is smaller than th2 of the area of box1,
+    # and the intersection area is larger than th1 of the area of box2,
+    # then box2 is considered contained by box1
+    contained = (inter_over_box1 < th2) & (inter_over_box2 > th1)  # (N, N)
+    contained_idx = contained.nonzero()  # (num_contained, 2)
+
+    mask_sub = mask.copy()  # (N, H, W)
+    # mask_sub[contained_idx[0]] = mask_sub[contained_idx[0]] & (~mask_sub[contained_idx[1]])
+    for i in range(len(contained_idx[0])):
+        mask_sub[contained_idx[0][i]] = mask_sub[contained_idx[0][i]] & (~mask_sub[contained_idx[1][i]])
+
+    return mask_sub, contained
+
+
+def filter_detections(cfg, detections_dict: dict, image: np.ndarray):
+    # If no detection at all
+    if len(detections_dict["xyxy"]) == 0:
+        return detections_dict
+
+    # Filter out the objects based on various criteria
+    idx_to_keep = []
+    for obj_idx in range(len(detections_dict["xyxy"])):
+        class_name = detections_dict["classes"][detections_dict["class_id"][obj_idx]]
+
+        # Skip masks that are too small
+        if detections_dict["mask"][obj_idx].sum() < max(cfg.mask_area_threshold, 10):
+            print(f"Skipping {class_name} mask with too few points")
+            continue
+
+        # Skip the BG classes
+        if cfg.skip_bg and class_name in cfg.bg_classes:
+            print(f"Skipping {class_name} as it is a background class")
+            continue
+
+        # Skip the non-background boxes that are too large
+        if class_name not in cfg.bg_classes:
+            x1, y1, x2, y2 = detections_dict["xyxy"][obj_idx]
+            bbox_area = (x2 - x1) * (y2 - y1)
+            image_area = image.shape[0] * image.shape[1]
+            if bbox_area > cfg.max_bbox_area_ratio * image_area:
+                print(f"Skipping {class_name} with area {bbox_area} > {cfg.max_bbox_area_ratio} * {image_area}")
+                continue
+
+        # Skip masks with low confidence
+        if detections_dict["confidence"][obj_idx] < cfg.mask_conf_threshold:
+            print(
+                f"Skipping {class_name} with confidence {detections_dict['confidence'][obj_idx]} < {cfg.mask_conf_threshold}"
+            )
+            continue
+
+        idx_to_keep.append(obj_idx)
+
+    for k in detections_dict.keys():
+        if isinstance(detections_dict[k], str) or k == "classes":  # Captions
+            continue
+        elif isinstance(detections_dict[k], list):
+            detections_dict[k] = [detections_dict[k][i] for i in idx_to_keep]
+        elif isinstance(detections_dict[k], np.ndarray):
+            detections_dict[k] = detections_dict[k][idx_to_keep]
+        else:
+            raise NotImplementedError(f"Unhandled type {type(detections_dict[k])}")
+
+    return detections_dict
+
+
+def sort_detections_by_area(detections_dict):
+    # Sort the detections by area, use negative to sort from large to small
+    sorted_indices = np.argsort(-detections_dict["area"])
+    for key in detections_dict.keys():
+        if isinstance(detections_dict[key], np.ndarray):  # Check to ensure it's an array
+            detections_dict[key] = detections_dict[key][sorted_indices]
+    return detections_dict
+
+
+def post_process_mask(detections_dict):
+    sam_masks = torch.tensor(detections_dict["subtracted_mask"])
+    uncompressed_mask_rles = mask_to_rle_pytorch(sam_masks)
+    rle_masks_list = [coco_encode_rle(uncompressed_mask_rles[i]) for i in range(len(uncompressed_mask_rles))]
+    detections_dict["rle"] = rle_masks_list
+    return detections_dict
+
+
+def crop_image_and_mask(image: Image, mask: np.ndarray, x1: int, y1: int, x2: int, y2: int, padding: int = 0):
+    """Crop the image and mask with some padding.
+
+    I made a single function that crops both the image and the mask at the same time because I was getting shape
+    mismatches when I cropped them separately.This way I can check that they are the same shape.
+    """
+
+    image = np.array(image)
+    # Verify initial dimensions
+    if image.shape[:2] != mask.shape:
+        print(f"Initial shape mismatch: Image shape {image.shape} != Mask shape {mask.shape}")
+        return None, None
+
+    # Define the cropping coordinates
+    x1 = max(0, x1 - padding)
+    y1 = max(0, y1 - padding)
+    x2 = min(image.shape[1], x2 + padding)
+    y2 = min(image.shape[0], y2 + padding)
+    # round the coordinates to integers
+    x1, y1, x2, y2 = round(x1), round(y1), round(x2), round(y2)
+
+    # Crop the image and the mask
+    image_crop = image[y1:y2, x1:x2]
+    mask_crop = mask[y1:y2, x1:x2]
+
+    # Verify cropped dimensions
+    if image_crop.shape[:2] != mask_crop.shape:
+        print(
+            "Cropped shape mismatch: Image crop shape {} != Mask crop shape {}".format(
+                image_crop.shape, mask_crop.shape
+            )
+        )
+        return None, None
+
+    # convert the image back to a pil image
+    image_crop = Image.fromarray(image_crop)
+
+    return image_crop, mask_crop
+
+
+def crop_detections_with_xyxy(cfg, image, detections_list):
+    for idx, detection in enumerate(detections_list):
+        x1, y1, x2, y2 = detection["xyxy"]
+        image_crop, mask_crop = crop_image_and_mask(image, detection["mask"], x1, y1, x2, y2, padding=10)
+        if cfg.masking_option == "blackout":
+            image_crop_modified = blackout_nonmasked_area(image_crop, mask_crop)
+        elif cfg.masking_option == "red_outline":
+            image_crop_modified = draw_red_outline(image_crop, mask_crop)
+        else:
+            image_crop_modified = image_crop  # No modification
+        detections_list[idx]["image_crop"] = image_crop
+        detections_list[idx]["mask_crop"] = mask_crop
+        detections_list[idx]["image_crop_modified"] = image_crop_modified
+    return detections_list
+
+
+def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
+    """
+    Encodes masks to an uncompressed RLE, in the format expected by
+    pycoco tools.
+    """
+    # Put in fortran order and flatten h,w
+    b, h, w = tensor.shape
+    tensor = tensor.permute(0, 2, 1).flatten(1)
+
+    # Compute change indices
+    diff = tensor[:, 1:] ^ tensor[:, :-1]
+    change_indices = diff.nonzero()
+
+    # Encode run length
+    out = []
+    for i in range(b):
+        cur_idxs = change_indices[change_indices[:, 0] == i, 1]
+        cur_idxs = torch.cat(
+            [
+                torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
+                cur_idxs + 1,
+                torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),
+            ]
+        )
+        btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
+        counts = [] if tensor[i, 0] == 0 else [0]
+        counts.extend(btw_idxs.detach().cpu().tolist())
+        out.append({"size": [h, w], "counts": counts})
+    return out
+
+
+def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
+    """Compute a binary mask from an uncompressed RLE."""
+    h, w = rle["size"]
+    mask = np.empty(h * w, dtype=bool)
+    idx = 0
+    parity = False
+    for count in rle["counts"]:
+        mask[idx : idx + count] = parity
+        idx += count
+        parity ^= True
+    mask = mask.reshape(w, h)
+    return mask.transpose()  # Put in C order
+
+
+def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
+    from pycocotools import mask as mask_utils  # type: ignore
+
+    h, w = uncompressed_rle["size"]
+    rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
+    rle["counts"] = rle["counts"].decode("utf-8")  # Necessary to serialize with json
+    return rle