ykcheungab
563 lines
21 KiB
Python
563 lines
21 KiB
Python
import argparse
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import os
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import sys
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import time
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import cv2
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import numpy as np
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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REPO_ROOT = os.path.abspath(os.path.join(SCRIPT_DIR, "..", ".."))
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SRC_DIR = os.path.join(REPO_ROOT, "src")
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if SRC_DIR not in sys.path:
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sys.path.insert(0, SRC_DIR)
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import orisys
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def load_flatten_remap(path):
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remap = np.load(path)
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result = {
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"map_x": remap["map_x"].astype(np.float32),
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"map_y": remap["map_y"].astype(np.float32),
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"valid_mask": remap["valid_mask"].astype(bool),
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}
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if "flat_xyz" in remap:
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result["flat_xyz"] = remap["flat_xyz"].astype(np.float32)
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return result
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def flip_sensor_frames_vertical(sensor):
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"""Flip captured BGR/gray frames to match Blender-exported map orientation."""
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if sensor._img_bgr_raw is not None:
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sensor._img_bgr_raw = cv2.flip(sensor._img_bgr_raw, 0)
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if sensor.img is not None:
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cv2.flip(sensor.img, 0, dst=sensor.img)
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def flatten_frame(frame, remap, border_value=0):
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flat = cv2.remap(
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frame,
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remap["map_x"],
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remap["map_y"],
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interpolation=cv2.INTER_LINEAR,
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borderMode=cv2.BORDER_CONSTANT,
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borderValue=border_value,
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)
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flat = flat.copy()
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flat[~remap["valid_mask"]] = border_value
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return flat
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def load_exported_data(data_dir):
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mesh = np.load(os.path.join(data_dir, "mesh.npz"))
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cam_data = np.load(os.path.join(data_dir, "camera_intrinsics.npz"))
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xyz_map_path = os.path.join(data_dir, "pixel_to_xyz.npz")
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if not os.path.exists(xyz_map_path):
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raise FileNotFoundError(f"Missing precomputed 3D map: {xyz_map_path}")
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xyz_map = np.load(xyz_map_path)["xyz_map"]
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width = int(cam_data["width"])
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height = int(cam_data["height"])
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if xyz_map.shape[:2] != (height, width):
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raise ValueError(
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f"pixel_to_xyz shape {xyz_map.shape[:2]} does not match camera size {(height, width)}"
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)
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return {
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"vertices": mesh["vertices"],
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"faces": mesh["faces"],
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"uv_data": mesh["uv_data"],
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"loop_to_vertex": mesh["loop_to_vertex"],
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"K": cam_data["K"],
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"width": width,
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"height": height,
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"world_to_cam": np.load(os.path.join(data_dir, "world_to_cam.npy")),
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"xyz_map": xyz_map,
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}
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def parse_source(value):
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value = str(value).strip()
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for prefix in ("camera:", "cam:", "webcam:"):
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if value.lower().startswith(prefix):
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value = value[len(prefix):].strip()
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break
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try:
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return int(value)
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except ValueError:
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return value
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def prepare_static_points(data, display_stride):
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xyz_map = data["xyz_map"]
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valid_mask = ~np.isnan(xyz_map[:, :, 0])
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valid_points = xyz_map[valid_mask].reshape(-1, 3)
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if len(valid_points) == 0:
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raise ValueError("pixel_to_xyz.npz contains no valid mesh hit points.")
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display_indices = np.arange(len(valid_points))[::max(1, int(display_stride))]
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return valid_mask, display_indices, valid_points[display_indices]
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def align_image_to_map(img_bgr, data, valid_mask, display_indices, flip_vertical=True, flip_horizontal=True):
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target_w = int(data["width"])
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target_h = int(data["height"])
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h, w = img_bgr.shape[:2]
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if (w, h) != (target_w, target_h):
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img_bgr = cv2.resize(img_bgr, (target_w, target_h), interpolation=cv2.INTER_LINEAR)
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img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
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if flip_vertical:
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img_rgb = np.flipud(img_rgb).copy()
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if flip_horizontal:
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img_rgb = np.fliplr(img_rgb).copy()
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colors = img_rgb.astype(np.float64) / 255.0
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return colors[valid_mask].reshape(-1, 3)[display_indices]
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def arrow_map_reference_colors(count, color=(0.04, 0.04, 0.04)):
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return np.tile(np.asarray(color, dtype=np.float64), (count, 1))
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def magnitude_map_colors(flow, valid_mask, display_indices, max_magnitude=None):
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mag = np.linalg.norm(flow, axis=2)
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valid_mag = mag[valid_mask].reshape(-1)
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display_mag = valid_mag[display_indices]
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if max_magnitude is None or max_magnitude <= 0:
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max_magnitude = max(float(np.percentile(display_mag, 95)), 1e-6)
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value = np.clip(display_mag / max_magnitude, 0.0, 1.0)
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return np.column_stack(
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(
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value,
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np.clip(1.0 - np.abs(value - 0.5) * 2.0, 0.0, 1.0),
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1.0 - value,
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)
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).astype(np.float64)
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def align_scalar_field_to_map(field, data, flip_vertical=True):
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if field.ndim == 3:
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field = np.linalg.norm(field, axis=2)
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target_w = int(data["width"])
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target_h = int(data["height"])
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h, w = field.shape[:2]
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if (w, h) != (target_w, target_h):
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field = cv2.resize(field, (target_w, target_h), interpolation=cv2.INTER_LINEAR)
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else:
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field = field.copy()
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if flip_vertical:
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field = np.flipud(field).copy()
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return field
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def scalar_map_colors(field, valid_mask, display_indices, value_min=None, value_max=None):
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valid_values = field[valid_mask].reshape(-1)
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display_values = valid_values[display_indices]
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finite_values = display_values[np.isfinite(display_values)]
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if len(finite_values) == 0:
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return arrow_map_reference_colors(len(display_indices))
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if value_min is None:
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value_min = float(np.percentile(finite_values, 5))
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if value_max is None:
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value_max = float(np.percentile(finite_values, 95))
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if value_max <= value_min:
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value_max = value_min + 1e-6
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value = np.clip((display_values - value_min) / (value_max - value_min), 0.0, 1.0)
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return np.column_stack(
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(
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value,
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np.clip(1.0 - np.abs(value - 0.5) * 2.0, 0.0, 1.0),
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1.0 - value,
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)
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).astype(np.float64)
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def align_vector_field_to_map(field, data, flip_vertical=True, scale_as_flow=True):
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target_w = int(data["width"])
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target_h = int(data["height"])
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h, w = field.shape[:2]
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if (w, h) != (target_w, target_h):
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sx = target_w / max(w, 1)
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sy = target_h / max(h, 1)
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field = cv2.resize(field, (target_w, target_h), interpolation=cv2.INTER_LINEAR)
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if scale_as_flow:
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field[:, :, 0] *= sx
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field[:, :, 1] *= sy
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else:
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field = field.copy()
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if flip_vertical:
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field = np.flipud(field).copy()
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field[:, :, 1] *= -1.0
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return field
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def estimate_surface_normals(xyz_map):
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valid = ~np.isnan(xyz_map[:, :, 0])
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filled = xyz_map.copy()
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filled[~valid] = 0
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d_y = np.gradient(filled, axis=0)
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d_x = np.gradient(filled, axis=1)
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normals = np.cross(d_x, d_y)
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norm = np.linalg.norm(normals, axis=2, keepdims=True)
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normals = np.divide(normals, norm, out=np.zeros_like(normals), where=norm > 1e-8)
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normals[~valid] = 0
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return normals
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def line_colors(mag, arrow_map_style):
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if len(mag) == 0:
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return np.empty((0, 3), dtype=np.float64)
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if arrow_map_style:
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return np.tile(np.array([0.0, 0.9, 1.0], dtype=np.float64), (len(mag), 1))
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norm_mag = mag / max(float(np.percentile(mag, 95)), 1e-6)
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norm_mag = np.clip(norm_mag, 0.0, 1.0)
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return np.column_stack((norm_mag, 1.0 - norm_mag, np.zeros_like(norm_mag))).astype(np.float64)
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def lines_from_vectors(starts, vectors, mag, flow_scale, arrow_map_style):
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vec_norm = np.linalg.norm(vectors, axis=1)
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good = np.isfinite(vec_norm) & (vec_norm > 1e-8)
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starts, vectors, mag = starts[good], vectors[good], mag[good]
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if len(starts) == 0:
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return np.empty((0, 3)), np.empty((0, 2), dtype=np.int32), np.empty((0, 3))
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points = np.empty((len(starts) * 2, 3), dtype=np.float64)
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points[0::2] = starts
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points[1::2] = starts + vectors * flow_scale
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lines = np.column_stack((np.arange(0, len(points), 2), np.arange(1, len(points), 2))).astype(np.int32)
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return points, lines, line_colors(mag, arrow_map_style)
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def build_flow_lines(
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xyz_map,
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valid_mask,
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flow,
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arrow_stride=32,
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flow_scale=1.0,
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min_magnitude=0.5,
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arrow_map_style=False,
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):
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h, w = flow.shape[:2]
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ys, xs = np.mgrid[0:h:arrow_stride, 0:w:arrow_stride]
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ys = ys.reshape(-1)
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xs = xs.reshape(-1)
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sample_flow = flow[ys, xs]
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mag = np.linalg.norm(sample_flow, axis=1)
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keep = valid_mask[ys, xs] & (mag >= min_magnitude)
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xs, ys, sample_flow, mag = xs[keep], ys[keep], sample_flow[keep], mag[keep]
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if len(xs) == 0:
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return np.empty((0, 3)), np.empty((0, 2), dtype=np.int32), np.empty((0, 3))
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tx = np.rint(xs + sample_flow[:, 0]).astype(np.int32)
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ty = np.rint(ys + sample_flow[:, 1]).astype(np.int32)
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inside = (0 <= tx) & (tx < w) & (0 <= ty) & (ty < h)
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xs, ys, tx, ty, mag = xs[inside], ys[inside], tx[inside], ty[inside], mag[inside]
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if len(xs) == 0:
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return np.empty((0, 3)), np.empty((0, 2), dtype=np.int32), np.empty((0, 3))
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valid_target = valid_mask[ty, tx]
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xs, ys, tx, ty, mag = xs[valid_target], ys[valid_target], tx[valid_target], ty[valid_target], mag[valid_target]
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starts = xyz_map[ys, xs]
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vectors = xyz_map[ty, tx] - starts
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return lines_from_vectors(starts, vectors, mag, flow_scale, arrow_map_style)
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def build_shear_normal_lines(
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xyz_map,
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valid_mask,
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vshear,
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vnormal,
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normals,
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arrow_stride=32,
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flow_scale=1.0,
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min_magnitude=0.01,
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normal_gain=1.0,
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arrow_map_style=False,
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):
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h, w = vshear.shape[:2]
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ys, xs = np.mgrid[0:h:arrow_stride, 0:w:arrow_stride]
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ys = ys.reshape(-1)
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xs = xs.reshape(-1)
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shear = vshear[ys, xs]
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normal_field = vnormal[ys, xs]
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shear_mag = np.linalg.norm(shear, axis=1)
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normal_mag = np.linalg.norm(normal_field, axis=1) * normal_gain
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combined_mag = shear_mag + normal_mag
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keep = valid_mask[ys, xs] & (combined_mag >= min_magnitude)
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xs, ys, shear, shear_mag, normal_mag, combined_mag = (
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xs[keep],
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ys[keep],
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shear[keep],
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shear_mag[keep],
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normal_mag[keep],
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combined_mag[keep],
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)
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if len(xs) == 0:
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return np.empty((0, 3)), np.empty((0, 2), dtype=np.int32), np.empty((0, 3))
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tx = np.rint(xs + shear[:, 0]).astype(np.int32)
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ty = np.rint(ys + shear[:, 1]).astype(np.int32)
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inside = (0 <= tx) & (tx < w) & (0 <= ty) & (ty < h)
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xs, ys, tx, ty, shear_mag, normal_mag, combined_mag = (
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xs[inside],
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ys[inside],
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tx[inside],
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ty[inside],
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shear_mag[inside],
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normal_mag[inside],
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combined_mag[inside],
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)
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if len(xs) == 0:
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return np.empty((0, 3)), np.empty((0, 2), dtype=np.int32), np.empty((0, 3))
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valid_target = valid_mask[ty, tx]
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xs, ys, tx, ty, shear_mag, normal_mag, combined_mag = (
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xs[valid_target],
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ys[valid_target],
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tx[valid_target],
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ty[valid_target],
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shear_mag[valid_target],
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normal_mag[valid_target],
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combined_mag[valid_target],
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)
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starts = xyz_map[ys, xs]
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tangent = xyz_map[ty, tx] - starts
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tangent_norm = np.linalg.norm(tangent, axis=1, keepdims=True)
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tangent_unit = np.divide(tangent, tangent_norm, out=np.zeros_like(tangent), where=tangent_norm > 1e-8)
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normal_unit = normals[ys, xs]
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shear_ratio = shear_mag / np.maximum(combined_mag, 1e-8)
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normal_ratio = normal_mag / np.maximum(combined_mag, 1e-8)
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vectors = tangent_unit * shear_ratio[:, None] + normal_unit * normal_ratio[:, None]
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vector_norm = np.linalg.norm(vectors, axis=1, keepdims=True)
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vectors = np.divide(vectors, vector_norm, out=np.zeros_like(vectors), where=vector_norm > 1e-8)
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return lines_from_vectors(starts, vectors, combined_mag, flow_scale, arrow_map_style)
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def init_viewer(cloud_points, cloud_colors, flow_points, flow_lines, flow_colors, arrow_map_style=False):
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import open3d as o3d
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vis = o3d.visualization.Visualizer()
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vis.create_window(window_name="SDK 3D Flow", width=1280, height=720)
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pcd = o3d.geometry.PointCloud()
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pcd.points = o3d.utility.Vector3dVector(cloud_points.astype(np.float64))
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pcd.colors = o3d.utility.Vector3dVector(cloud_colors.astype(np.float64))
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vis.add_geometry(pcd)
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line_set = o3d.geometry.LineSet()
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line_set.points = o3d.utility.Vector3dVector(flow_points.astype(np.float64))
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line_set.lines = o3d.utility.Vector2iVector(flow_lines)
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line_set.colors = o3d.utility.Vector3dVector(flow_colors.astype(np.float64))
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vis.add_geometry(line_set)
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render_option = vis.get_render_option()
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render_option.point_size = 1.0 if arrow_map_style else 2.0
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render_option.line_width = 2.0
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render_option.background_color = np.array([0.0, 0.0, 0.0])
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vis.poll_events()
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vis.update_renderer()
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return vis, pcd, line_set, o3d
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def run_sdk_3d_flow(args):
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data = load_exported_data(args.data_dir)
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flatten_remap = load_flatten_remap(args.flatten_remap) if args.flatten_remap else None
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view_data = data
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if flatten_remap is not None:
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if "flat_xyz" not in flatten_remap:
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raise ValueError("Flatten remap must contain flat_xyz for 3D visualization.")
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view_data = dict(data)
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view_data["xyz_map"] = flatten_remap["flat_xyz"]
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view_data["height"], view_data["width"] = view_data["xyz_map"].shape[:2]
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valid_mask, display_indices, cloud_points = prepare_static_points(view_data, args.display_stride)
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surface_normals = estimate_surface_normals(view_data["xyz_map"])
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flip_vertical = not args.no_flip_vertical
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sensor = orisys.Sensor(
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parse_source(args.source),
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config_name=args.config,
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isstitch=False,
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cuda=args.cuda,
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verbose=args.verbose,
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mirror=not args.no_mirror,
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)
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vis = pcd = line_set = o3d = None
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frame_count = 0
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t0 = time.perf_counter()
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try:
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while True:
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if vis is not None and not vis.poll_events():
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break
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if sensor.get_img() is None:
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break
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if flip_vertical:
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flip_sensor_frames_vertical(sensor)
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flat_bgr = None
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if flatten_remap is not None:
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flat_bgr = flatten_frame(sensor.read_info(sensor.info.IMG)[0], flatten_remap)
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flat_gray = cv2.cvtColor(flat_bgr, cv2.COLOR_BGR2GRAY)
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sensor.img = flat_gray
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sensor._img_bgr_raw = flat_bgr
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sensor.img_size_raw = flat_bgr.shape
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needs_decomposition = args.vector_mode in ("shear-normal", "depth-map")
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sensor.compute_deformation(decompose=needs_decomposition, check_motion=False)
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if args.vector_mode == "depth-map":
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sensor.compute_contact()
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flow, img_bgr, vshear, vnormal, depth_map = sensor.read_info(
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sensor.info.VRAW,
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sensor.info.IMG,
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sensor.info.VSHEAR,
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sensor.info.VNORMAL,
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sensor.info.DEPTH,
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)
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if flat_bgr is not None:
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img_bgr = flat_bgr
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active_data = view_data if flatten_remap is not None else data
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# Input is already flipped above; flattened domain uses flat_xyz directly.
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flip_for_map = False if (flatten_remap is not None or flip_vertical) else True
|
|
flow_map = align_vector_field_to_map(flow, active_data, flip_vertical=flip_for_map)
|
|
if args.vector_mode in ("vraw-magnitude", "vraw-magnitude-arrows"):
|
|
cloud_colors = magnitude_map_colors(flow_map, valid_mask, display_indices, args.magnitude_max)
|
|
elif args.vector_mode == "depth-map":
|
|
depth_map = align_scalar_field_to_map(depth_map, active_data, flip_vertical=flip_for_map)
|
|
cloud_colors = scalar_map_colors(
|
|
depth_map,
|
|
valid_mask,
|
|
display_indices,
|
|
value_min=args.depth_min,
|
|
value_max=args.depth_max,
|
|
)
|
|
elif args.arrow_map_style:
|
|
cloud_colors = arrow_map_reference_colors(len(cloud_points))
|
|
else:
|
|
cloud_colors = align_image_to_map(
|
|
img_bgr,
|
|
active_data,
|
|
valid_mask,
|
|
display_indices,
|
|
flip_vertical=flip_for_map,
|
|
)
|
|
|
|
if args.vector_mode == "shear-normal":
|
|
vshear_map = align_vector_field_to_map(vshear, active_data, flip_vertical=flip_for_map)
|
|
vnormal_map = align_vector_field_to_map(vnormal, active_data, flip_vertical=flip_for_map)
|
|
flow_points, flow_lines, flow_colors = build_shear_normal_lines(
|
|
active_data["xyz_map"],
|
|
valid_mask,
|
|
vshear_map,
|
|
vnormal_map,
|
|
surface_normals,
|
|
arrow_stride=args.arrow_stride,
|
|
flow_scale=args.flow_scale,
|
|
min_magnitude=args.min_flow,
|
|
normal_gain=args.normal_gain,
|
|
arrow_map_style=args.arrow_map_style,
|
|
)
|
|
elif args.vector_mode in ("vraw-magnitude", "depth-map"):
|
|
flow_points = np.empty((0, 3), dtype=np.float64)
|
|
flow_lines = np.empty((0, 2), dtype=np.int32)
|
|
flow_colors = np.empty((0, 3), dtype=np.float64)
|
|
else:
|
|
flow_points, flow_lines, flow_colors = build_flow_lines(
|
|
active_data["xyz_map"],
|
|
valid_mask,
|
|
flow_map,
|
|
arrow_stride=args.arrow_stride,
|
|
flow_scale=args.flow_scale,
|
|
min_magnitude=args.min_flow,
|
|
arrow_map_style=args.arrow_map_style,
|
|
)
|
|
|
|
if vis is None:
|
|
vis, pcd, line_set, o3d = init_viewer(
|
|
cloud_points,
|
|
cloud_colors,
|
|
flow_points,
|
|
flow_lines,
|
|
flow_colors,
|
|
arrow_map_style=args.arrow_map_style,
|
|
)
|
|
print("SDK 3D flow viewer started. Close the Open3D window to stop.")
|
|
else:
|
|
pcd.colors = o3d.utility.Vector3dVector(cloud_colors)
|
|
line_set.points = o3d.utility.Vector3dVector(flow_points.astype(np.float64))
|
|
line_set.lines = o3d.utility.Vector2iVector(flow_lines)
|
|
line_set.colors = o3d.utility.Vector3dVector(flow_colors.astype(np.float64))
|
|
vis.update_geometry(pcd)
|
|
vis.update_geometry(line_set)
|
|
vis.update_renderer()
|
|
|
|
frame_count += 1
|
|
if frame_count % 30 == 0:
|
|
elapsed = max(time.perf_counter() - t0, 1e-6)
|
|
print(f"frames={frame_count}, viewer FPS={frame_count / elapsed:.1f}, arrows={len(flow_lines)}")
|
|
finally:
|
|
sensor.disconnect()
|
|
if vis is not None:
|
|
vis.destroy_window()
|
|
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser(description="Map SDK flow fields to a 3D point cloud.")
|
|
parser.add_argument("--data-dir", default=os.path.join(SCRIPT_DIR, "exported_data"))
|
|
parser.add_argument(
|
|
"--source",
|
|
default=os.path.join(SCRIPT_DIR, "20260602-094916.mp4"),
|
|
help="Video path or live camera ID, for example 0, 1, camera:0, or cam:1.",
|
|
)
|
|
parser.add_argument("--config", default=os.path.join(REPO_ROOT, "config", "dd02-ov.json"))
|
|
parser.add_argument("--display-stride", type=int, default=4)
|
|
parser.add_argument("--arrow-stride", type=int, default=32)
|
|
parser.add_argument("--flow-scale", type=float, default=3.0)
|
|
parser.add_argument("--min-flow", type=float, default=0.5)
|
|
parser.add_argument("--normal-gain", type=float, default=1.0)
|
|
parser.add_argument("--magnitude-max", type=float, default=None,
|
|
help="Upper VRAW magnitude for heat-map color scaling; default uses frame percentile.")
|
|
parser.add_argument("--depth-min", type=float, default=None,
|
|
help="Minimum depth value for heat-map color scaling; default uses frame percentile.")
|
|
parser.add_argument("--depth-max", type=float, default=None,
|
|
help="Maximum depth value for heat-map color scaling; default uses frame percentile.")
|
|
parser.add_argument("--flatten-remap", default=None,
|
|
help="Optional flatten_remap.npz to run optical flow in UV-flattened image space.")
|
|
parser.add_argument(
|
|
"--vector-mode",
|
|
choices=["vraw", "vraw-magnitude", "vraw-magnitude-arrows", "shear-normal", "depth-map"],
|
|
default="vraw",
|
|
)
|
|
parser.add_argument(
|
|
"--arrow-map-style",
|
|
action="store_true",
|
|
help="Hide real texture and use a dark Qt-viewer-like arrow map style.",
|
|
)
|
|
parser.add_argument(
|
|
"--no-flip-vertical",
|
|
action="store_true",
|
|
help="Do not flip captured video vertically before processing.",
|
|
)
|
|
parser.add_argument("--no-mirror", action="store_true")
|
|
parser.add_argument("--cuda", action="store_true")
|
|
parser.add_argument("--verbose", action="store_true")
|
|
return parser.parse_args()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_sdk_3d_flow(parse_args())
|