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Middlebury Dataset Loader
=========================
Scans, groups, loads and parses Middlebury stereo-pair data bundled at
``./data/middlebury/``.
"""
import io
import os
import re
from pathlib import Path
import cv2
import numpy as np
import streamlit as st
DEFAULT_MIDDLEBURY_ROOT = os.path.join(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
"data", "middlebury",
)
BUNDLED_SCENES = {
"artroom": ["artroom1", "artroom2"],
"curule": ["curule1", "curule2", "curule3"],
"skates": ["skates1", "skates2"],
"skiboots": ["skiboots1", "skiboots2", "skiboots3"],
}
# ------------------------------------------------------------------
# Scanning
# ------------------------------------------------------------------
@st.cache_data
def scan_dataset_root(root_path: str = DEFAULT_MIDDLEBURY_ROOT) -> list:
"""Return sorted list of scene names that contain im0.png, im1.png, calib.txt."""
if not os.path.isdir(root_path):
return []
scenes = []
for entry in sorted(os.listdir(root_path)):
scene_dir = os.path.join(root_path, entry)
if not os.path.isdir(scene_dir):
continue
required = ["im0.png", "im1.png", "calib.txt"]
if all(os.path.isfile(os.path.join(scene_dir, f)) for f in required):
scenes.append(entry)
return scenes
@st.cache_data
def get_scene_groups(root_path: str = DEFAULT_MIDDLEBURY_ROOT) -> dict:
"""Group scenes by base name (strip trailing digits)."""
scenes = scan_dataset_root(root_path)
groups = {}
for name in scenes:
base = re.sub(r"\d+$", "", name)
groups.setdefault(base, []).append(name)
return {k: sorted(v) for k, v in sorted(groups.items())}
def get_available_views(scene_path: str) -> list:
"""Return available view variants. Always single entry for this dataset."""
return [{"suffix": "", "label": "Primary (im0/im1)"}]
# ------------------------------------------------------------------
# Loading
# ------------------------------------------------------------------
@st.cache_data
def load_stereo_pair(scene_path: str, view_suffix: str = "") -> dict:
"""Load left + right images, calibration and optional GT disparity."""
left = cv2.imread(os.path.join(scene_path, f"im0{view_suffix}.png"),
cv2.IMREAD_COLOR)
right = cv2.imread(os.path.join(scene_path, f"im1{view_suffix}.png"),
cv2.IMREAD_COLOR)
calib = parse_calib(os.path.join(scene_path, "calib.txt"))
disp0_path = os.path.join(scene_path, "disp0.pfm")
disparity_gt = load_pfm(disp0_path) if os.path.isfile(disp0_path) else None
return {
"left": left,
"right": right,
"calib": calib,
"disparity_gt": disparity_gt,
}
@st.cache_data
def load_single_view(scene_path: str, view_suffix: str = "") -> np.ndarray:
"""Load and return im0{suffix}.png from a scene folder."""
return cv2.imread(os.path.join(scene_path, f"im0{view_suffix}.png"),
cv2.IMREAD_COLOR)
# ------------------------------------------------------------------
# Calibration parser
# ------------------------------------------------------------------
@st.cache_data
def parse_calib(calib_path: str) -> dict:
"""
Parse Middlebury ``calib.txt``.
Returns dict with at least: fx, baseline, doffs, width, height, ndisp.
Also returns raw cam0/cam1 matrices and conf_raw text.
"""
text = Path(calib_path).read_text()
params = {}
for line in text.strip().splitlines():
line = line.strip()
if "=" not in line:
continue
key, val = line.split("=", 1)
key, val = key.strip(), val.strip()
if "[" in val:
nums = list(map(float,
re.findall(r"[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?", val)))
params[key] = np.array(nums).reshape(3, 3) if len(nums) == 9 else nums
else:
try:
params[key] = float(val)
except ValueError:
params[key] = val
cam0 = params.get("cam0")
fx = float(cam0[0, 0]) if isinstance(cam0, np.ndarray) and cam0.shape == (3, 3) else 0.0
params["fx"] = fx
params["conf_raw"] = text
return params
# ------------------------------------------------------------------
# PFM loader
# ------------------------------------------------------------------
@st.cache_data
def load_pfm(filepath: str) -> np.ndarray:
"""Read a PFM (Portable FloatMap) and return a float32 ndarray."""
with open(filepath, "rb") as f:
header = f.readline().decode("ascii").strip()
if header not in ("Pf", "PF"):
raise ValueError(f"Not a valid PFM file (header: {header!r})")
color = header == "PF"
line = f.readline().decode("ascii").strip()
while line.startswith("#"):
line = f.readline().decode("ascii").strip()
w, h = map(int, line.split())
scale = float(f.readline().decode("ascii").strip())
endian = "<" if scale < 0 else ">"
channels = 3 if color else 1
data = np.frombuffer(f.read(), dtype=np.dtype(endian + "f4"))
data = data.reshape((h, w, channels) if color else (h, w))
return np.flipud(data.copy())
@st.cache_data
def read_pfm_bytes(file_bytes: bytes) -> np.ndarray:
"""Parse PFM from raw bytes (uploaded file)."""
buf = io.BytesIO(file_bytes)
header = buf.readline().decode("ascii").strip()
if header not in ("Pf", "PF"):
raise ValueError(f"Not a valid PFM file (header: {header!r})")
color = header == "PF"
line = buf.readline().decode("ascii").strip()
while line.startswith("#"):
line = buf.readline().decode("ascii").strip()
w, h = map(int, line.split())
scale = float(buf.readline().decode("ascii").strip())
endian = "<" if scale < 0 else ">"
channels = 3 if color else 1
data = np.frombuffer(buf.read(), dtype=np.dtype(endian + "f4"))
data = data.reshape((h, w, channels) if color else (h, w))
return np.flipud(data.copy())
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