565 lines
17 KiB
Python
565 lines
17 KiB
Python
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import argparse
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import json
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import re
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from pathlib import Path
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import cv2
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import numpy as np
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# ============================================================
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# RAW10
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# ============================================================
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def unpack_raw10_packed(raw: bytes, width: int, height: int) -> np.ndarray:
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arr = np.frombuffer(raw, dtype=np.uint8)
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pixel_count = width * height
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expected_bytes = (pixel_count // 4) * 5
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if pixel_count % 4 != 0:
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raise RuntimeError(f"width*height precisa ser múltiplo de 4. Recebido: {pixel_count}")
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if arr.size < expected_bytes:
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raise RuntimeError(
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f"RAW10 menor que esperado. bytes={arr.size}, esperado={expected_bytes}, "
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f"width={width}, height={height}"
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)
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arr = arr[:expected_bytes]
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groups = arr.reshape(-1, 5).astype(np.uint16)
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p0 = (groups[:, 0] << 2) | ((groups[:, 4] >> 0) & 0x03)
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p1 = (groups[:, 1] << 2) | ((groups[:, 4] >> 2) & 0x03)
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p2 = (groups[:, 2] << 2) | ((groups[:, 4] >> 4) & 0x03)
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p3 = (groups[:, 3] << 2) | ((groups[:, 4] >> 6) & 0x03)
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out = np.empty(groups.shape[0] * 4, dtype=np.uint16)
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out[0::4] = p0
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out[1::4] = p1
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out[2::4] = p2
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out[3::4] = p3
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return out.reshape(height, width)
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def normalize_to_u8(img: np.ndarray, p_low=1.0, p_high=99.0) -> np.ndarray:
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arr = img.astype(np.float32)
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valid = np.isfinite(arr)
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if np.count_nonzero(valid) < 20:
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return np.zeros(arr.shape[:2], dtype=np.uint8)
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vals = arr[valid]
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lo = np.percentile(vals, p_low)
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hi = np.percentile(vals, p_high)
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out = (arr - lo) / (hi - lo + 1e-6)
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out = np.clip(out, 0.0, 1.0)
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return (out * 255).astype(np.uint8)
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def raw10_bin_to_gray(path: Path, width: int, height: int, use_clahe=True) -> np.ndarray:
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raw = path.read_bytes()
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mono10 = unpack_raw10_packed(raw, width, height)
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gray = normalize_to_u8(mono10)
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if use_clahe:
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clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
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gray = clahe.apply(gray)
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return gray
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# ============================================================
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# Pairing CAM bins
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# ============================================================
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def clean_stem_for_pair(path: Path, cam_key: str):
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s = path.stem
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variants = [
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cam_key,
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cam_key.lower(),
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cam_key.replace("_", ""),
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cam_key.replace("_", "").lower(),
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]
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for v in variants:
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s = s.replace(v, "")
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s = re.sub(r"[_\-\s]+", "_", s).strip("_").lower()
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return s
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def find_cam_bins(root_dir: Path, cam_key: str):
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return sorted([
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p for p in root_dir.rglob("*.bin")
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if cam_key.lower() in p.name.lower()
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])
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def pair_cam_bins(root_dir: Path, left_cam: str, right_cam: str):
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left_bins = find_cam_bins(root_dir, left_cam)
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right_bins = find_cam_bins(root_dir, right_cam)
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right_by_folder_key = {}
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right_by_key = {}
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for rp in right_bins:
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key = clean_stem_for_pair(rp, right_cam)
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right_by_folder_key[(rp.parent, key)] = rp
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right_by_key.setdefault(key, rp)
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pairs = []
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for lp in left_bins:
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key = clean_stem_for_pair(lp, left_cam)
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rp = right_by_folder_key.get((lp.parent, key))
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if rp is None:
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rp = right_by_key.get(key)
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if rp is None:
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same_folder = [r for r in right_bins if r.parent == lp.parent]
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if len(same_folder) == 1:
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rp = same_folder[0]
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if rp is not None:
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pairs.append((lp, rp))
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if pairs:
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return pairs, left_bins, right_bins, "name/key"
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n = min(len(left_bins), len(right_bins))
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return list(zip(left_bins[:n], right_bins[:n])), left_bins, right_bins, "order"
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# ============================================================
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# Calibration loading
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# ============================================================
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def load_stereo_calib(calib_path: Path):
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data = np.load(str(calib_path), allow_pickle=True)
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required = ["map1x", "map1y", "map2x", "map2y", "image_size"]
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for k in required:
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if k not in data:
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raise RuntimeError(f"Calibração sem chave obrigatória: {k}")
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calib = {
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"map1x": data["map1x"],
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"map1y": data["map1y"],
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"map2x": data["map2x"],
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"map2y": data["map2y"],
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"image_size": tuple(data["image_size"].astype(int).tolist()),
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}
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for k in ["left_cam", "right_cam", "rms_left", "rms_right", "rms_stereo", "T"]:
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if k in data:
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calib[k] = data[k]
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return calib
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def rectify_pair(left_gray, right_gray, calib):
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image_w, image_h = calib["image_size"]
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if left_gray.shape[::-1] != (image_w, image_h):
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left_gray = cv2.resize(left_gray, (image_w, image_h), interpolation=cv2.INTER_AREA)
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if right_gray.shape[::-1] != (image_w, image_h):
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right_gray = cv2.resize(right_gray, (image_w, image_h), interpolation=cv2.INTER_AREA)
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left_rect = cv2.remap(
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left_gray,
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calib["map1x"],
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calib["map1y"],
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interpolation=cv2.INTER_LINEAR,
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borderMode=cv2.BORDER_CONSTANT,
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borderValue=0,
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)
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right_rect = cv2.remap(
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right_gray,
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calib["map2x"],
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calib["map2y"],
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interpolation=cv2.INTER_LINEAR,
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borderMode=cv2.BORDER_CONSTANT,
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borderValue=0,
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)
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return left_rect, right_rect
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# ============================================================
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# SGBM
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# ============================================================
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def make_sgbm(args):
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num_disp = max(16, int(round(args.num_disp / 16)) * 16)
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block_size = max(3, int(args.block_size))
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if block_size % 2 == 0:
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block_size += 1
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matcher = cv2.StereoSGBM_create(
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minDisparity=args.min_disp,
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numDisparities=num_disp,
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blockSize=block_size,
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P1=8 * block_size * block_size,
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P2=32 * block_size * block_size,
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disp12MaxDiff=1,
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uniquenessRatio=args.uniqueness,
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speckleWindowSize=args.speckle_window,
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speckleRange=args.speckle_range,
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preFilterCap=63,
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mode=cv2.STEREO_SGBM_MODE_SGBM_3WAY,
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)
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return matcher, num_disp, block_size
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def compute_disparity(left_rect, right_rect, args):
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matcher, num_disp, block_size = make_sgbm(args)
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disp_raw = matcher.compute(left_rect, right_rect).astype(np.float32) / 16.0
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valid = disp_raw > args.min_valid_disp
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disp_vis = disp_raw.copy()
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disp_vis[~valid] = 0.0
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if np.count_nonzero(valid) > 20:
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vals = disp_vis[valid]
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p2 = np.percentile(vals, 2)
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p98 = np.percentile(vals, 98)
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disp_norm = (disp_vis - p2) / (p98 - p2 + 1e-6)
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disp_norm = np.clip(disp_norm, 0.0, 1.0)
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else:
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disp_norm = np.zeros_like(disp_vis, dtype=np.float32)
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disp_color = cv2.applyColorMap((disp_norm * 255).astype(np.uint8), cv2.COLORMAP_TURBO)
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valid_mask = np.zeros_like(disp_color)
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valid_mask[valid] = (255, 255, 255)
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stats = {
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"num_disp": num_disp,
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"block_size": block_size,
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"valid_pct": float(np.mean(valid) * 100.0),
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"disp_p05": float(np.percentile(disp_vis[valid], 5)) if np.count_nonzero(valid) > 20 else 0.0,
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"disp_p50": float(np.percentile(disp_vis[valid], 50)) if np.count_nonzero(valid) > 20 else 0.0,
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"disp_p95": float(np.percentile(disp_vis[valid], 95)) if np.count_nonzero(valid) > 20 else 0.0,
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}
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return disp_raw, disp_color, valid_mask, valid, stats
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# ============================================================
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# Visualization
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# ============================================================
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def draw_epipolar_lines(img_bgr, step=40):
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out = img_bgr.copy()
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h, w = out.shape[:2]
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for y in range(0, h, step):
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color = (0, 255, 255) if (y // step) % 2 == 0 else (255, 255, 0)
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cv2.line(out, (0, y), (w, y), color, 1, cv2.LINE_AA)
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return out
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def resize_to_height(img, target_h):
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h, w = img.shape[:2]
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if h == target_h:
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return img
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scale = target_h / h
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new_w = max(1, int(w * scale))
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return cv2.resize(img, (new_w, target_h), interpolation=cv2.INTER_AREA)
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def draw_header(canvas, lines):
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header_h = 24 + 24 * len(lines)
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cv2.rectangle(canvas, (0, 0), (canvas.shape[1], header_h), (0, 0, 0), -1)
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y = 24
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for line in lines:
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cv2.putText(
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canvas,
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line,
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(12, y),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.55,
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(255, 255, 255),
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1,
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cv2.LINE_AA,
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)
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y += 24
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return canvas
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def make_overlay(left_bgr, disp_color, alpha=0.45):
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disp_resized = cv2.resize(
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disp_color,
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(left_bgr.shape[1], left_bgr.shape[0]),
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interpolation=cv2.INTER_AREA,
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)
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return cv2.addWeighted(left_bgr, 1.0 - alpha, disp_resized, alpha, 0)
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def compose_view(left_rect, right_rect, disp_color, valid_mask, pair, idx, total, mode, stats, args):
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left_bgr = cv2.cvtColor(left_rect, cv2.COLOR_GRAY2BGR)
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right_bgr = cv2.cvtColor(right_rect, cv2.COLOR_GRAY2BGR)
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if args.lines:
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left_bgr = draw_epipolar_lines(left_bgr, step=args.line_step)
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right_bgr = draw_epipolar_lines(right_bgr, step=args.line_step)
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if mode == "disp":
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third = disp_color
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mode_name = "disparity"
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elif mode == "mask":
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third = valid_mask
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mode_name = "valid mask"
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elif mode == "overlay":
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third = make_overlay(cv2.cvtColor(left_rect, cv2.COLOR_GRAY2BGR), disp_color)
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mode_name = "overlay"
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else:
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diff = cv2.absdiff(left_rect, right_rect)
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third = cv2.cvtColor(diff, cv2.COLOR_GRAY2BGR)
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mode_name = "rect diff"
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left = resize_to_height(left_bgr, args.view_h)
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right = resize_to_height(right_bgr, args.view_h)
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third = resize_to_height(third, args.view_h)
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h = min(left.shape[0], right.shape[0], third.shape[0])
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left = left[:h]
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right = right[:h]
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third = third[:h]
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canvas = np.hstack([left, right, third])
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lp, rp = pair
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lines = [
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f"{idx + 1}/{total} | L={lp.name} | R={rp.name}",
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f"mode={mode_name} | valid={stats['valid_pct']:.1f}% | disp p05={stats['disp_p05']:.2f} p50={stats['disp_p50']:.2f} p95={stats['disp_p95']:.2f}",
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f"numDisp={stats['num_disp']} | block={stats['block_size']} | uniqueness={args.uniqueness} | lines={args.lines}",
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"N/SPACE prox | A ant | M modo | L linhas | [ ] numDisp | - + block | S salvar | Q sair",
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]
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return draw_header(canvas, lines)
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# ============================================================
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# Main
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# ============================================================
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--root_dir", required=True)
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parser.add_argument("--calib_path", required=True)
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parser.add_argument("--left_cam", default="CAM_C")
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parser.add_argument("--right_cam", default="CAM_B")
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parser.add_argument("--width", type=int, default=1280)
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parser.add_argument("--height", type=int, default=800)
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parser.add_argument("--view_h", type=int, default=480)
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parser.add_argument("--num_disp", type=int, default=128)
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parser.add_argument("--block_size", type=int, default=7)
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parser.add_argument("--min_disp", type=int, default=0)
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parser.add_argument("--min_valid_disp", type=float, default=1.0)
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parser.add_argument("--uniqueness", type=int, default=8)
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parser.add_argument("--speckle_window", type=int, default=80)
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parser.add_argument("--speckle_range", type=int, default=2)
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parser.add_argument("--no_clahe", action="store_true")
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parser.add_argument("--lines", action="store_true")
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parser.add_argument("--line_step", type=int, default=40)
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parser.add_argument("--save_dir", default="stereo_rectified_sgbm_saves")
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args = parser.parse_args()
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root_dir = Path(args.root_dir)
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calib_path = Path(args.calib_path)
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save_dir = Path(args.save_dir)
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save_dir.mkdir(parents=True, exist_ok=True)
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calib = load_stereo_calib(calib_path)
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print(f"[INFO] calib_path: {calib_path}")
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print(f"[INFO] calib image_size: {calib['image_size']}")
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if "rms_left" in calib:
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print(f"[INFO] rms_left: {float(calib['rms_left']):.6f}")
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if "rms_right" in calib:
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print(f"[INFO] rms_right: {float(calib['rms_right']):.6f}")
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if "rms_stereo" in calib:
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print(f"[INFO] rms_stereo: {float(calib['rms_stereo']):.6f}")
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if "T" in calib:
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print(f"[INFO] T: {np.array(calib['T']).ravel()}")
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pairs, left_bins, right_bins, pair_mode = pair_cam_bins(root_dir, args.left_cam, args.right_cam)
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print(f"[INFO] root_dir: {root_dir}")
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print(f"[INFO] left_cam: {args.left_cam} | arquivos: {len(left_bins)}")
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print(f"[INFO] right_cam: {args.right_cam} | arquivos: {len(right_bins)}")
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print(f"[INFO] pares: {len(pairs)}")
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print(f"[INFO] pareamento: {pair_mode}")
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if not pairs:
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raise RuntimeError("Nenhum par encontrado.")
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idx = 0
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mode = "disp"
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cached_key = None
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cached_data = None
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cv2.namedWindow("Stereo RAW10 Rectified SGBM Viewer", cv2.WINDOW_NORMAL)
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while True:
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pair = pairs[idx]
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lp, rp = pair
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key_cache = (
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str(lp),
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str(rp),
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args.num_disp,
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args.block_size,
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args.uniqueness,
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args.speckle_window,
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args.speckle_range,
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args.min_disp,
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args.min_valid_disp,
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args.no_clahe,
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args.width,
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args.height,
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str(args.calib_path),
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)
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if key_cache != cached_key:
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print(f"[RUN] {idx + 1}/{len(pairs)} - L={lp.name} | R={rp.name}")
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try:
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left_gray = raw10_bin_to_gray(
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lp,
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width=args.width,
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height=args.height,
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use_clahe=not args.no_clahe,
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)
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right_gray = raw10_bin_to_gray(
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rp,
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width=args.width,
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height=args.height,
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use_clahe=not args.no_clahe,
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)
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left_rect, right_rect = rectify_pair(left_gray, right_gray, calib)
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_, disp_color, valid_mask, valid, stats = compute_disparity(left_rect, right_rect, args)
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cached_data = (left_rect, right_rect, disp_color, valid_mask, stats)
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cached_key = key_cache
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except Exception as e:
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print(f"[ERRO] Falha processando par:")
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print(f" L={lp}")
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print(f" R={rp}")
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print(f" erro={e}")
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idx = min(idx + 1, len(pairs) - 1)
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cached_key = None
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cached_data = None
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continue
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else:
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left_rect, right_rect, disp_color, valid_mask, stats = cached_data
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view = compose_view(
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left_rect=left_rect,
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right_rect=right_rect,
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disp_color=disp_color,
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valid_mask=valid_mask,
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pair=pair,
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idx=idx,
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total=len(pairs),
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mode=mode,
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stats=stats,
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args=args,
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)
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cv2.imshow("Stereo RAW10 Rectified SGBM Viewer", view)
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key = cv2.waitKey(0) & 0xFF
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if key in [27, ord("q"), ord("Q")]:
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break
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elif key in [ord("n"), ord("N"), 32]:
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idx = min(idx + 1, len(pairs) - 1)
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cached_key = None
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elif key in [ord("a"), ord("A")]:
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idx = max(idx - 1, 0)
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cached_key = None
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elif key in [ord("m"), ord("M")]:
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if mode == "disp":
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mode = "mask"
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elif mode == "mask":
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mode = "overlay"
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elif mode == "overlay":
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mode = "diff"
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else:
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mode = "disp"
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elif key in [ord("l"), ord("L")]:
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args.lines = not args.lines
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print(f"[PARAM] lines={args.lines}")
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elif key == ord("["):
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args.num_disp = max(16, args.num_disp - 16)
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cached_key = None
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print(f"[PARAM] num_disp={args.num_disp}")
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elif key == ord("]"):
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args.num_disp = min(512, args.num_disp + 16)
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cached_key = None
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print(f"[PARAM] num_disp={args.num_disp}")
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elif key in [ord("-"), ord("_")]:
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args.block_size = max(3, args.block_size - 2)
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if args.block_size % 2 == 0:
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args.block_size -= 1
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cached_key = None
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print(f"[PARAM] block_size={args.block_size}")
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elif key in [ord("+"), ord("=")]:
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args.block_size = min(31, args.block_size + 2)
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if args.block_size % 2 == 0:
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args.block_size += 1
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cached_key = None
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print(f"[PARAM] block_size={args.block_size}")
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elif key in [ord("s"), ord("S")]:
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out_path = save_dir / f"stereo_rectified_{idx:04d}_{mode}.png"
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cv2.imwrite(str(out_path), view)
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print(f"[SAVE] {out_path}")
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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main()
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