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# camera_loader.py
# ============================================================
#
# ETHUKU CREATE PANNINOM? (Why does this file exist?)
# ━━━━━━━━━━━━━━━━━━━━━━
# ORE oru camera-vukku vendiya 3 vishayathai edukka:
# 1. Image tensor -> resize + normalize panniyathu
# 2. K (intrinsics) -> camera-oda "lens math" (3x3)
# 3. E (extrinsics) -> camera car-la enga, epadi thirumbi irukku (4x4)
# 6 camera-kum ithe function-a 6 thadava koopiduvom.
#
# MUNADI FILE ODA CONNECTION:
# ━━━━━━━━━━━━━━━━━━━━━━━━━━
# constants.py-la irunthu TARGET_W/H, SCALE, MEAN/STD edukirom.
# sample_loader.py ithai 6 thadava koopidum.
#
# INNER OPERATIONS:
# ━━━━━━━━━━━━━━━━
# nuScenes DB -> photo path + calibration -> cv2 read -> resize ->
# BGR to RGB -> 0..1 -> normalize -> CHW tensor.
# K matrix-ai resize scale-la multiply (MUKIYAM! illaina 3D thappu).
# E = rotation(quaternion->3x3) + translation -> 4x4 matrix.
#
# INPUT / OUTPUT:
# ━━━━━━━━━━━━━━
# Input : nusc object, sample_data_token (str)
# Output: dict {image [3,224,400], intrinsic [3,3], extrinsic [4,4]}
#
# EPADI USE AAGUM:
# ━━━━━━━━━━━━━━━
# Model-uku image mattum pothathu. "Intha pixel real world-la enga?"
# nu kandupidikka K, E rendum kandippa venum. LSS athai use pannum.
#
# ============================================================
import cv2
import numpy as np
import torch
from pyquaternion import Quaternion
from .constants import (
TARGET_W, TARGET_H, SCALE_W, SCALE_H,
IMAGENET_MEAN, IMAGENET_STD,
)
def load_image(image_path: str) -> torch.Tensor:
"""
Oru photo-va padichi, resize + normalize panni tensor-a thara.
Args:
image_path: full path, e.g. "data/nuscenes-mini/samples/CAM_FRONT/xxx.jpg"
Returns:
torch.Tensor shape [3, 224, 400], dtype float32.
Values roughly -2.5 to +2.5 (normalize pannathaala, 0-1 illa).
"""
# cv2 BGR order-la padikkum (Blue,Green,Red) - OpenCV oda pazhaya vazhakkam
img = cv2.imread(image_path) # [900, 1600, 3] uint8
if img is None:
raise FileNotFoundError(f"Image kedaikala: {image_path}")
# 1600x900 -> 400x224. INTER_LINEAR = neighbour pixels average
# (smooth-a suruki, jagged edges varathu)
img = cv2.resize(img, (TARGET_W, TARGET_H), interpolation=cv2.INTER_LINEAR)
# BGR -> RGB. PyTorch/timm ellam RGB expect pannum.
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # [224, 400, 3]
# uint8 (0..255) -> float32 (0..1)
img = img.astype(np.float32) / 255.0
# ImageNet normalize: (x - mean) / std
# Yaen? Pretrained EfficientNet ithe scale-la kathukkichu.
# Example: pixel 0.485 -> (0.485-0.485)/0.229 = 0.0 (average pixel)
img = (img - np.array(IMAGENET_MEAN, dtype=np.float32)) / np.array(IMAGENET_STD, dtype=np.float32)
# HWC -> CHW. PyTorch conv layers channel-first expect pannum.
img = np.transpose(img, (2, 0, 1)) # [3, 224, 400]
return torch.from_numpy(np.ascontiguousarray(img))
def scale_intrinsic(K: np.ndarray) -> np.ndarray:
"""
K matrix-ai resize scale-ku match panna adjust pannurathu.
Yaen ithu MUKIYAM?
Original photo-la car center pixel (800, 450)-la irunthuchu nu vachiko.
Photo-va 4x suruki-tom -> car ippo (200, 112)-la.
Aana K innum "800, 450" nu solli-kittu irundha, LSS car-ai
thappana edathula BEV-la potrum. So K-yum suruka vendiyathu.
Args:
K: [3,3] original intrinsic matrix
[[fx, 0, cx],
[0, fy, cy],
[0, 0, 1]]
Returns:
[3,3] scaled K. fx,cx -> * 0.25 ; fy,cy -> * 0.2489
"""
K = K.copy().astype(np.float32)
K[0, :] *= SCALE_W # row 0 = x-axis: fx, cx
K[1, :] *= SCALE_H # row 1 = y-axis: fy, cy
return K
def load_camera(nusc, sample_data_token: str, data_root: str) -> dict:
"""
Oru camera-oda image + K + E moonum load pannurathu.
Args:
nusc: NuScenes devkit object (database)
sample_data_token: intha oru photo-oda unique id
data_root: dataset folder path
Returns:
dict:
"image" -> [3, 224, 400] float32
"intrinsic" -> [3, 3] float32 (scaled K)
"extrinsic" -> [4, 4] float32 (camera -> ego car transform)
"""
import os
sd = nusc.get("sample_data", sample_data_token)
# --- 1. Image ---
image = load_image(os.path.join(data_root, sd["filename"]))
# --- 2 & 3. Calibration (K and E rendum inga irukku) ---
calib = nusc.get("calibrated_sensor", sd["calibrated_sensor_token"])
K = scale_intrinsic(np.array(calib["camera_intrinsic"]))
# E = camera coordinate -> ego (car) coordinate
# rotation quaternion (4 numbers) -> 3x3 rotation matrix
R = Quaternion(calib["rotation"]).rotation_matrix # [3,3]
t = np.array(calib["translation"], dtype=np.float32) # [3] metres
# 4x4 la pack pannurom:
# [ R t ]
# [ 0 1 ]
# Yaen 4x4? Rotation + translation-a ORE matrix multiply-la
# mudika mudiyum (homogeneous coordinates trick).
E = np.eye(4, dtype=np.float32)
E[:3, :3] = R
E[:3, 3] = t
return {
"image": image,
"intrinsic": torch.from_numpy(K),
"extrinsic": torch.from_numpy(E),
}
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