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0000000000000000000000000000000000000000..9524b440fa1e122813dc973a9474a2c097375325 --- /dev/null +++ b/calibration/calibration_prev_bev.tar.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b35488d037e8c070dde88296cda9cd6c54f70e8d885930cea2ccb128a430733 +size 75938882 diff --git a/inference_axmodel.py b/inference_axmodel.py new file mode 100644 index 0000000000000000000000000000000000000000..f95de4c31d4913383441a96c0fa8287983152c79 --- /dev/null +++ b/inference_axmodel.py @@ -0,0 +1,1002 @@ +#!/usr/bin/env python3 +import argparse +import json +import os +import os.path as osp +import cv2 +import numpy as np +import axengine as axe +from collections import defaultdict +from tqdm import tqdm + + +def parse_args(): + parser = argparse.ArgumentParser(description='BEVFormer AXEngine Inference from Extracted Data') + parser.add_argument('model', help='AXModel path') + parser.add_argument('config_json', help='JSON config file path') + parser.add_argument('data_dir', help='extracted data directory (extracted_data)') + parser.add_argument('--output-dir', default='./inference_results_extracted', help='output directory') + parser.add_argument('--score-thr', type=float, default=0.1, help='score threshold') + parser.add_argument('--fps', type=int, default=3, help='video fps') + parser.add_argument('--start-scene', type=int, default=0, help='start scene index') + parser.add_argument('--end-scene', type=int, default=None, help='end scene index (None for all)') + return parser.parse_args() + + +def load_axmodel(axmodel_path): + """Load AXModel""" + session = axe.InferenceSession(axmodel_path) + return session + + +def load_config_from_json(config_path): + """Load configuration from JSON file""" + with open(config_path, 'r') as f: + config = json.load(f) + return config + + +def preprocess_image(img_path, img_norm_cfg, target_size=(480, 800)): + """Preprocess image: load, resize, normalize + + Args: + img_path: path to image file + img_norm_cfg: normalization config with 'mean', 'std', 'to_rgb' + target_size: (H, W) target size + + Returns: + img: (C, H, W) normalized numpy array, float32 + """ + # Load image + img = cv2.imread(img_path) + if img is None: + raise ValueError(f"Cannot load image: {img_path}") + + # Convert BGR to RGB if needed + if img_norm_cfg.get('to_rgb', True): + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + + # Resize if needed + if img.shape[:2] != target_size: + img = cv2.resize(img, (target_size[1], target_size[0])) # (W, H) + + # Convert to float and normalize + img = img.astype(np.float32) + mean = np.array(img_norm_cfg.get('mean', [123.675, 116.28, 103.53]), dtype=np.float32) + std = np.array(img_norm_cfg.get('std', [58.395, 57.12, 57.375]), dtype=np.float32) + + img = (img - mean) / std + img = img.transpose(2, 0, 1) # (H, W, C) -> (C, H, W) + + return img + + +def load_data(data_dir, scene_name, frame_idx): + """Load data + + Args: + data_dir: data directory path + scene_name: scene name (scene token) + frame_idx: frame index (sample index) + + Returns: + img: (1, N, C, H, W) numpy array + lidar2img: (1, N, 4, 4) numpy array + can_bus: (1, 18) numpy array + meta: dict with metadata + """ + scene_dir = osp.join(data_dir, scene_name) + + # Load meta + meta_path = osp.join(scene_dir, f'meta_{frame_idx:06d}.json') + with open(meta_path, 'r') as f: + meta = json.load(f) + + # Get normalization config + img_norm_cfg = meta.get('img_norm_cfg', { + 'mean': [123.675, 116.28, 103.53], + 'std': [58.395, 57.12, 57.375], + 'to_rgb': True + }) + + # Get image shape + img_shape = meta.get('img_shape', [[480, 800, 3]] * 6) + target_size = (img_shape[0][0], img_shape[0][1]) # (H, W) + + # Load images for all cameras + num_cams = meta.get('num_cams', 6) + imgs = [] + for cam_idx in range(num_cams): + img_path = osp.join(scene_dir, f'cam_{cam_idx:02d}_{frame_idx:06d}.png') + img = preprocess_image(img_path, img_norm_cfg, target_size) + imgs.append(img) + + # Stack images: (N, C, H, W) -> (1, N, C, H, W) + img = np.stack(imgs, axis=0) # (N, C, H, W) + img = img[np.newaxis, ...] # (1, N, C, H, W) + + # Load lidar2img: (N, 4, 4) -> (1, N, 4, 4) + lidar2img = np.array(meta['lidar2img'], dtype=np.float32) # (N, 4, 4) + lidar2img = lidar2img[np.newaxis, ...] # (1, N, 4, 4) + + # Load can_bus: (18,) -> (1, 18) + can_bus = np.array(meta['can_bus'], dtype=np.float32) # (18,) + can_bus = can_bus[np.newaxis, ...] # (1, 18) + + return img, lidar2img, can_bus, meta + +CLASS_COLORS = { + 0: (0, 255, 0), 1: (255, 255, 0), 2: (0, 0, 255), 3: (0, 165, 255), + 4: (255, 0, 255), 5: (0, 255, 255), 6: (128, 0, 128), 7: (255, 165, 0), + 8: (0, 0, 255), 9: (128, 128, 128), +} + + +def denormalize_bbox_np(normalized_bboxes, pc_range): + """Denormalize bbox using numpy only""" + # rotation + rot_sine = normalized_bboxes[..., 6:7] + rot_cosine = normalized_bboxes[..., 7:8] + + rot = np.arctan2(rot_sine, rot_cosine) + + # center in the bev + cx = normalized_bboxes[..., 0:1] + cy = normalized_bboxes[..., 1:2] + cz = normalized_bboxes[..., 4:5] + + # size + w = normalized_bboxes[..., 2:3] + l = normalized_bboxes[..., 3:4] + h = normalized_bboxes[..., 5:6] + + w = np.exp(w) + l = np.exp(l) + h = np.exp(h) + + if normalized_bboxes.shape[-1] > 8: + # velocity + vx = normalized_bboxes[:, 8:9] + vy = normalized_bboxes[:, 9:10] + denormalized_bboxes = np.concatenate([cx, cy, cz, w, l, h, rot, vx, vy], axis=-1) + else: + denormalized_bboxes = np.concatenate([cx, cy, cz, w, l, h, rot], axis=-1) + return denormalized_bboxes + +def decode_bboxes_custom_np(all_cls_scores, all_bbox_preds, pc_range, post_center_range, max_num=100, score_threshold=None, num_classes=10): + """Custom bbox decode function""" + # Use output from the last decoder layer + all_cls_scores = all_cls_scores[-1] # (bs, num_query, num_classes) + all_bbox_preds = all_bbox_preds[-1] # (bs, num_query, 10) + + batch_size = all_cls_scores.shape[0] + predictions_list = [] + + for i in range(batch_size): + cls_scores = all_cls_scores[i] # (num_query, num_classes) + bbox_preds = all_bbox_preds[i] # (num_query, 10) + + # Apply sigmoid + cls_scores = 1.0 / (1.0 + np.exp(-cls_scores)) + + # TopK selection + cls_scores_flat = cls_scores.reshape(-1) + topk_indices = np.argsort(cls_scores_flat)[::-1][:max_num] + scores = cls_scores_flat[topk_indices] + labels = topk_indices % num_classes + bbox_index = topk_indices // num_classes + bbox_preds = bbox_preds[bbox_index] + + # Denormalize bbox + final_box_preds = denormalize_bbox_np(bbox_preds, pc_range) # (max_num, 9) + final_scores = scores + final_preds = labels + + # Apply score threshold + if score_threshold is not None: + thresh_mask = final_scores > score_threshold + tmp_score = score_threshold + while thresh_mask.sum() == 0: + tmp_score *= 0.9 + if tmp_score < 0.01: + thresh_mask = np.ones(len(final_scores), dtype=bool) + break + thresh_mask = final_scores >= tmp_score + else: + thresh_mask = np.ones(len(final_scores), dtype=bool) + + # Apply post processing range filtering + if post_center_range is not None: + post_center_range_arr = np.array(post_center_range) + mask = (final_box_preds[..., :3] >= post_center_range_arr[:3]).all(1) + mask &= (final_box_preds[..., :3] <= post_center_range_arr[3:]).all(1) + mask &= thresh_mask + + boxes3d = final_box_preds[mask] + scores = final_scores[mask] + labels = final_preds[mask] + else: + boxes3d = final_box_preds[thresh_mask] + scores = final_scores[thresh_mask] + labels = final_preds[thresh_mask] + + predictions_list.append({ + 'bboxes': boxes3d, + 'scores': scores, + 'labels': labels + }) + + return predictions_list + + +def get_bboxes_custom_np(preds_dicts, pc_range, post_center_range, max_num=100, score_threshold=None, num_classes=10): + """Custom get_bboxes function""" + # Decode bounding boxes + preds_list = decode_bboxes_custom_np( + preds_dicts['all_cls_scores'], + preds_dicts['all_bbox_preds'], + pc_range, + post_center_range, + max_num, + score_threshold, + num_classes + ) + + num_samples = len(preds_list) + ret_list = [] + + for i in range(num_samples): + preds = preds_list[i] + bboxes = preds['bboxes'] + + if len(bboxes) == 0: + ret_list.append(( + np.zeros((0, 9), dtype=np.float32), + np.zeros((0,), dtype=np.float32), + np.zeros((0,), dtype=np.int64) + )) + continue + + # Adjust z coordinate: convert center z to bottom center z + bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 5] * 0.5 + + # Shrink box dimensions: multiply w, l, h by 0.9 to fix oversized boxes + bboxes[:, 3:6] = bboxes[:, 3:6] * 0.9 + + scores = preds['scores'] + labels = preds['labels'] + + ret_list.append((bboxes, scores, labels)) + + return ret_list + + +def format_bbox_result_np(bboxes, scores, labels): + return { + 'boxes_3d': bboxes, + 'scores_3d': scores, + 'labels_3d': labels + } + + +def rotation_3d_in_axis_np(points, angles, axis=2): + """Rotate points by angles according to axis""" + rot_sin = np.sin(angles) + rot_cos = np.cos(angles) + ones = np.ones_like(rot_cos) + zeros = np.zeros_like(rot_cos) + + if axis == 2 or axis == -1: + # Rotate around z-axis + # Build rotation matrix: (N, 3, 3) + N = len(angles) + rot_mat = np.zeros((N, 3, 3), dtype=points.dtype) + rot_mat[:, 0, 0] = rot_cos + rot_mat[:, 0, 1] = -rot_sin + rot_mat[:, 0, 2] = zeros + rot_mat[:, 1, 0] = rot_sin + rot_mat[:, 1, 1] = rot_cos + rot_mat[:, 1, 2] = zeros + rot_mat[:, 2, 0] = zeros + rot_mat[:, 2, 1] = zeros + rot_mat[:, 2, 2] = ones + + # Apply rotation: (N, M, 3) @ (N, 3, 3) -> (N, M, 3) + return np.einsum('aij,ajk->aik', points, rot_mat) + else: + raise ValueError(f'Only axis=2 (z-axis) is supported for LiDAR boxes') + + +def compute_bbox_corners_np(bboxes): + """Compute 8 corners of 3D bbox""" + if len(bboxes) == 0: + return np.zeros((0, 8, 3), dtype=np.float32) + + dtype = bboxes.dtype + + # Extract bbox parameters + centers = bboxes[:, :3] # (N, 3) [x, y, z] - the bottom center + w = bboxes[:, 3:4] # width (y direction) + l = bboxes[:, 4:5] # length (x direction) + h = bboxes[:, 5:6] # height (z direction) + dims = np.concatenate([l, w, h], axis=1) # (N, 3) [x_size, y_size, z_size] = [l, w, h] + yaws = bboxes[:, 6] # (N,) yaw angle + + # Key fix: offset yaw by -80 degrees + yaws = yaws - (np.pi / 2.0 - np.pi / 18.0) + + # Generate corners + corners_norm = np.stack(np.unravel_index(np.arange(8), [2] * 3), axis=1).astype(dtype) + + # Rearrange to [0, 1, 3, 2, 4, 5, 7, 6] + corners_norm = corners_norm[[0, 1, 3, 2, 4, 5, 7, 6]] + + # Use relative origin [0.5, 0.5, 0] (bottom center) + corners_norm = corners_norm - np.array([0.5, 0.5, 0], dtype=dtype) + + # Scale corners: dims is [x_size, y_size, z_size] + corners = dims[:, np.newaxis, :] * corners_norm[np.newaxis, :, :] # (N, 8, 3) + + # Rotate around z-axis + corners = rotation_3d_in_axis_np(corners, yaws, axis=2) + + # Translate to center point + corners += centers[:, np.newaxis, :] + + return corners + + +def draw_bbox3d_on_img_custom_np(bboxes, raw_img, lidar2img_rt, color=(0, 255, 0), thickness=2): + """Custom 3D bbox drawing""" + img = raw_img.copy() + + if len(bboxes) == 0: + return img + + if not isinstance(bboxes, np.ndarray): + bboxes = np.array(bboxes) + if not isinstance(lidar2img_rt, np.ndarray): + lidar2img_rt = np.array(lidar2img_rt) + + lidar2img_rt = lidar2img_rt.reshape(4, 4) + + # Compute corners + corners_3d = compute_bbox_corners_np(bboxes) # (N, 8, 3) + + num_bbox = corners_3d.shape[0] + + # Project to 2D + corners_3d_flat = corners_3d.reshape(-1, 3) # (N*8, 3) + ones = np.ones((corners_3d_flat.shape[0], 1), dtype=np.float32) + pts_4d = np.concatenate([corners_3d_flat, ones], axis=-1) # (N*8, 4) + + # Project + pts_2d = pts_4d @ lidar2img_rt.T # (N*8, 4) + + # Perspective division + pts_2d[:, 2] = np.clip(pts_2d[:, 2], a_min=1e-5, a_max=1e5) + pts_2d[:, 0] /= pts_2d[:, 2] + pts_2d[:, 1] /= pts_2d[:, 2] + + # Reshape to (N, 8, 2) + imgfov_pts_2d = pts_2d[:, :2].reshape(num_bbox, 8, 2) + + # Draw 12 lines + line_indices = ((0, 1), (0, 3), (0, 4), (1, 2), (1, 5), (3, 2), (3, 7), + (4, 5), (4, 7), (2, 6), (5, 6), (6, 7)) + + for i in range(num_bbox): + corners = imgfov_pts_2d[i].astype(np.int32) + for start, end in line_indices: + pt1 = (int(corners[start, 0]), int(corners[start, 1])) + pt2 = (int(corners[end, 0]), int(corners[end, 1])) + # Check if points are within image range + h, w = img.shape[:2] + if (0 <= pt1[0] < w and 0 <= pt1[1] < h) or (0 <= pt2[0] < w and 0 <= pt2[1] < h): + cv2.line(img, pt1, pt2, color, thickness, cv2.LINE_AA) + + return img.astype(np.uint8) + + +def post_process_outputs_np(all_cls_scores, all_bbox_preds, config, score_thr=0.1): + bbox_coder = config['model']['bbox_coder'] + pc_range = bbox_coder['pc_range'] + post_center_range = bbox_coder['post_center_range'] + max_num = bbox_coder['max_num'] + score_threshold = bbox_coder.get('score_threshold', None) + num_classes = bbox_coder['num_classes'] + + preds_dicts = { + 'all_cls_scores': all_cls_scores, + 'all_bbox_preds': all_bbox_preds + } + + bbox_list = get_bboxes_custom_np( + preds_dicts, pc_range, post_center_range, + max_num, score_threshold, num_classes + ) + + results = [] + for bboxes, scores, labels in bbox_list: + # Set class score thresholds + class_score_thrs = { + 0: 0.3, # Car + 1: 0.3, # Truck + 2: 0.3, # Construction vehicle + 3: 0.3, # Bus + 4: 0.3, # Trailer + 5: 0.3, # Barrier + 6: 0.3, # Motorcycle + 7: 0.3, # Bicycle + 8: 0.3, # Pedestrian + 9: 0.3, # Traffic cone + } + default_thr = score_thr + + keep_indices = [] + for i in range(len(scores)): + cls_id = int(labels[i]) + thr = class_score_thrs.get(cls_id, default_thr) + if scores[i] > thr: + keep_indices.append(i) + + if len(keep_indices) == 0: + results.append(format_bbox_result_np( + np.zeros((0, 9), dtype=np.float32), + np.zeros((0,), dtype=np.float32), + np.zeros((0,), dtype=np.int64) + )) + continue + + keep_indices = np.array(keep_indices, dtype=np.int64) + bboxes = bboxes[keep_indices] + scores = scores[keep_indices] + labels = labels[keep_indices] + + # Circle NMS + dist_thrs = { + 0: 2.0, 1: 3.0, 2: 2.5, 3: 4.0, 4: 3.0, + 5: 1.0, 6: 1.5, 7: 1.0, 8: 0.5, 9: 0.3, + } + + if len(scores) > 0: + keep_nms = circle_nms_np(bboxes, scores, labels, dist_thrs) + if len(keep_nms) > 0: + bboxes = bboxes[keep_nms] + scores = scores[keep_nms] + labels = labels[keep_nms] + else: + results.append(format_bbox_result_np( + np.zeros((0, 9), dtype=np.float32), + np.zeros((0,), dtype=np.float32), + np.zeros((0,), dtype=np.int64) + )) + continue + + results.append(format_bbox_result_np(bboxes, scores, labels)) + + return results + + +def circle_nms_np(bboxes, scores, labels, dist_thrs): + if len(bboxes) == 0: + return np.array([], dtype=np.int64) + + keep = [] + order = np.argsort(scores)[::-1] + bboxes = bboxes[order] + scores = scores[order] + labels = labels[order] + + pts = bboxes[:, :2] + labels_np = labels + + suppressed = np.zeros(len(bboxes), dtype=bool) + + for i in range(len(bboxes)): + if suppressed[i]: + continue + keep.append(order[i]) + + curr_cls = int(labels_np[i]) + radius = dist_thrs.get(curr_cls, 1.0) + + if i + 1 < len(bboxes): + dists = np.linalg.norm(pts[i+1:] - pts[i], axis=1) + idx_to_suppress = np.where( + (dists < radius) & (labels_np[i+1:] == curr_cls) + )[0] + suppressed[i+1:][idx_to_suppress] = True + + return np.array(keep, dtype=np.int64) + + +def denormalize_img_np(img_array, img_norm_cfg): + """Denormalize image array (C, H, W) to (H, W, C) BGR""" + mean = np.array(img_norm_cfg.get('mean', [123.675, 116.28, 103.53])) + std = np.array(img_norm_cfg.get('std', [58.395, 57.12, 57.375])) + + # (C, H, W) RGB -> (H, W, C) RGB + if img_array.ndim == 3: + img = img_array.transpose(1, 2, 0) + else: + img = img_array + img = (img * std + mean) + img = np.clip(img, 0, 255).astype(np.uint8) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + return img + + +def draw_bev_map(bboxes, labels, scores, pc_range, bev_size=(800, 800), score_thr=0.1): + """Draw BEV (Bird's Eye View) map with detections + + Args: + bboxes: (N, 9) numpy array, format: [x, y, z, w, l, h, yaw, vx, vy] + labels: (N,) numpy array, class labels + scores: (N,) numpy array, detection scores + pc_range: [x_min, y_min, z_min, x_max, y_max, z_max] + bev_size: (width, height) of BEV image + score_thr: score threshold + + Returns: + bev_img: (H, W, 3) numpy array, BEV visualization + """ + bev_w, bev_h = bev_size # BEV image size + bev_img = np.ones((bev_h, bev_w, 3), dtype=np.uint8) * 255 # White background + + # Draw grid + x_min, y_min, z_min, x_max, y_max, z_max = pc_range + x_range = x_max - x_min + y_range = y_max - y_min + + # Draw grid lines + grid_color = (200, 200, 200) # Light gray grid lines + for i in range(-5, 6): + x = x_min + (i + 5) * x_range / 10 + y = y_min + (i + 5) * y_range / 10 + # Vertical lines (y direction in LiDAR -> x direction in image) + img_x = int((y - y_min) / y_range * bev_w) + if 0 <= img_x < bev_w: + cv2.line(bev_img, (img_x, 0), (img_x, bev_h), grid_color, 1) + # Horizontal lines (x direction in LiDAR -> y direction in image, flipped) + img_y = int((x_max - x) / x_range * bev_h) + if 0 <= img_y < bev_h: + cv2.line(bev_img, (0, img_y), (bev_w, img_y), grid_color, 1) + + # Draw center lines (ego vehicle position) - darker on white background + center_x = int((0 - y_min) / y_range * bev_w) + center_y = int((x_max - 0) / x_range * bev_h) + cv2.line(bev_img, (center_x, 0), (center_x, bev_h), (150, 150, 150), 2) + cv2.line(bev_img, (0, center_y), (bev_w, center_y), (150, 150, 150), 2) + + + ego_length_px = 30 # pixels (representing ~4.5m, along x-axis rightward) + ego_width_px = 12 # pixels (representing ~1.8m, along y-axis downward) + + ego_corners_local = np.array([ + [ego_length_px//2, -ego_width_px//2], # front-top (head) + [ego_length_px//2, ego_width_px//2], # front-bottom + [-ego_length_px//2, ego_width_px//2], # back-bottom + [-ego_length_px//2, -ego_width_px//2], # back-top + ], dtype=np.float32) + + + rotation_angle_90 = np.pi / 2 # 90 degrees in radians + cos_rot_90 = np.cos(rotation_angle_90) + sin_rot_90 = np.sin(rotation_angle_90) + rot_mat_90 = np.array([[cos_rot_90, -sin_rot_90], [sin_rot_90, cos_rot_90]]) + + ego_corners_rotated_90 = ego_corners_local @ rot_mat_90.T + + ego_corners_rotated = ego_corners_rotated_90 @ rot_mat_90.T + + # Translate to image coordinates (center position) + ego_corners = [] + for corner in ego_corners_rotated: + corner_img_x = int(center_x + corner[0]) + corner_img_y = int(center_y + corner[1]) + ego_corners.append([corner_img_x, corner_img_y]) + ego_corners = np.array(ego_corners, dtype=np.int32) + + # Draw filled rectangle + cv2.fillPoly(bev_img, [ego_corners], (0, 0, 255)) # Red filled + cv2.polylines(bev_img, [ego_corners], True, (0, 0, 0), 2) # Black outline + + + arrow_length = ego_length_px // 2 + initial_direction = np.array([1.0, 0.0]) + arrow_dir_rotated_90 = initial_direction @ rot_mat_90.T + arrow_dir_rotated = arrow_dir_rotated_90 @ rot_mat_90.T + arrow_end_x = int(center_x + arrow_length * arrow_dir_rotated[0]) + arrow_end_y = int(center_y + arrow_length * arrow_dir_rotated[1]) + cv2.arrowedLine(bev_img, (center_x, center_y), (arrow_end_x, arrow_end_y), + (0, 0, 0), 3, tipLength=0.3) # Black arrow + + if len(bboxes) == 0: + return bev_img + + if score_thr > 0: + mask = scores > score_thr + bboxes = bboxes[mask] + labels = labels[mask] + scores = scores[mask] + + if len(bboxes) == 0: + return bev_img + + default_color = (255, 255, 255) + + + for i in range(len(bboxes)): + box = bboxes[i] + label = int(labels[i]) + score = float(scores[i]) + color = CLASS_COLORS.get(label, default_color) + + x, y, z = box[0], box[1], box[2] # center position + w, l, h = box[3], box[4], box[5] # width, length, height + yaw = box[6] # yaw angle + + yaw = yaw - np.pi / 2.0 # Subtract 90 degrees (counterclockwise) + + # Convert to image coordinates + # Note: In LiDAR coordinate, x is forward, y is left, z is up + # In BEV image (top-down view): + # - x (forward) -> image y (downward, flipped) + # - y (left) -> image x (rightward) + # So: img_x = (y - y_min) / y_range * bev_w + # img_y = (x_max - x) / x_range * bev_h (flip x to get top-down view) + img_x = int((y - y_min) / y_range * bev_w) + img_y = int((x_max - x) / x_range * bev_h) # Flip x for top-down view + + # Skip if outside image + if not (0 <= img_x < bev_w and 0 <= img_y < bev_h): + continue + + # Calculate box dimensions in image space + box_w_px = int(w / x_range * bev_w) + box_l_px = int(l / y_range * bev_h) + + # Draw rotated rectangle + # Calculate 4 corners of the box in LiDAR coordinates + cos_yaw = np.cos(yaw) + sin_yaw = np.sin(yaw) + + # Box corners relative to center (in LiDAR frame: x forward, y left) + corners_local = np.array([ + [l/2, w/2], # front-right + [l/2, -w/2], # front-left + [-l/2, -w/2], # back-left + [-l/2, w/2] # back-right + ]) + + # Rotate corners + rot_mat = np.array([[cos_yaw, -sin_yaw], [sin_yaw, cos_yaw]]) + corners_rotated = corners_local @ rot_mat.T + + # Translate to world coordinates and convert to image space + corners_img = [] + for corner in corners_rotated: + corner_x = x + corner[0] # x in LiDAR (forward) + corner_y = y + corner[1] # y in LiDAR (left) + corner_img_x = int((corner_y - y_min) / y_range * bev_w) # y -> img_x + corner_img_y = int((x_max - corner_x) / x_range * bev_h) # x -> img_y (flipped) + corners_img.append([corner_img_x, corner_img_y]) + + corners_img = np.array(corners_img, dtype=np.int32) + + # Draw filled polygon (semi-transparent on white background) + overlay = bev_img.copy() + cv2.fillPoly(overlay, [corners_img], color) + cv2.addWeighted(overlay, 0.5, bev_img, 0.5, 0, bev_img) + # Draw outline (black on white background) + cv2.polylines(bev_img, [corners_img], True, (0, 0, 0), 2) + + # Draw direction arrow (forward direction) - black on white + # In LiDAR: forward is +x, left is +y + # In BEV image: x -> img_y (flipped), y -> img_x + # So rotation: img_x += sin(yaw) * length, img_y -= cos(yaw) * length + arrow_length = max(box_l_px // 2, 10) + arrow_end_x = int(img_x + arrow_length * sin_yaw) # y component -> img_x + arrow_end_y = int(img_y - arrow_length * cos_yaw) # x component -> img_y (flipped) + cv2.arrowedLine(bev_img, (img_x, img_y), (arrow_end_x, arrow_end_y), + (0, 0, 0), 2, tipLength=0.3) # Black arrow + + # Draw center point + cv2.circle(bev_img, (img_x, img_y), 3, (0, 0, 0), -1) # Black center point + + # Rotate BEV map counterclockwise by 90 degrees (map only, not text) + center = (bev_w // 2, bev_h // 2) + rotation_matrix = cv2.getRotationMatrix2D(center, 90, 1.0) # 90 degrees counterclockwise + bev_img = cv2.warpAffine(bev_img, rotation_matrix, (bev_w, bev_h), borderValue=(255, 255, 255)) + + # Flip horizontally to fix mirror effect + bev_img = cv2.flip(bev_img, 1) # 1 for horizontal flip + + text = 'BEV Map' + font = cv2.FONT_HERSHEY_SIMPLEX + font_scale = 1 + thickness = 2 + (text_width, text_height), baseline = cv2.getTextSize(text, font, font_scale, thickness) + text_x = bev_w - text_width - 10 + text_y = text_height + 10 + cv2.putText(bev_img, text, (text_x, text_y), font, font_scale, (0, 0, 0), thickness) + + return bev_img + + +def visualize_results_np(img, result, lidar2img, img_norm_cfg, class_names, score_thr=0.3, pc_range=None): + num_cams = img.shape[1] if img.ndim == 5 else 1 + raw_imgs = [denormalize_img_np(img[0, cam_idx], img_norm_cfg) for cam_idx in range(num_cams)] + boxes_3d = result.get('boxes_3d') + scores_3d = result.get('scores_3d') + labels_3d = result.get('labels_3d') + vis_imgs = [] + boxes_3d_for_bev = labels_3d_for_bev = scores_3d_for_bev = None + + if boxes_3d is not None and len(boxes_3d) > 0: + mask = (scores_3d > score_thr) if (score_thr > 0 and scores_3d is not None) else np.ones_like(scores_3d, dtype=bool) + if np.any(mask): + boxes_3d = boxes_3d[mask] + scores_3d = scores_3d[mask] + labels_3d = labels_3d[mask] + boxes_3d_for_bev = boxes_3d.copy() + labels_3d_for_bev = labels_3d.copy() + scores_3d_for_bev = scores_3d.copy() + for cam_idx, vis_img in enumerate(raw_imgs): + vis_img = vis_img.copy() + if lidar2img.shape[1] > cam_idx: + cam_lidar2img = lidar2img[0, cam_idx] + for box, label in zip(boxes_3d, labels_3d): + color = CLASS_COLORS.get(int(label), (255, 255, 255)) + try: + vis_img = draw_bbox3d_on_img_custom_np(box[None], vis_img, cam_lidar2img, color=color, thickness=2) + except Exception: + pass + vis_imgs.append(vis_img) + else: + vis_imgs = raw_imgs + + if pc_range is None: + pc_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0] + + if boxes_3d_for_bev is not None and len(boxes_3d_for_bev) > 0: + bev_size = (vis_imgs[0].shape[0], vis_imgs[0].shape[0]) if vis_imgs else (800, 800) + bev_img = draw_bev_map(boxes_3d_for_bev, labels_3d_for_bev, scores_3d_for_bev, pc_range, bev_size=bev_size, score_thr=score_thr) + else: + bev_size = (vis_imgs[0].shape[0], vis_imgs[0].shape[0]) if vis_imgs else (800, 800) + bev_img = np.full((bev_size[1], bev_size[0], 3), 255, np.uint8) + cv2.putText(bev_img, 'BEV Map (No Detections)', (10, bev_size[1]//2), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2) + + if len(vis_imgs) == 6: + target_height = max(img.shape[0] for img in vis_imgs) + resized_imgs = [img if img.shape[0] == target_height else cv2.resize(img, (int(img.shape[1] * target_height / img.shape[0]), target_height)) for img in vis_imgs] + + reordered_imgs = [ + resized_imgs[2], resized_imgs[0], resized_imgs[1], + cv2.flip(resized_imgs[4], 1), cv2.flip(resized_imgs[3], 1), cv2.flip(resized_imgs[5], 1) + ] + top_row = np.hstack(reordered_imgs[:3]) + bottom_row = np.hstack(reordered_imgs[3:]) + left_side = np.vstack([top_row, bottom_row]) + bev_img = cv2.resize(bev_img, (int(bev_img.shape[1] * left_side.shape[0] / bev_img.shape[0]), left_side.shape[0])) + vis_img = np.hstack([left_side, bev_img]) + elif len(vis_imgs) > 1: + target_height = max(img.shape[0] for img in vis_imgs) + resized_imgs = [img if img.shape[0] == target_height else cv2.resize(img, (int(img.shape[1] * target_height / img.shape[0]), target_height)) for img in vis_imgs] + if bev_img.shape[0] != target_height: + bev_img = cv2.resize(bev_img, (int(bev_img.shape[1] * target_height / bev_img.shape[0]), target_height)) + vis_img = np.hstack([np.hstack(resized_imgs), bev_img]) + else: + cam_img = vis_imgs[0] if vis_imgs else bev_img + if bev_img.shape[0] != cam_img.shape[0]: + bev_img = cv2.resize(bev_img, (int(bev_img.shape[1] * cam_img.shape[0] / bev_img.shape[0]), cam_img.shape[0])) + vis_img = np.hstack([cam_img, bev_img]) if vis_imgs else bev_img + + return vis_img + + +def create_video_from_images(image_dir, output_video_path, fps=3): + import subprocess + + image_files = sorted([f for f in os.listdir(image_dir) if f.endswith(('.png', '.jpg', '.jpeg'))]) + if len(image_files) == 0: + return + + first_img = cv2.imread(osp.join(image_dir, image_files[0])) + if first_img is None: + return + + height, width = first_img.shape[:2] + + max_width, max_height = 1920, 1080 + if width > max_width or height > max_height: + scale = min(max_width / width, max_height / height) + width, height = int(width * scale), int(height * scale) + + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + video_writer = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height)) + if not video_writer.isOpened(): + fourcc = cv2.VideoWriter_fourcc(*'XVID') + video_writer = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height)) + + for img_file in tqdm(image_files, desc=f"Creating video: {osp.basename(output_video_path)}"): + img_path = osp.join(image_dir, img_file) + img = cv2.imread(img_path) + if img is not None: + if img.shape[:2] != (height, width): + img = cv2.resize(img, (width, height)) + video_writer.write(img) + + video_writer.release() + +def main(): + args = parse_args() + + # Load configuration from JSON + config = load_config_from_json(args.config_json) + + # Create output directory + os.makedirs(args.output_dir, exist_ok=True) + + # Load AXModel + ax_session = load_axmodel(args.axmodel) + + # Get model parameters from config + transformer_cfg = config['model']['transformer'] + bev_h = transformer_cfg['bev_h'] + bev_w = transformer_cfg['bev_w'] + embed_dims = transformer_cfg['embed_dims'] + + # Load scene index + scene_index_path = osp.join(args.data_dir, 'scene_index.json') + with open(scene_index_path, 'r') as f: + scene_index_data = json.load(f) + + scenes_dict = scene_index_data['scenes'] + scene_names = list(scenes_dict.keys()) + + end_scene = args.end_scene if args.end_scene is not None else len(scene_names) + end_scene = min(end_scene, len(scene_names)) + + prev_frame_info = { + 'prev_bev': None, + 'scene_token': None, + 'prev_pos': np.zeros(3, dtype=np.float32), + 'prev_angle': 0.0, + } + + scene_results = defaultdict(list) + + # Process all scenes + for scene_idx in range(args.start_scene, end_scene): + scene_name = scene_names[scene_idx] + scene_info = scenes_dict[scene_name] + sample_indices = scene_info['samples'] + num_frames = len(sample_indices) + + print(f"Processing scene {scene_idx+1}/{len(scene_names)}: {scene_name} ({num_frames} frames)") + + # Reset prev_bev for new scene + if scene_name != prev_frame_info['scene_token']: + prev_frame_info['prev_bev'] = None + prev_frame_info['prev_pos'] = np.zeros(3, dtype=np.float32) + prev_frame_info['prev_angle'] = 0.0 + + prev_frame_info['scene_token'] = scene_name + + # Process all frames in this scene + for local_idx, frame_idx in enumerate(tqdm(sample_indices, desc=f"Scene {scene_name}")): + # Load data + img, lidar2img, can_bus, meta = load_data(args.data_dir, scene_name, frame_idx) + + # Process can_bus (compute delta) + curr_can_bus_np = can_bus[0] # (18,) + + tmp_pos = curr_can_bus_np[:3].copy() + tmp_angle = curr_can_bus_np[-1] + + delta_can_bus_np = curr_can_bus_np.copy() + + if prev_frame_info['prev_bev'] is not None and prev_frame_info['scene_token'] == scene_name: + delta_can_bus_np[:3] -= prev_frame_info['prev_pos'] + delta_can_bus_np[-1] -= prev_frame_info['prev_angle'] + else: + delta_can_bus_np[:3] = 0.0 + delta_can_bus_np[-1] = 0.0 + + prev_frame_info['prev_pos'] = tmp_pos + prev_frame_info['prev_angle'] = tmp_angle + + # Prepare prev_bev + prev_bev_input = next((inp for inp in ax_session.get_inputs() if inp.name == 'prev_bev'), None) + expected_shape = (bev_h * bev_w, 1, embed_dims) + if prev_bev_input is not None: + expected_shape = list(prev_bev_input.shape) + for i, dim in enumerate(expected_shape): + if isinstance(dim, str) or dim < 0: + expected_shape[i] = (bev_h * bev_w, 1, embed_dims)[i] if i < 3 else 1 + expected_shape = tuple(expected_shape) + + if prev_frame_info['prev_bev'] is None: + prev_bev = np.zeros(expected_shape, dtype=np.float32) + else: + prev_bev = prev_frame_info['prev_bev'] + if prev_bev.shape != expected_shape and len(prev_bev.shape) == 3: + prev_bev = prev_bev.reshape(expected_shape) + + # Prepare AXEngine inputs + img_np = img.astype(np.float32) + lidar2img_np = lidar2img.astype(np.float32) + can_bus_np = delta_can_bus_np.reshape(1, -1).astype(np.float32) + + input_names = [inp.name for inp in ax_session.get_inputs()] + ax_inputs = {} + for name in input_names: + if name == 'img': + ax_inputs['img'] = img_np + elif name == 'can_bus': + ax_inputs['can_bus'] = can_bus_np + elif name == 'lidar2img': + ax_inputs['lidar2img'] = lidar2img_np + elif name == 'prev_bev': + ax_inputs['prev_bev'] = prev_bev + + # Run inference + ax_outputs = ax_session.run(None, ax_inputs) + bev_embed, all_cls_scores, all_bbox_preds = ax_outputs + + prev_frame_info['prev_bev'] = bev_embed + + # Post-process + results = post_process_outputs_np( + all_cls_scores, all_bbox_preds, config, args.score_thr + ) + + # Visualize + img_norm_cfg = config['img_norm'] + class_names = config['dataset']['class_names'] + pc_range = config['model']['bbox_coder']['pc_range'] + vis_img = visualize_results_np( + img, results[0], lidar2img, img_norm_cfg, class_names, args.score_thr, pc_range=pc_range + ) + + scene_results[scene_name].append({ + 'frame_idx': local_idx, + 'result': results[0], + 'vis_img': vis_img, + 'meta': meta + }) + + # Save results + for scene_name, frames in tqdm(scene_results.items(), desc="Save scene results"): + scene_dir = osp.join(args.output_dir, scene_name) + os.makedirs(scene_dir, exist_ok=True) + images_dir = osp.join(scene_dir, 'images') + os.makedirs(images_dir, exist_ok=True) + + for local_idx, frame_data in enumerate(frames): + vis_img = frame_data['vis_img'] + + if vis_img is None: + continue + + if not isinstance(vis_img, np.ndarray): + vis_img = np.array(vis_img) + + if vis_img.dtype != np.uint8: + vis_img = (vis_img * 255).astype(np.uint8) if vis_img.max() <= 1.0 else vis_img.astype(np.uint8) + + if len(vis_img.shape) == 3 and vis_img.shape[0] in (1, 3): + vis_img = vis_img.transpose(1, 2, 0) + + if vis_img.shape[0] > 0 and vis_img.shape[1] > 0: + cv2.imwrite(osp.join(images_dir, f'frame_{local_idx:06d}.png'), vis_img) + + create_video_from_images(images_dir, osp.join(scene_dir, f'{scene_name}_result.mp4'), args.fps) + print(f"✓ Scene {scene_name}: {len(frames)} frames, video: {osp.join(scene_dir, f'{scene_name}_result.mp4')}") + + +if __name__ == '__main__': + main() + diff --git a/inference_config.json b/inference_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6bed9893753ecd33ab82cc2111fa69b965ae6e4d --- /dev/null +++ b/inference_config.json @@ -0,0 +1,68 @@ +{ + "model": { + "bev_h": 50, + "bev_w": 50, + "num_query": 900, + "num_classes": 10, + "transformer": { + "embed_dims": 256, + "bev_h": 50, + "bev_w": 50 + }, + "bbox_coder": { + "pc_range": [ + -51.2, + -51.2, + -5.0, + 51.2, + 51.2, + 3.0 + ], + "post_center_range": [ + -61.2, + -61.2, + -10.0, + 61.2, + 61.2, + 10.0 + ], + "max_num": 300, + "num_classes": 10 + } + }, + "dataset": { + "class_names": [ + "car", + "truck", + "construction_vehicle", + "bus", + "trailer", + "barrier", + "motorcycle", + "bicycle", + 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0000000000000000000000000000000000000000..85e1357877d4396be89e8532412c3e7c245db070 --- /dev/null +++ b/inference_data/scene_index.json @@ -0,0 +1,98 @@ +{ + "scenes": { + "fcbccedd61424f1b85dcbf8f897f9754": { + "samples": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39 + ] + }, + "325cef682f064c55a255f2625c533b75": { + "samples": [ + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80 + ] + } + }, + "total_samples": 81, + "extracted_range": [ + 0, + 81 + ] +} \ No newline at end of file diff --git a/inference_onnx.py b/inference_onnx.py new file mode 100644 index 0000000000000000000000000000000000000000..ada00e26bf8c2a21a7e08049b08581fd5ba7912c --- /dev/null +++ b/inference_onnx.py @@ -0,0 +1,1013 @@ +#!/usr/bin/env python3 +import argparse +import json +import os +import os.path as osp +import cv2 +import numpy as np +import onnxruntime as ort +from collections import defaultdict +from tqdm import tqdm + + +def parse_args(): + parser = argparse.ArgumentParser(description='BEVFormer ONNX Inference from Extracted Data') + parser.add_argument('onnx_model', help='ONNX model path') + parser.add_argument('config_json', help='JSON config file path') + parser.add_argument('data_dir', help='extracted data directory (inference_data)') + parser.add_argument('--output-dir', default='./inference_results', help='output directory') + parser.add_argument('--score-thr', type=float, default=0.1, help='score threshold') + parser.add_argument('--device', default='cuda:0', help='device for ONNX inference') + parser.add_argument('--fps', type=int, default=3, help='video fps') + parser.add_argument('--start-scene', type=int, default=0, help='start scene index') + parser.add_argument('--end-scene', type=int, default=None, help='end scene index (None for all)') + return parser.parse_args() + +def load_onnx_model(onnx_path, device='cuda:0'): + """Load ONNX model""" + available_providers = ort.get_available_providers() + + providers = [] + if 'cuda' in device.lower() and 'CUDAExecutionProvider' in available_providers: + providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] + else: + providers = ['CPUExecutionProvider'] + + sess_options = ort.SessionOptions() + sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC + + session = ort.InferenceSession(onnx_path, sess_options=sess_options, providers=providers) + return session + + +def load_config_from_json(config_path): + """Load configuration from JSON file""" + with open(config_path, 'r') as f: + config = json.load(f) + return config + + +def preprocess_image(img_path, img_norm_cfg, target_size=(480, 800)): + """Preprocess image: load, resize, normalize + + Args: + img_path: path to image file + img_norm_cfg: normalization config with 'mean', 'std', 'to_rgb' + target_size: (H, W) target size + + Returns: + img: (C, H, W) normalized numpy array, float32 + """ + # Load image + img = cv2.imread(img_path) + if img is None: + raise ValueError(f"Cannot load image: {img_path}") + + # Convert BGR to RGB if needed + if img_norm_cfg.get('to_rgb', True): + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + + # Resize if needed + if img.shape[:2] != target_size: + img = cv2.resize(img, (target_size[1], target_size[0])) # (W, H) + + # Convert to float and normalize + img = img.astype(np.float32) + mean = np.array(img_norm_cfg.get('mean', [123.675, 116.28, 103.53]), dtype=np.float32) + std = np.array(img_norm_cfg.get('std', [58.395, 57.12, 57.375]), dtype=np.float32) + + img = (img - mean) / std + img = img.transpose(2, 0, 1) # (H, W, C) -> (C, H, W) + + return img + + +def load_data(data_dir, scene_name, frame_idx): + """Load data + + Args: + data_dir: data directory path + scene_name: scene name (scene token) + frame_idx: frame index (sample index) + + Returns: + img: (1, N, C, H, W) numpy array + lidar2img: (1, N, 4, 4) numpy array + can_bus: (1, 18) numpy array + meta: dict with metadata + """ + scene_dir = osp.join(data_dir, scene_name) + + # Load meta + meta_path = osp.join(scene_dir, f'meta_{frame_idx:06d}.json') + with open(meta_path, 'r') as f: + meta = json.load(f) + + # Get normalization config + img_norm_cfg = meta.get('img_norm_cfg', { + 'mean': [123.675, 116.28, 103.53], + 'std': [58.395, 57.12, 57.375], + 'to_rgb': True + }) + + # Get image shape + img_shape = meta.get('img_shape', [[480, 800, 3]] * 6) + target_size = (img_shape[0][0], img_shape[0][1]) # (H, W) + + # Load images for all cameras + num_cams = meta.get('num_cams', 6) + imgs = [] + for cam_idx in range(num_cams): + img_path = osp.join(scene_dir, f'cam_{cam_idx:02d}_{frame_idx:06d}.png') + img = preprocess_image(img_path, img_norm_cfg, target_size) + imgs.append(img) + + # Stack images: (N, C, H, W) -> (1, N, C, H, W) + img = np.stack(imgs, axis=0) # (N, C, H, W) + img = img[np.newaxis, ...] # (1, N, C, H, W) + + # Load lidar2img: (N, 4, 4) -> (1, N, 4, 4) + lidar2img = np.array(meta['lidar2img'], dtype=np.float32) # (N, 4, 4) + lidar2img = lidar2img[np.newaxis, ...] # (1, N, 4, 4) + + # Load can_bus: (18,) -> (1, 18) + can_bus = np.array(meta['can_bus'], dtype=np.float32) # (18,) + can_bus = can_bus[np.newaxis, ...] # (1, 18) + + return img, lidar2img, can_bus, meta + +CLASS_COLORS = { + 0: (0, 255, 0), 1: (255, 255, 0), 2: (0, 0, 255), 3: (0, 165, 255), + 4: (255, 0, 255), 5: (0, 255, 255), 6: (128, 0, 128), 7: (255, 165, 0), + 8: (0, 0, 255), 9: (128, 128, 128), +} + + +def denormalize_bbox_np(normalized_bboxes, pc_range): + """Denormalize bbox using numpy only""" + # rotation + rot_sine = normalized_bboxes[..., 6:7] + rot_cosine = normalized_bboxes[..., 7:8] + + rot = np.arctan2(rot_sine, rot_cosine) + + # center in the bev + cx = normalized_bboxes[..., 0:1] + cy = normalized_bboxes[..., 1:2] + cz = normalized_bboxes[..., 4:5] + + # size + w = normalized_bboxes[..., 2:3] + l = normalized_bboxes[..., 3:4] + h = normalized_bboxes[..., 5:6] + + w = np.exp(w) + l = np.exp(l) + h = np.exp(h) + + if normalized_bboxes.shape[-1] > 8: + # velocity + vx = normalized_bboxes[:, 8:9] + vy = normalized_bboxes[:, 9:10] + denormalized_bboxes = np.concatenate([cx, cy, cz, w, l, h, rot, vx, vy], axis=-1) + else: + denormalized_bboxes = np.concatenate([cx, cy, cz, w, l, h, rot], axis=-1) + return denormalized_bboxes + +def decode_bboxes_custom_np(all_cls_scores, all_bbox_preds, pc_range, post_center_range, max_num=100, score_threshold=None, num_classes=10): + """Custom bbox decode function""" + # Use output from the last decoder layer + all_cls_scores = all_cls_scores[-1] # (bs, num_query, num_classes) + all_bbox_preds = all_bbox_preds[-1] # (bs, num_query, 10) + + batch_size = all_cls_scores.shape[0] + predictions_list = [] + + for i in range(batch_size): + cls_scores = all_cls_scores[i] # (num_query, num_classes) + bbox_preds = all_bbox_preds[i] # (num_query, 10) + + # Apply sigmoid + cls_scores = 1.0 / (1.0 + np.exp(-cls_scores)) + + # TopK selection + cls_scores_flat = cls_scores.reshape(-1) + topk_indices = np.argsort(cls_scores_flat)[::-1][:max_num] + scores = cls_scores_flat[topk_indices] + labels = topk_indices % num_classes + bbox_index = topk_indices // num_classes + bbox_preds = bbox_preds[bbox_index] + + # Denormalize bbox + final_box_preds = denormalize_bbox_np(bbox_preds, pc_range) # (max_num, 9) + final_scores = scores + final_preds = labels + + # Apply score threshold + if score_threshold is not None: + thresh_mask = final_scores > score_threshold + tmp_score = score_threshold + while thresh_mask.sum() == 0: + tmp_score *= 0.9 + if tmp_score < 0.01: + thresh_mask = np.ones(len(final_scores), dtype=bool) + break + thresh_mask = final_scores >= tmp_score + else: + thresh_mask = np.ones(len(final_scores), dtype=bool) + + # Apply post processing range filtering + if post_center_range is not None: + post_center_range_arr = np.array(post_center_range) + mask = (final_box_preds[..., :3] >= post_center_range_arr[:3]).all(1) + mask &= (final_box_preds[..., :3] <= post_center_range_arr[3:]).all(1) + mask &= thresh_mask + + boxes3d = final_box_preds[mask] + scores = final_scores[mask] + labels = final_preds[mask] + else: + boxes3d = final_box_preds[thresh_mask] + scores = final_scores[thresh_mask] + labels = final_preds[thresh_mask] + + predictions_list.append({ + 'bboxes': boxes3d, + 'scores': scores, + 'labels': labels + }) + + return predictions_list + + +def get_bboxes_custom_np(preds_dicts, pc_range, post_center_range, max_num=100, score_threshold=None, num_classes=10): + """Custom get_bboxes function""" + # Decode bounding boxes + preds_list = decode_bboxes_custom_np( + preds_dicts['all_cls_scores'], + preds_dicts['all_bbox_preds'], + pc_range, + post_center_range, + max_num, + score_threshold, + num_classes + ) + + num_samples = len(preds_list) + ret_list = [] + + for i in range(num_samples): + preds = preds_list[i] + bboxes = preds['bboxes'] + + if len(bboxes) == 0: + ret_list.append(( + np.zeros((0, 9), dtype=np.float32), + np.zeros((0,), dtype=np.float32), + np.zeros((0,), dtype=np.int64) + )) + continue + + # Adjust z coordinate: convert center z to bottom center z + bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 5] * 0.5 + + # Shrink box dimensions: multiply w, l, h by 0.9 to fix oversized boxes + bboxes[:, 3:6] = bboxes[:, 3:6] * 0.9 + + scores = preds['scores'] + labels = preds['labels'] + + ret_list.append((bboxes, scores, labels)) + + return ret_list + + +def format_bbox_result_np(bboxes, scores, labels): + return { + 'boxes_3d': bboxes, + 'scores_3d': scores, + 'labels_3d': labels + } + + +def rotation_3d_in_axis_np(points, angles, axis=2): + """Rotate points by angles according to axis""" + rot_sin = np.sin(angles) + rot_cos = np.cos(angles) + ones = np.ones_like(rot_cos) + zeros = np.zeros_like(rot_cos) + + if axis == 2 or axis == -1: + # Rotate around z-axis + # Build rotation matrix: (N, 3, 3) + N = len(angles) + rot_mat = np.zeros((N, 3, 3), dtype=points.dtype) + rot_mat[:, 0, 0] = rot_cos + rot_mat[:, 0, 1] = -rot_sin + rot_mat[:, 0, 2] = zeros + rot_mat[:, 1, 0] = rot_sin + rot_mat[:, 1, 1] = rot_cos + rot_mat[:, 1, 2] = zeros + rot_mat[:, 2, 0] = zeros + rot_mat[:, 2, 1] = zeros + rot_mat[:, 2, 2] = ones + + # Apply rotation: (N, M, 3) @ (N, 3, 3) -> (N, M, 3) + return np.einsum('aij,ajk->aik', points, rot_mat) + else: + raise ValueError(f'Only axis=2 (z-axis) is supported for LiDAR boxes') + + +def compute_bbox_corners_np(bboxes): + """Compute 8 corners of 3D bbox""" + if len(bboxes) == 0: + return np.zeros((0, 8, 3), dtype=np.float32) + + dtype = bboxes.dtype + + # Extract bbox parameters + centers = bboxes[:, :3] # (N, 3) [x, y, z] - the bottom center + w = bboxes[:, 3:4] # width (y direction) + l = bboxes[:, 4:5] # length (x direction) + h = bboxes[:, 5:6] # height (z direction) + dims = np.concatenate([l, w, h], axis=1) # (N, 3) [x_size, y_size, z_size] = [l, w, h] + yaws = bboxes[:, 6] # (N,) yaw angle + + # Key fix: offset yaw by -80 degrees + yaws = yaws - (np.pi / 2.0 - np.pi / 18.0) + + # Generate corners + corners_norm = np.stack(np.unravel_index(np.arange(8), [2] * 3), axis=1).astype(dtype) + + # Rearrange to [0, 1, 3, 2, 4, 5, 7, 6] + corners_norm = corners_norm[[0, 1, 3, 2, 4, 5, 7, 6]] + + # Use relative origin [0.5, 0.5, 0] (bottom center) + corners_norm = corners_norm - np.array([0.5, 0.5, 0], dtype=dtype) + + # Scale corners: dims is [x_size, y_size, z_size] + corners = dims[:, np.newaxis, :] * corners_norm[np.newaxis, :, :] # (N, 8, 3) + + # Rotate around z-axis + corners = rotation_3d_in_axis_np(corners, yaws, axis=2) + + # Translate to center point + corners += centers[:, np.newaxis, :] + + return corners + + +def draw_bbox3d_on_img_custom_np(bboxes, raw_img, lidar2img_rt, color=(0, 255, 0), thickness=2): + """Custom 3D bbox drawing""" + img = raw_img.copy() + + if len(bboxes) == 0: + return img + + if not isinstance(bboxes, np.ndarray): + bboxes = np.array(bboxes) + if not isinstance(lidar2img_rt, np.ndarray): + lidar2img_rt = np.array(lidar2img_rt) + + lidar2img_rt = lidar2img_rt.reshape(4, 4) + + # Compute corners + corners_3d = compute_bbox_corners_np(bboxes) # (N, 8, 3) + + num_bbox = corners_3d.shape[0] + + # Project to 2D + corners_3d_flat = corners_3d.reshape(-1, 3) # (N*8, 3) + ones = np.ones((corners_3d_flat.shape[0], 1), dtype=np.float32) + pts_4d = np.concatenate([corners_3d_flat, ones], axis=-1) # (N*8, 4) + + # Project + pts_2d = pts_4d @ lidar2img_rt.T # (N*8, 4) + + # Perspective division + pts_2d[:, 2] = np.clip(pts_2d[:, 2], a_min=1e-5, a_max=1e5) + pts_2d[:, 0] /= pts_2d[:, 2] + pts_2d[:, 1] /= pts_2d[:, 2] + + # Reshape to (N, 8, 2) + imgfov_pts_2d = pts_2d[:, :2].reshape(num_bbox, 8, 2) + + # Draw 12 lines + line_indices = ((0, 1), (0, 3), (0, 4), (1, 2), (1, 5), (3, 2), (3, 7), + (4, 5), (4, 7), (2, 6), (5, 6), (6, 7)) + + for i in range(num_bbox): + corners = imgfov_pts_2d[i].astype(np.int32) + for start, end in line_indices: + pt1 = (int(corners[start, 0]), int(corners[start, 1])) + pt2 = (int(corners[end, 0]), int(corners[end, 1])) + # Check if points are within image range + h, w = img.shape[:2] + if (0 <= pt1[0] < w and 0 <= pt1[1] < h) or (0 <= pt2[0] < w and 0 <= pt2[1] < h): + cv2.line(img, pt1, pt2, color, thickness, cv2.LINE_AA) + + return img.astype(np.uint8) + + +def post_process_outputs_np(all_cls_scores, all_bbox_preds, config, score_thr=0.1): + bbox_coder = config['model']['bbox_coder'] + pc_range = bbox_coder['pc_range'] + post_center_range = bbox_coder['post_center_range'] + max_num = bbox_coder['max_num'] + score_threshold = bbox_coder.get('score_threshold', None) + num_classes = bbox_coder['num_classes'] + + preds_dicts = { + 'all_cls_scores': all_cls_scores, + 'all_bbox_preds': all_bbox_preds + } + + bbox_list = get_bboxes_custom_np( + preds_dicts, pc_range, post_center_range, + max_num, score_threshold, num_classes + ) + + results = [] + for bboxes, scores, labels in bbox_list: + # Set class score thresholds + class_score_thrs = { + 0: 0.3, # Car + 1: 0.3, # Truck + 2: 0.3, # Construction vehicle + 3: 0.3, # Bus + 4: 0.3, # Trailer + 5: 0.3, # Barrier + 6: 0.3, # Motorcycle + 7: 0.3, # Bicycle + 8: 0.3, # Pedestrian + 9: 0.3, # Traffic cone + } + default_thr = score_thr + + keep_indices = [] + for i in range(len(scores)): + cls_id = int(labels[i]) + thr = class_score_thrs.get(cls_id, default_thr) + if scores[i] > thr: + keep_indices.append(i) + + if len(keep_indices) == 0: + results.append(format_bbox_result_np( + np.zeros((0, 9), dtype=np.float32), + np.zeros((0,), dtype=np.float32), + np.zeros((0,), dtype=np.int64) + )) + continue + + keep_indices = np.array(keep_indices, dtype=np.int64) + bboxes = bboxes[keep_indices] + scores = scores[keep_indices] + labels = labels[keep_indices] + + # Circle NMS + dist_thrs = { + 0: 2.0, 1: 3.0, 2: 2.5, 3: 4.0, 4: 3.0, + 5: 1.0, 6: 1.5, 7: 1.0, 8: 0.5, 9: 0.3, + } + + if len(scores) > 0: + keep_nms = circle_nms_np(bboxes, scores, labels, dist_thrs) + if len(keep_nms) > 0: + bboxes = bboxes[keep_nms] + scores = scores[keep_nms] + labels = labels[keep_nms] + else: + results.append(format_bbox_result_np( + np.zeros((0, 9), dtype=np.float32), + np.zeros((0,), dtype=np.float32), + np.zeros((0,), dtype=np.int64) + )) + continue + + results.append(format_bbox_result_np(bboxes, scores, labels)) + + return results + + +def circle_nms_np(bboxes, scores, labels, dist_thrs): + if len(bboxes) == 0: + return np.array([], dtype=np.int64) + + keep = [] + order = np.argsort(scores)[::-1] + bboxes = bboxes[order] + scores = scores[order] + labels = labels[order] + + pts = bboxes[:, :2] + labels_np = labels + + suppressed = np.zeros(len(bboxes), dtype=bool) + + for i in range(len(bboxes)): + if suppressed[i]: + continue + keep.append(order[i]) + + curr_cls = int(labels_np[i]) + radius = dist_thrs.get(curr_cls, 1.0) + + if i + 1 < len(bboxes): + dists = np.linalg.norm(pts[i+1:] - pts[i], axis=1) + idx_to_suppress = np.where( + (dists < radius) & (labels_np[i+1:] == curr_cls) + )[0] + suppressed[i+1:][idx_to_suppress] = True + + return np.array(keep, dtype=np.int64) + + +def denormalize_img_np(img_array, img_norm_cfg): + """Denormalize image array (C, H, W) to (H, W, C) BGR""" + mean = np.array(img_norm_cfg.get('mean', [123.675, 116.28, 103.53])) + std = np.array(img_norm_cfg.get('std', [58.395, 57.12, 57.375])) + + # (C, H, W) RGB -> (H, W, C) RGB + if img_array.ndim == 3: + img = img_array.transpose(1, 2, 0) + else: + img = img_array + img = (img * std + mean) + img = np.clip(img, 0, 255).astype(np.uint8) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + return img + + +def draw_bev_map(bboxes, labels, scores, pc_range, bev_size=(800, 800), score_thr=0.1): + """Draw BEV (Bird's Eye View) map with detections + + Args: + bboxes: (N, 9) numpy array, format: [x, y, z, w, l, h, yaw, vx, vy] + labels: (N,) numpy array, class labels + scores: (N,) numpy array, detection scores + pc_range: [x_min, y_min, z_min, x_max, y_max, z_max] + bev_size: (width, height) of BEV image + score_thr: score threshold + + Returns: + bev_img: (H, W, 3) numpy array, BEV visualization + """ + bev_w, bev_h = bev_size # BEV image size + bev_img = np.ones((bev_h, bev_w, 3), dtype=np.uint8) * 255 # White background + + # Draw grid + x_min, y_min, z_min, x_max, y_max, z_max = pc_range + x_range = x_max - x_min + y_range = y_max - y_min + + # Draw grid lines + grid_color = (200, 200, 200) # Light gray grid lines + for i in range(-5, 6): + x = x_min + (i + 5) * x_range / 10 + y = y_min + (i + 5) * y_range / 10 + # Vertical lines (y direction in LiDAR -> x direction in image) + img_x = int((y - y_min) / y_range * bev_w) + if 0 <= img_x < bev_w: + cv2.line(bev_img, (img_x, 0), (img_x, bev_h), grid_color, 1) + # Horizontal lines (x direction in LiDAR -> y direction in image, flipped) + img_y = int((x_max - x) / x_range * bev_h) + if 0 <= img_y < bev_h: + cv2.line(bev_img, (0, img_y), (bev_w, img_y), grid_color, 1) + + # Draw center lines (ego vehicle position) - darker on white background + center_x = int((0 - y_min) / y_range * bev_w) + center_y = int((x_max - 0) / x_range * bev_h) + cv2.line(bev_img, (center_x, 0), (center_x, bev_h), (150, 150, 150), 2) + cv2.line(bev_img, (0, center_y), (bev_w, center_y), (150, 150, 150), 2) + + + ego_length_px = 30 # pixels (representing ~4.5m, along x-axis rightward) + ego_width_px = 12 # pixels (representing ~1.8m, along y-axis downward) + + ego_corners_local = np.array([ + [ego_length_px//2, -ego_width_px//2], # front-top (head) + [ego_length_px//2, ego_width_px//2], # front-bottom + [-ego_length_px//2, ego_width_px//2], # back-bottom + [-ego_length_px//2, -ego_width_px//2], # back-top + ], dtype=np.float32) + + + rotation_angle_90 = np.pi / 2 # 90 degrees in radians + cos_rot_90 = np.cos(rotation_angle_90) + sin_rot_90 = np.sin(rotation_angle_90) + rot_mat_90 = np.array([[cos_rot_90, -sin_rot_90], [sin_rot_90, cos_rot_90]]) + + ego_corners_rotated_90 = ego_corners_local @ rot_mat_90.T + + ego_corners_rotated = ego_corners_rotated_90 @ rot_mat_90.T + + # Translate to image coordinates (center position) + ego_corners = [] + for corner in ego_corners_rotated: + corner_img_x = int(center_x + corner[0]) + corner_img_y = int(center_y + corner[1]) + ego_corners.append([corner_img_x, corner_img_y]) + ego_corners = np.array(ego_corners, dtype=np.int32) + + # Draw filled rectangle + cv2.fillPoly(bev_img, [ego_corners], (0, 0, 255)) # Red filled + cv2.polylines(bev_img, [ego_corners], True, (0, 0, 0), 2) # Black outline + + + arrow_length = ego_length_px // 2 + initial_direction = np.array([1.0, 0.0]) + arrow_dir_rotated_90 = initial_direction @ rot_mat_90.T + arrow_dir_rotated = arrow_dir_rotated_90 @ rot_mat_90.T + arrow_end_x = int(center_x + arrow_length * arrow_dir_rotated[0]) + arrow_end_y = int(center_y + arrow_length * arrow_dir_rotated[1]) + cv2.arrowedLine(bev_img, (center_x, center_y), (arrow_end_x, arrow_end_y), + (0, 0, 0), 3, tipLength=0.3) # Black arrow + + if len(bboxes) == 0: + return bev_img + + if score_thr > 0: + mask = scores > score_thr + bboxes = bboxes[mask] + labels = labels[mask] + scores = scores[mask] + + if len(bboxes) == 0: + return bev_img + + default_color = (255, 255, 255) + + + for i in range(len(bboxes)): + box = bboxes[i] + label = int(labels[i]) + score = float(scores[i]) + color = CLASS_COLORS.get(label, default_color) + + x, y, z = box[0], box[1], box[2] # center position + w, l, h = box[3], box[4], box[5] # width, length, height + yaw = box[6] # yaw angle + + yaw = yaw - np.pi / 2.0 # Subtract 90 degrees (counterclockwise) + + # Convert to image coordinates + # Note: In LiDAR coordinate, x is forward, y is left, z is up + # In BEV image (top-down view): + # - x (forward) -> image y (downward, flipped) + # - y (left) -> image x (rightward) + # So: img_x = (y - y_min) / y_range * bev_w + # img_y = (x_max - x) / x_range * bev_h (flip x to get top-down view) + img_x = int((y - y_min) / y_range * bev_w) + img_y = int((x_max - x) / x_range * bev_h) # Flip x for top-down view + + # Skip if outside image + if not (0 <= img_x < bev_w and 0 <= img_y < bev_h): + continue + + # Calculate box dimensions in image space + box_w_px = int(w / x_range * bev_w) + box_l_px = int(l / y_range * bev_h) + + # Draw rotated rectangle + # Calculate 4 corners of the box in LiDAR coordinates + cos_yaw = np.cos(yaw) + sin_yaw = np.sin(yaw) + + # Box corners relative to center (in LiDAR frame: x forward, y left) + corners_local = np.array([ + [l/2, w/2], # front-right + [l/2, -w/2], # front-left + [-l/2, -w/2], # back-left + [-l/2, w/2] # back-right + ]) + + # Rotate corners + rot_mat = np.array([[cos_yaw, -sin_yaw], [sin_yaw, cos_yaw]]) + corners_rotated = corners_local @ rot_mat.T + + # Translate to world coordinates and convert to image space + corners_img = [] + for corner in corners_rotated: + corner_x = x + corner[0] # x in LiDAR (forward) + corner_y = y + corner[1] # y in LiDAR (left) + corner_img_x = int((corner_y - y_min) / y_range * bev_w) # y -> img_x + corner_img_y = int((x_max - corner_x) / x_range * bev_h) # x -> img_y (flipped) + corners_img.append([corner_img_x, corner_img_y]) + + corners_img = np.array(corners_img, dtype=np.int32) + + # Draw filled polygon (semi-transparent on white background) + overlay = bev_img.copy() + cv2.fillPoly(overlay, [corners_img], color) + cv2.addWeighted(overlay, 0.5, bev_img, 0.5, 0, bev_img) + # Draw outline (black on white background) + cv2.polylines(bev_img, [corners_img], True, (0, 0, 0), 2) + + # Draw direction arrow (forward direction) - black on white + # In LiDAR: forward is +x, left is +y + # In BEV image: x -> img_y (flipped), y -> img_x + # So rotation: img_x += sin(yaw) * length, img_y -= cos(yaw) * length + arrow_length = max(box_l_px // 2, 10) + arrow_end_x = int(img_x + arrow_length * sin_yaw) # y component -> img_x + arrow_end_y = int(img_y - arrow_length * cos_yaw) # x component -> img_y (flipped) + cv2.arrowedLine(bev_img, (img_x, img_y), (arrow_end_x, arrow_end_y), + (0, 0, 0), 2, tipLength=0.3) # Black arrow + + # Draw center point + cv2.circle(bev_img, (img_x, img_y), 3, (0, 0, 0), -1) # Black center point + + # Rotate BEV map counterclockwise by 90 degrees (map only, not text) + center = (bev_w // 2, bev_h // 2) + rotation_matrix = cv2.getRotationMatrix2D(center, 90, 1.0) # 90 degrees counterclockwise + bev_img = cv2.warpAffine(bev_img, rotation_matrix, (bev_w, bev_h), borderValue=(255, 255, 255)) + + # Flip horizontally to fix mirror effect + bev_img = cv2.flip(bev_img, 1) # 1 for horizontal flip + + text = 'BEV Map' + font = cv2.FONT_HERSHEY_SIMPLEX + font_scale = 1 + thickness = 2 + (text_width, text_height), baseline = cv2.getTextSize(text, font, font_scale, thickness) + text_x = bev_w - text_width - 10 + text_y = text_height + 10 + cv2.putText(bev_img, text, (text_x, text_y), font, font_scale, (0, 0, 0), thickness) + + return bev_img + + +def visualize_results_np(img, result, lidar2img, img_norm_cfg, class_names, score_thr=0.3, pc_range=None): + num_cams = img.shape[1] if img.ndim == 5 else 1 + raw_imgs = [denormalize_img_np(img[0, cam_idx], img_norm_cfg) for cam_idx in range(num_cams)] + boxes_3d = result.get('boxes_3d') + scores_3d = result.get('scores_3d') + labels_3d = result.get('labels_3d') + vis_imgs = [] + boxes_3d_for_bev = labels_3d_for_bev = scores_3d_for_bev = None + + if boxes_3d is not None and len(boxes_3d) > 0: + mask = (scores_3d > score_thr) if (score_thr > 0 and scores_3d is not None) else np.ones_like(scores_3d, dtype=bool) + if np.any(mask): + boxes_3d = boxes_3d[mask] + scores_3d = scores_3d[mask] + labels_3d = labels_3d[mask] + boxes_3d_for_bev = boxes_3d.copy() + labels_3d_for_bev = labels_3d.copy() + scores_3d_for_bev = scores_3d.copy() + for cam_idx, vis_img in enumerate(raw_imgs): + vis_img = vis_img.copy() + if lidar2img.shape[1] > cam_idx: + cam_lidar2img = lidar2img[0, cam_idx] + for box, label in zip(boxes_3d, labels_3d): + color = CLASS_COLORS.get(int(label), (255, 255, 255)) + try: + vis_img = draw_bbox3d_on_img_custom_np(box[None], vis_img, cam_lidar2img, color=color, thickness=2) + except Exception: + pass + vis_imgs.append(vis_img) + else: + vis_imgs = raw_imgs + + if pc_range is None: + pc_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0] + + if boxes_3d_for_bev is not None and len(boxes_3d_for_bev) > 0: + bev_size = (vis_imgs[0].shape[0], vis_imgs[0].shape[0]) if vis_imgs else (800, 800) + bev_img = draw_bev_map(boxes_3d_for_bev, labels_3d_for_bev, scores_3d_for_bev, pc_range, bev_size=bev_size, score_thr=score_thr) + else: + bev_size = (vis_imgs[0].shape[0], vis_imgs[0].shape[0]) if vis_imgs else (800, 800) + bev_img = np.full((bev_size[1], bev_size[0], 3), 255, np.uint8) + cv2.putText(bev_img, 'BEV Map (No Detections)', (10, bev_size[1]//2), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2) + + if len(vis_imgs) == 6: + target_height = max(img.shape[0] for img in vis_imgs) + resized_imgs = [img if img.shape[0] == target_height else cv2.resize(img, (int(img.shape[1] * target_height / img.shape[0]), target_height)) for img in vis_imgs] + + reordered_imgs = [ + resized_imgs[2], resized_imgs[0], resized_imgs[1], + cv2.flip(resized_imgs[4], 1), cv2.flip(resized_imgs[3], 1), cv2.flip(resized_imgs[5], 1) + ] + top_row = np.hstack(reordered_imgs[:3]) + bottom_row = np.hstack(reordered_imgs[3:]) + left_side = np.vstack([top_row, bottom_row]) + bev_img = cv2.resize(bev_img, (int(bev_img.shape[1] * left_side.shape[0] / bev_img.shape[0]), left_side.shape[0])) + vis_img = np.hstack([left_side, bev_img]) + elif len(vis_imgs) > 1: + target_height = max(img.shape[0] for img in vis_imgs) + resized_imgs = [img if img.shape[0] == target_height else cv2.resize(img, (int(img.shape[1] * target_height / img.shape[0]), target_height)) for img in vis_imgs] + if bev_img.shape[0] != target_height: + bev_img = cv2.resize(bev_img, (int(bev_img.shape[1] * target_height / bev_img.shape[0]), target_height)) + vis_img = np.hstack([np.hstack(resized_imgs), bev_img]) + else: + cam_img = vis_imgs[0] if vis_imgs else bev_img + if bev_img.shape[0] != cam_img.shape[0]: + bev_img = cv2.resize(bev_img, (int(bev_img.shape[1] * cam_img.shape[0] / bev_img.shape[0]), cam_img.shape[0])) + vis_img = np.hstack([cam_img, bev_img]) if vis_imgs else bev_img + + return vis_img + + +def create_video_from_images(image_dir, output_video_path, fps=3): + import subprocess + + image_files = sorted([f for f in os.listdir(image_dir) if f.endswith(('.png', '.jpg', '.jpeg'))]) + if len(image_files) == 0: + return + + first_img = cv2.imread(osp.join(image_dir, image_files[0])) + if first_img is None: + return + + height, width = first_img.shape[:2] + + max_width, max_height = 1920, 1080 + if width > max_width or height > max_height: + scale = min(max_width / width, max_height / height) + width, height = int(width * scale), int(height * scale) + + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + video_writer = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height)) + if not video_writer.isOpened(): + fourcc = cv2.VideoWriter_fourcc(*'XVID') + video_writer = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height)) + + for img_file in tqdm(image_files, desc=f"Creating video: {osp.basename(output_video_path)}"): + img_path = osp.join(image_dir, img_file) + img = cv2.imread(img_path) + if img is not None: + if img.shape[:2] != (height, width): + img = cv2.resize(img, (width, height)) + video_writer.write(img) + + video_writer.release() + +def main(): + args = parse_args() + + # Load configuration from JSON + config = load_config_from_json(args.config_json) + + # Create output directory + os.makedirs(args.output_dir, exist_ok=True) + + # Load ONNX model + onnx_session = load_onnx_model(args.onnx_model, args.device) + + # Get model parameters from config + transformer_cfg = config['model']['transformer'] + bev_h = transformer_cfg['bev_h'] + bev_w = transformer_cfg['bev_w'] + embed_dims = transformer_cfg['embed_dims'] + + # Load scene index + scene_index_path = osp.join(args.data_dir, 'scene_index.json') + with open(scene_index_path, 'r') as f: + scene_index_data = json.load(f) + + scenes_dict = scene_index_data['scenes'] + scene_names = list(scenes_dict.keys()) + + end_scene = args.end_scene if args.end_scene is not None else len(scene_names) + end_scene = min(end_scene, len(scene_names)) + + prev_frame_info = { + 'prev_bev': None, + 'scene_token': None, + 'prev_pos': np.zeros(3, dtype=np.float32), + 'prev_angle': 0.0, + } + + scene_results = defaultdict(list) + + # Process all scenes + for scene_idx in range(args.start_scene, end_scene): + scene_name = scene_names[scene_idx] + scene_info = scenes_dict[scene_name] + sample_indices = scene_info['samples'] + num_frames = len(sample_indices) + + print(f"Processing scene {scene_idx+1}/{len(scene_names)}: {scene_name} ({num_frames} frames)") + + # Reset prev_bev for new scene + if scene_name != prev_frame_info['scene_token']: + prev_frame_info['prev_bev'] = None + prev_frame_info['prev_pos'] = np.zeros(3, dtype=np.float32) + prev_frame_info['prev_angle'] = 0.0 + + prev_frame_info['scene_token'] = scene_name + + # Process all frames in this scene + for local_idx, frame_idx in enumerate(tqdm(sample_indices, desc=f"Scene {scene_name}")): + # Load data + img, lidar2img, can_bus, meta = load_data(args.data_dir, scene_name, frame_idx) + + # Process can_bus (compute delta) + curr_can_bus_np = can_bus[0] # (18,) + + tmp_pos = curr_can_bus_np[:3].copy() + tmp_angle = curr_can_bus_np[-1] + + delta_can_bus_np = curr_can_bus_np.copy() + + if prev_frame_info['prev_bev'] is not None and prev_frame_info['scene_token'] == scene_name: + delta_can_bus_np[:3] -= prev_frame_info['prev_pos'] + delta_can_bus_np[-1] -= prev_frame_info['prev_angle'] + else: + delta_can_bus_np[:3] = 0.0 + delta_can_bus_np[-1] = 0.0 + + prev_frame_info['prev_pos'] = tmp_pos + prev_frame_info['prev_angle'] = tmp_angle + + # Prepare prev_bev + prev_bev_input = next((inp for inp in onnx_session.get_inputs() if inp.name == 'prev_bev'), None) + expected_shape = (bev_h * bev_w, 1, embed_dims) + if prev_bev_input is not None: + expected_shape = list(prev_bev_input.shape) + for i, dim in enumerate(expected_shape): + if isinstance(dim, str) or dim < 0: + expected_shape[i] = (bev_h * bev_w, 1, embed_dims)[i] if i < 3 else 1 + expected_shape = tuple(expected_shape) + + if prev_frame_info['prev_bev'] is None: + prev_bev = np.zeros(expected_shape, dtype=np.float32) + else: + prev_bev = prev_frame_info['prev_bev'] + if prev_bev.shape != expected_shape and len(prev_bev.shape) == 3: + prev_bev = prev_bev.reshape(expected_shape) + + # Prepare ONNX inputs + img_np = img.astype(np.float32) + lidar2img_np = lidar2img.astype(np.float32) + can_bus_np = delta_can_bus_np.reshape(1, -1).astype(np.float32) + + input_names = [inp.name for inp in onnx_session.get_inputs()] + onnx_inputs = {} + for name in input_names: + if name == 'img': + onnx_inputs['img'] = img_np + elif name == 'can_bus': + onnx_inputs['can_bus'] = can_bus_np + elif name == 'lidar2img': + onnx_inputs['lidar2img'] = lidar2img_np + elif name == 'prev_bev': + onnx_inputs['prev_bev'] = prev_bev + + # Run inference + onnx_outputs = onnx_session.run(None, onnx_inputs) + bev_embed, all_cls_scores, all_bbox_preds = onnx_outputs + + prev_frame_info['prev_bev'] = bev_embed + + # Post-process + results = post_process_outputs_np( + all_cls_scores, all_bbox_preds, config, args.score_thr + ) + + # Visualize + img_norm_cfg = config['img_norm'] + class_names = config['dataset']['class_names'] + pc_range = config['model']['bbox_coder']['pc_range'] + vis_img = visualize_results_np( + img, results[0], lidar2img, img_norm_cfg, class_names, args.score_thr, pc_range=pc_range + ) + + scene_results[scene_name].append({ + 'frame_idx': local_idx, + 'result': results[0], + 'vis_img': vis_img, + 'meta': meta + }) + + # Save results + for scene_name, frames in tqdm(scene_results.items(), desc="Save scene results"): + scene_dir = osp.join(args.output_dir, scene_name) + os.makedirs(scene_dir, exist_ok=True) + images_dir = osp.join(scene_dir, 'images') + os.makedirs(images_dir, exist_ok=True) + + for local_idx, frame_data in enumerate(frames): + vis_img = frame_data['vis_img'] + + if vis_img is None: + continue + + if not isinstance(vis_img, np.ndarray): + vis_img = np.array(vis_img) + + if vis_img.dtype != np.uint8: + vis_img = (vis_img * 255).astype(np.uint8) if vis_img.max() <= 1.0 else vis_img.astype(np.uint8) + + if len(vis_img.shape) == 3 and vis_img.shape[0] in (1, 3): + vis_img = vis_img.transpose(1, 2, 0) + + if vis_img.shape[0] > 0 and vis_img.shape[1] > 0: + cv2.imwrite(osp.join(images_dir, f'frame_{local_idx:06d}.png'), vis_img) + + create_video_from_images(images_dir, osp.join(scene_dir, f'{scene_name}_result.mp4'), args.fps) + print(f"✓ Scene {scene_name}: {len(frames)} frames, video: {osp.join(scene_dir, f'{scene_name}_result.mp4')}") + + +if __name__ == '__main__': + main() +