Upload visualize.py

scripts/visualize.py

@@ -0,0 +1,105 @@
+import os
+import cv2
+import h5py
+import imageio
+import argparse
+import numpy as np
+
+
+METER_PER_PIXEL_X = 0.2
+METER_PER_PIXEL_Y = 0.2
+BUCKET_HALF_X = 2
+BUCKET_HALF_Y = 2
+MAIN_BGR_H = 384
+MAIN_BGR_W = 480
+
+
+def excavator_kinematic(qpos, k=6.670, n=2.90746):
+    '''根据关节角度、大小臂长度计算末端位姿'''
+    # 490: k=6.670, n=2.90746
+    boom = qpos[:, 0]
+    arm = qpos[:, 1]
+    bucket = qpos[:, 2]
+    swing = qpos[:, 3]
+    x = k * np.sin(boom + 1) + n * np.sin(np.pi - boom - 1 - arm - 1.5)
+    z = k * np.cos(boom + 1) - n * np.cos(np.pi - boom - 1 - arm - 1.5) 
+    
+    return x, z, swing, bucket
+
+
+def resize_with_aspect_ratio(image, width=None, height=None):
+    if width is None and height is None:
+        return image
+    
+    h, w = image.shape[:2]
+    ratio = min(height / h, width / w)
+    new_h, new_w = int(h * ratio), int(w * ratio)
+    
+    # keep ratio resize
+    resized = cv2.resize(image, (new_w, new_h))
+    
+    # pad size
+    pad_top = (height - new_h) // 2
+    pad_bottom = height - new_h - pad_top
+    pad_left = (width - new_w) // 2
+    pad_right = width - new_w - pad_left
+    
+    # pad with zero
+    padded = cv2.copyMakeBorder(
+        resized, pad_top, pad_bottom, pad_left, pad_right, cv2.BORDER_CONSTANT, value=[0, 0, 0])
+
+    return padded
+
+
+def traj_visualization(data_path, out_dir, fmt='mp4', fps=30):
+    # load data
+    with h5py.File(data_path) as f:
+        elevations = np.array(f['observations']['images']['elevation'])
+        joints = np.array(f['observations']['qpos'])
+        mains = np.array(f['observations']['images']['main'])
+
+    assert elevations.shape[0] == joints.shape[0]
+    H, W = elevations.shape[1:3]
+
+    # forward kinemetics
+    x = excavator_kinematic(joints, k=6.670, n=2.90746)[0]
+    # x = excavator_kinematic(joints, k=3.6957, n=1.62233)[0]
+    px = (x / METER_PER_PIXEL_X).astype(np.int32)
+    
+    # visualization
+    frames = []
+    for i in range(elevations.shape[0]):
+        # end pose
+        traj_elevation = elevations[i].copy()
+        center_u = W // 2
+        center_v = H // 2 - px[i]
+        traj_elevation[center_v - BUCKET_HALF_X: center_v + BUCKET_HALF_X,
+                       center_u - BUCKET_HALF_Y: center_u + BUCKET_HALF_Y] = (255, 0, 0)
+
+        # elevation
+        traj_elevation = resize_with_aspect_ratio(traj_elevation, height=MAIN_BGR_H, width=MAIN_BGR_W)
+        
+        # main bgr
+        traj_main = mains[i].copy()[..., ::-1]
+        traj_main = resize_with_aspect_ratio(traj_main, height=MAIN_BGR_H, width=MAIN_BGR_W)
+        traj_img = np.concatenate([traj_main, traj_elevation], axis=1)
+        
+        frames.append(traj_img)
+    
+    # save
+    os.makedirs(out_dir, exist_ok=True)
+    imageio.mimsave(f"{out_dir}/{data_path.split('/')[-1].split('.')[0]}.{fmt}", frames, fps=fps)
+
+
+
+if __name__=="__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data_path", type=str, required=True, help='path to the h5 file')
+    parser.add_argument("--out_dir", type=str, required=True, help='path the output directory')
+    parser.add_argument("--format", type=str, required=False, default='mp4', help='format of the output file, mp4 or gif are supported')
+    parser.add_argument("--fps", type=str, required=False, default=30, help='FPS of the output file')
+    args = parser.parse_args()
+
+
+    "/home/netease/data/excavator_motion/490/robot_data_20250430_123028.h5"
+    traj_visualization(args.data_path, args.out_dir, args.format)