ViewSpatial — Gemma4-31B — sample 5449

# Identify frames containing the desk, television, and toilet
question = "In which frames are the following objects visible? Please provide the frame indices (0-9). \n1. Desk\n2. Television\n3. Toilet"
answer = vlm.ask_with_thinking(InputImages, question)
print(answer)

# Also show the frames to visually confirm
show(InputImages)

[VLM Q | thinking] In which frames are the following objects visible? Please provide the frame indices (0-9). 
1. Desk
2. Television
3. Toilet
[VLM A | thinking] 1. Desk: 0, 1, 2, 3, 4, 7
2. Television: 8, 9
3. Toilet: 9
1. Desk: 0, 1, 2, 3, 4, 7
2. Television: 8, 9
3. Toilet: 9
 [image: show_0001] 

# 1. 3D Reconstruction
recon = tools.Reconstruct.Reconstruct(InputImages)

# 2. Segmentation
# Desk in frame 4
seg_desk = tools.SAM3.segment_image_by_text(InputImages[4], "desk")
# Television in frame 8
seg_tv = tools.SAM3.segment_image_by_text(InputImages[8], "television")
# Toilet in frame 9
seg_toilet = tools.SAM3.segment_image_by_text(InputImages[9], "toilet")

# Verification of masks
# Desk
fi_desk = seg_desk.frame_indices[0]
mask_desk = seg_desk.get_mask(frame=fi_desk, object=0)
print(f"Desk mask area: {mask_desk.sum()} pixels")

# TV
fi_tv = seg_tv.frame_indices[0]
mask_tv = seg_tv.get_mask(frame=fi_tv, object=0)
print(f"TV mask area: {mask_tv.sum()} pixels")

# Toilet
fi_toilet = seg_toilet.frame_indices[0]
mask_toilet = seg_toilet.get_mask(frame=fi_toilet, object=0)
print(f"Toilet mask area: {mask_toilet.sum()} pixels")

# Visual verification
show([
    seg_desk.visualize(fi_desk),
    seg_tv.visualize(fi_tv),
    seg_toilet.visualize(fi_toilet)
])

Desk mask area: 55902 pixels
TV mask area: 37623 pixels
Toilet mask area: 6683 pixels
 [image: show_0002] 

# 1. Extract 3D centroids
p_desk = seg_desk.get_centroid_3d(recon, frame=fi_desk, object=0)
p_tv = seg_tv.get_centroid_3d(recon, frame=fi_tv, object=0)
p_toilet = seg_toilet.get_centroid_3d(recon, frame=fi_toilet, object=0)

print(f"Desk centroid: {p_desk}")
print(f"TV centroid: {p_tv}")
print(f"Toilet centroid: {p_toilet}")

# 2. Relative direction computation
# Forward vector: Desk -> TV
v_fwd = p_tv - p_desk
v_fwd = v_fwd / np.linalg.norm(v_fwd)

# World Up vector
v_up = np.array([0, 1, 0])

# Right vector: Forward x Up
v_right = np.cross(v_fwd, v_up)
v_right = v_right / np.linalg.norm(v_right)

# Target vector: Desk -> Toilet
v_target = p_toilet - p_desk

# Projections
proj_fwd = np.dot(v_target, v_fwd)
proj_right = np.dot(v_target, v_right)

print(f"Projection Forward: {proj_fwd:.3f}")
print(f"Projection Right: {proj_right:.3f}")

# 3. Determine direction label
if proj_fwd > 0:
    direction = "front"
else:
    if proj_right > 0:
        direction = "back-right"
    elif proj_right < 0:
        direction = "back-left"
    else:
        direction = "back"

print(f"Calculated direction: {direction}")

# 4. Visual Verification with BEV
# Combine masks for BEV rendering
# Since we have separate PerFrameMask objects, we can't easily combine them into one 
# without creating a new one or passing them as a list/array.
# Let's create a combined mask array for the reference frame.
ref_fi = recon.frame_indices[0]
# We can just pass the individual masks to render_bev if we wrap them.
# Actually, the simplest is to use the labels and masks.
# Let's just use the centroids to plot manually if needed, 
# but let's try to use the provided render_bev.

# Create a combined mask structure for BEV
# We need masks for a range of frames. Let's just use the ones we have.
# The render_bev expects (N, N_obj, H, W) or PerFrameMask.
# Let's just manually check the centroids on a BEV of egomotion or similar.
# Or just trust the math if centroids are clean.

# To be safe, let's render a BEV with just the masks we have.
# We'll create a simple array for one frame to see relative positions.
# Since the objects are in different frames, we can't easily put them in one 2D mask.
# But we can just plot the 3D centroids on a 2D plot (X-Z plane).

plt.figure(figsize=(6, 6))
plt.scatter(p_desk[0], p_desk[2], color='blue', label='Desk')
plt.scatter(p_tv[0], p_tv[2], color='red', label='TV')
plt.scatter(p_toilet[0], p_toilet[2], color='green', label='Toilet')
plt.xlabel('World X')
plt.ylabel('World Z')
plt.title('Top-down view (X-Z plane)')
plt.legend()
plt.grid(True)
plt.axis('equal')
plt.show()

Desk centroid: [ 0.5674441  1.7442945 -3.476451 ]
TV centroid: [ 2.2806118  1.475707  -1.7709688]
Toilet centroid: [ 4.4783      0.14239559 -1.932271  ]
Projection Forward: 4.014
Projection Right: -1.665
Calculated direction: front
 [image: matplotlib figure] 
 [image: Top-down view (X-Z plane)] 

ReturnAnswer("C")

[ReturnAnswer] Answer submitted: C