VSI-Bench-U — Gemma4-31B — sample 2454

# Select frames for object identification
# Frame 0: Monitor
# Frame 284: Ceiling light
# Frame 950: Door
frames_to_check = [InputImages[0], InputImages[284], InputImages[950]]

# 1. Identify objects and confirm visibility
confirmation = vlm.ask_with_thinking(frames_to_check, "In these frames, can you identify the ceiling light (long white tube), the computer monitor, and the door? Tell me which frame index (0, 1, or 2) shows each object most clearly.")
print(f"VLM Confirmation: {confirmation}")

# 2. Locate the objects in the best frames
# Monitor in frame 0
monitor_box_norm = vlm.locate(InputImages[0], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the computer monitor. Reply with ONLY the numbers.")
print(f"Monitor box (norm): {monitor_box_norm}")

# Ceiling light in frame 284
light_box_norm = vlm.locate(InputImages[284], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the long white ceiling light. Reply with ONLY the numbers.")
print(f"Light box (norm): {light_box_norm}")

# Door in frame 950
door_box_norm = vlm.locate(InputImages[950], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the door. Reply with ONLY the numbers.")
print(f"Door box (norm): {door_box_norm}")

[VLM Q | thinking] In these frames, can you identify the ceiling light (long white tube), the computer monitor, and the door? Tell me which frame index (0, 1, or 2) shows each object most clearly.
[VLM A | thinking] The ceiling light (long white tube) is most clearly shown in frame 1, the computer monitor in frame 0, and the door in frame 2.
VLM Confirmation: The ceiling light (long white tube) is most clearly shown in frame 1, the computer monitor in frame 0, and the door in frame 2.
[VLM Q | locate] Before answering, first check whether what the question describes is clearly visible in the provided image(s). If it is absent or ambiguous, reply EXACTLY `Not visible` on its own line (optionally followed by one short line explaining what you saw instead) — do NOT return coordinates in that case. Only if the requested target is clearly and unambiguously present, answer the request below.

Request: Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the computer monitor. Reply with ONLY the numbers.
[VLM A | locate] 200 195 496 565
Monitor box (norm): 200 195 496 565
[VLM Q | locate] Before answering, first check whether what the question describes is clearly visible in the provided image(s). If it is absent or ambiguous, reply EXACTLY `Not visible` on its own line (optionally followed by one short line explaining what you saw instead) — do NOT return coordinates in that case. Only if the requested target is clearly and unambiguously present, answer the request below.

Request: Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the long white ceiling light. Reply with ONLY the numbers.
[VLM A | locate] 548 0 598 146
Light box (norm): 548 0 598 146
[VLM Q | locate] Before answering, first check whether what the question describes is clearly visible in the provided image(s). If it is absent or ambiguous, reply EXACTLY `Not visible` on its own line (optionally followed by one short line explaining what you saw instead) — do NOT return 

# 1. Convert normalized coordinates to pixels
def norm_to_px(norm_str, img):
    coords = [int(x) for x in norm_str.split()]
    return tools.Geometry.normalized_to_pixel(coords, img.width, img.height)

monitor_px = norm_to_px(monitor_box_norm, InputImages[0])
light_px = norm_to_px(light_box_norm, InputImages[284])
door_px = norm_to_px(door_box_norm, InputImages[950])

print(f"Monitor px: {monitor_px}")
print(f"Light px: {light_px}")
print(f"Door px: {door_px}")

# 2. 3D Reconstruction
# Subsample InputImages to 32 frames for reconstruction
indices = np.linspace(0, len(InputImages) - 1, 32, dtype=int)
recon_frames = [InputImages[i] for i in indices]
recon = tools.Reconstruct.Reconstruct(recon_frames)

print("Reconstruction complete.")

Monitor px: [153.60000000000002, 112.32000000000001, 380.928, 325.43999999999994]
Light px: [420.86400000000003, 0.0, 459.264, 84.09599999999999]
Door px: [134.39999999999998, 0.0, 702.72, 576.0]
Reconstruction complete.

# 1. Segment the door in frame 950
seg_door = tools.SAM3.segment_image_by_text(InputImages[950], "door")

# 2. Try to segment the ceiling light in frame 284 again with a different prompt
# I'll try a few prompts to be sure
light_prompts = ["fluorescent light", "ceiling lamp", "white tube light"]
seg_light = None
for prompt in light_prompts:
    temp_seg = tools.SAM3.segment_image_by_text(InputImages[284], prompt)
    if temp_seg.num_objects > 0:
        seg_light = temp_seg
        print(f"Successfully segmented light with prompt: {prompt}")
        break

# 3. Extract 3D centroids
def get_obj_centroid(seg, recon, frame_idx):
    if seg is None or seg.num_objects == 0:
        return None
    # Use the first object found
    return seg.get_centroid_3d(recon, frame=frame_idx, object=0)

# Monitor centroid (from seg_monitor in Step 3)
p_monitor = get_obj_centroid(seg_monitor, recon, 0)

# Light centroid
p_light = get_obj_centroid(seg_light, recon, 284)

# Door centroid
p_door = get_obj_centroid(seg_door, recon, 950)

print(f"P_monitor: {p_monitor}")
print(f"P_light: {p_light}")
print(f"P_door: {p_door}")

# Visual verification of masks
if seg_door:
    fi_door = seg_door.frame_indices[0]
    show([InputImages[fi_door], seg_door.visualize(fi_door)])
if seg_light:
    fi_light = seg_light.frame_indices[0]
    show([InputImages[fi_light], seg_light.visualize(fi_light)])

Successfully segmented light with prompt: fluorescent light
P_monitor: [-1.2835853  1.6072092 -0.8871776]
P_light: [ 0.478106   3.0092416 -0.5048477]
P_door: [-1.4424605   0.685904    0.64404464]
 [image: show_0001] 
 [image: show_0002] 

import numpy as np

# 1. Define the observer's coordinate system
p_light = p_light.astype(float)
p_monitor = p_monitor.astype(float)
p_door = p_door.astype(float)

# Forward vector: Light -> Monitor
f_vec = p_monitor - p_light
f_vec /= np.linalg.norm(f_vec)

# World up vector
u_vec = np.array([0, 1, 0])

# Right vector: f x u
r_vec = np.cross(f_vec, u_vec)
r_vec /= np.linalg.norm(r_vec)

# Vector from observer to door
v_door = p_door - p_light

# Dot product to find if door is left or right
score = np.dot(v_door, r_vec)

print(f"Forward vector: {f_vec}")
print(f"Right vector: {r_vec}")
print(f"Vector to door: {v_door}")
print(f"Score (dot product): {score}")

if score > 0:
    result = "right"
else:
    result = "left"

print(f"Quantitative result: The door is to the {result} of the monitor.")

# 2. Visual Cross-Validation with BEV
# Create a combined PerFrameMask for the BEV
# We can just use a list of masks or a custom object. 
# For render_bev, we can pass masks as a raw array (N, N_obj, H, W)
# Since we only have single-frame masks, we can create a dummy PerFrameMask or just use labels.

# To use render_bev effectively, we need a PerFrameMask object.
# Let's create a simple one by combining the masks we have.
class SimpleMasks:
    def __init__(self, masks, labels, frame_indices):
        self.masks = masks # (N, N_obj, H, W)
        self.labels = labels
        self.frame_indices = frame_indices
        self.num_objects = len(labels)
        self.num_frames = len(frame_indices)

# We'll use the frames we segmented
fi_mon = seg_monitor.frame_indices[0]
fi_light = seg_light.frame_indices[0]
fi_door = seg_door.frame_indices[0]

# For BEV, we need masks on the SAME frame or across frames.
# Since the objects are static, we can just use the masks from their respective frames.
# But render_bev expects masks aligned with the reconstruction frames.
# A simpler way: just use the centroids we already have to draw a plot.
plt.figure(figsize=(8, 8))
plt.scatter(p_light[0], p_light[2], c='yellow', label='Ceiling Light', s=100, marker='*')
plt.scatter(p_monitor[0], p_monitor[2], c='blue', label='Monitor', s=100)
plt.scatter(p_door[0], p_door[2], c='red', label='Door', s=100)
plt.quiver(p_light[0], p_light[2], f_vec[0], f_vec[2], color='green', scale=5, label='Forward')
plt.quiver(p_light[0], p_light[2], r_vec[0], r_vec[2], color='orange', scale=5, label='Right')
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()

Forward vector: [-0.7714093  -0.61392186 -0.16741459]
Right vector: [ 0.21208719  0.         -0.97725075]
Vector to door: [-1.92056653 -2.32333755  1.14889234]
Score (dot product): -1.5300834635363088
Quantitative result: The door is to the left of the monitor.
 [image: Top-down View (X-Z Plane)] 

ReturnAnswer("B")

[ReturnAnswer] Answer submitted: B