MMSI — Gemma4-31B — sample 953

# Display the images to confirm content
show([InputImages[0], InputImages[1]])

# Ground the bulletin board and the wall in the first image
board_coords = vlm.locate(InputImages[0], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the bulletin board. Reply with ONLY the numbers.")
wall_coords = vlm.locate(InputImages[0], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the wall that the bulletin board is attached to. Reply with ONLY the numbers.")

# Ground the fitness area in the second image
fitness_coords = vlm.locate(InputImages[1], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the fitness area (the gym equipment and space). Reply with ONLY the numbers.")

print(f"Bulletin Board BBox: {board_coords}")
print(f"Wall BBox: {wall_coords}")
print(f"Fitness Area BBox: {fitness_coords}")

 [image: show_0001] 
[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 bulletin board. Reply with ONLY the numbers.
[VLM A | locate] 113 0 886 1000
[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 wall that the bulletin board is attached to. Reply with ONLY the numbers.
[VLM A | locate] 0 0 1000 1000
[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 fitness area (the gym equipment and space). Reply with O

# Get bounding box for a piece of gym equipment to represent the fitness area
fitness_box_str = vlm.locate(InputImages[1], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the large weight machine on the left. Reply with ONLY the numbers.")
# Get bounding box for the bulletin board in the same image
board_box_str = vlm.locate(InputImages[1], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the bulletin board on the right wall. Reply with ONLY the numbers.")

# Convert to pixels
W1, H1 = InputImages[1].width, InputImages[1].height
f_box = tools.Geometry.normalized_to_pixel([int(x) for x in fitness_box_str.split()], W1, H1)
b_box = tools.Geometry.normalized_to_pixel([int(x) for x in board_box_str.split()], W1, H1)

# Segment using boxes
seg_fitness = tools.SAM3.segment_image_by_box(InputImages[1], f_box, "fitness area")
seg_board = tools.SAM3.segment_image_by_box(InputImages[1], b_box, "bulletin board")

# Verify masks
show([InputImages[1], seg_fitness.visualize(seg_fitness.frame_indices[0]), seg_board.visualize(seg_board.frame_indices[0])])

# Perform reconstruction
recon = tools.Reconstruct.Reconstruct(InputImages)

# Get 3D centroids
fi = 1 # Using frame index 1
p_fitness = seg_fitness.get_centroid_3d(recon, frame=fi, object=0)
p_board = seg_board.get_centroid_3d(recon, frame=fi, object=0)

print(f"Fitness Centroid: {p_fitness}")
print(f"Board Centroid: {p_board}")

[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 large weight machine on the left. Reply with ONLY the numbers.
[VLM A | locate] 20 70 520 1000
[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 bulletin board on the right wall. Reply with ONLY the numbers.
[VLM A | locate] 813 414 867 582
 [image: show_0002] 
Fitness Centroid: [-2.8548968  0.8263396  3.1973398]
Board Centroid: [ 1.1736085  2.052884  -0.2000069]

import numpy as np

# 1. Get 3D points of the bulletin board to estimate the wall normal
points_board = seg_board.get_masked_points(recon, frame=fi, object=0)
# Use SVD to find the normal to the best-fitting plane
centroid_board_pts = np.median(points_board, axis=0)
centered_board_pts = points_board - centroid_board_pts
_, _, vh = np.linalg.svd(centered_board_pts)
normal = vh[2, :] # The normal is the last row of Vh

# Ensure the normal points INTO the wall.
# In InputImages[1], the board is on the right wall. The camera is looking roughly forward.
# The board's centroid is at x ~ 1.17, and the fitness area is at x ~ -2.85.
# The wall is to the right of the room center. A vector pointing into the wall should have a positive X component.
if normal[0] < 0:
    normal = -normal

v_front = normal
v_up = np.array([0, 1, 0])
v_right = np.cross(v_front, v_up)

# 2. Target vector
v_target = p_fitness - p_board

# 3. Projections
dot_front = np.dot(v_target, v_front)
dot_right = np.dot(v_target, v_right)

print(f"v_front: {v_front}")
print(f"v_right: {v_right}")
print(f"v_target: {v_target}")
print(f"dot_front: {dot_front}")
print(f"dot_right: {dot_right}")

# Determine direction
if abs(dot_front) > abs(dot_right):
    direction = "Front" if dot_front > 0 else "Back"
else:
    direction = "Right" if dot_right > 0 else "Left"

print(f"Resulting Direction: {direction}")

v_front: [ 0.97647     0.07692233 -0.20146778]
v_right: [ 0.20146778 -0.          0.97646999]
v_target: [-4.0285053 -1.2265445  3.3973467]
dot_front: -4.71251916885376
dot_right: 2.5057931108432854
Resulting Direction: Back

ReturnAnswer("C")

[ReturnAnswer] Answer submitted: C