VSI-Bench-U — Gemma4-31B — sample 930

# Subsample frames to stay within the 64-frame limit for reconstruction
# 1670 / 50 is approx 33 frames, which is a good balance of coverage and speed.
subsampled_images = InputImages[::50]
recon = tools.Reconstruct.Reconstruct(subsampled_images)

print(f"Reconstruction complete. Number of frames used: {recon.num_frames}")
print(f"Metric scale: {recon.metric_scale}")

Reconstruction complete. Number of frames used: 34
Metric scale: 1.6682838201522827

# Target frames
washer_fi = 270
stool_fi = 1296

# Locate washer
washer_box_str = vlm.locate(InputImages[washer_fi], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the washing machine. Reply with ONLY the numbers.")
# Locate stool
stool_box_str = vlm.locate(InputImages[stool_fi], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the red stool. Reply with ONLY the numbers.")

print(f"Washer box string: {washer_box_str}")
print(f"Stool box string: {stool_box_str}")

def get_px_box(box_str, img):
    if not box_str or "Not visible" in box_str:
        return None
    try:
        coords = [int(x) for x in box_str.replace(',', ' ').split()]
        return tools.Geometry.normalized_to_pixel(coords, img.width, img.height)
    except Exception as e:
        print(f"Error parsing box: {e}")
        return None

washer_box = get_px_box(washer_box_str, InputImages[washer_fi])
stool_box = get_px_box(stool_box_str, InputImages[stool_fi])

# Segment the washer
if washer_box:
    seg_washer = tools.SAM3.segment_image_by_box(InputImages[washer_fi], washer_box, "washer")
else:
    seg_washer = None

# Segment the stool
if stool_box:
    seg_stool = tools.SAM3.segment_image_by_box(InputImages[stool_fi], stool_box, "stool")
else:
    seg_stool = None

# Visual verification
if seg_washer:
    show([InputImages[washer_fi], seg_washer.visualize(seg_washer.frame_indices[0])])
if seg_stool:
    show([InputImages[stool_fi], seg_stool.visualize(seg_stool.frame_indices[0])])

[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 washing machine. Reply with ONLY the numbers.
[VLM A | locate] 450 30 720 830
[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 red stool. Reply with ONLY the numbers.
[VLM A | locate] Not visible
Washer box string: 450 30 720 830
Stool box string: Not visible
 [image: show_0001] 

# Reconstruction frames
washer_fi = 300
stool_fi = 1600

# Locate washer
washer_box_str = vlm.locate(InputImages[washer_fi], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the washing machine. Reply with ONLY the numbers.")
# Locate stool
stool_box_str = vlm.locate(InputImages[stool_fi], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the small red stool. Reply with ONLY the numbers.")

print(f"Washer box string: {washer_box_str}")
print(f"Stool box string: {stool_box_str}")

def get_px_box(box_str, img):
    if not box_str or "Not visible" in box_str:
        return None
    try:
        coords = [int(x) for x in box_str.replace(',', ' ').split()]
        return tools.Geometry.normalized_to_pixel(coords, img.width, img.height)
    except:
        return None

washer_box = get_px_box(washer_box_str, InputImages[washer_fi])
stool_box = get_px_box(stool_box_str, InputImages[stool_fi])

# Segment washer
seg_washer = None
if washer_box:
    seg_washer = tools.SAM3.segment_image_by_box(InputImages[washer_fi], washer_box, "washer")

# Segment stool
seg_stool = None
if stool_box:
    seg_stool = tools.SAM3.segment_image_by_box(InputImages[stool_fi], stool_box, "stool")

# Visual verification
if seg_washer:
    show([InputImages[washer_fi], seg_washer.visualize(seg_washer.frame_indices[0])])
if seg_stool:
    show([InputImages[stool_fi], seg_stool.visualize(seg_stool.frame_indices[0])])

[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 washing machine. Reply with ONLY the numbers.
[VLM A | locate] 381 0 962 376
[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 small red stool. Reply with ONLY the numbers.
[VLM A | locate] Not visible
Washer box string: 381 0 962 376
Stool box string: Not visible
 [image: show_0001] 

# Visually inspect the frame to see where the stool is
show(InputImages[1650])

# Use a visual reasoner to get a bounding box for the stool in this specific frame
stool_box_str = vlm.ask_with_thinking(InputImages[1650], "Find the red stool on the floor. Give its bounding box (x1, y1, x2, y2) in 0-1000 normalized scale. Reply with ONLY the numbers.")

print(f"Stool box string: {stool_box_str}")

# Convert to pixels and segment
def get_px_box(box_str, img):
    if not box_str or "Not visible" in box_str:
        return None
    try:
        # Extract numbers from the string
        import re
        nums = re.findall(r'\d+', box_str)
        if len(nums) >= 4:
            coords = [int(x) for x in nums[:4]]
            return tools.Geometry.normalized_to_pixel(coords, img.width, img.height)
    except Exception as e:
        print(f"Error parsing box: {e}")
    return None

stool_box = get_px_box(stool_box_str, InputImages[1650])

if stool_box:
    seg_stool = tools.SAM3.segment_image_by_box(InputImages[1650], stool_box, "stool")
    show([InputImages[1650], seg_stool.visualize(seg_stool.frame_indices[0])])
else:
    print("Stool box could not be determined.")
    seg_stool = None

 [image: show_0002] 
[VLM Q | thinking] Find the red stool on the floor. Give its bounding box (x1, y1, x2, y2) in 0-1000 normalized scale. Reply with ONLY the numbers.
[VLM A | thinking] Cannot determine from the images.
No red stool is visible in the provided image.
Stool box string: Cannot determine from the images.
No red stool is visible in the provided image.
Stool box could not be determined.

# Target frame from the reconstruction set for the stool
stool_fi = 1300

# Visually inspect the frame to confirm the stool is there
show(InputImages[stool_fi])

# Use VLM to locate the red stool
stool_box_str = vlm.locate(InputImages[stool_fi], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the red stool. Reply with ONLY the numbers.")
print(f"Stool box string: {stool_box_str}")

def get_px_box(box_str, img):
    if not box_str or "Not visible" in box_str:
        return None
    try:
        import re
        nums = re.findall(r'\d+', box_str)
        if len(nums) >= 4:
            coords = [int(x) for x in nums[:4]]
            return tools.Geometry.normalized_to_pixel(coords, img.width, img.height)
    except Exception as e:
        print(f"Error parsing box: {e}")
    return None

stool_box = get_px_box(stool_box_str, InputImages[stool_fi])

if stool_box:
    seg_stool = tools.SAM3.segment_image_by_box(InputImages[stool_fi], stool_box, "stool")
    show([InputImages[stool_fi], seg_stool.visualize(seg_stool.frame_indices[0])])
else:
    print("Stool box could not be determined.")
    seg_stool = None

 [image: show_0003] 
[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 red stool. Reply with ONLY the numbers.
[VLM A | locate] Not visible
Stool box string: Not visible
Stool box could not be determined.

# Target frame from the reconstruction set for the stool
stool_fi = 1450

# Visually inspect the frame to confirm the stool is there
show(InputImages[stool_fi])

# Use VLM to locate the red stool
stool_box_str = vlm.locate(InputImages[stool_fi], "Give the bounding box (x1, y1, x2, y2) in 0-1000 normalized scale for the red stool. Reply with ONLY the numbers.")
print(f"Stool box string: {stool_box_str}")

def get_px_box(box_str, img):
    if not box_str or "Not visible" in box_str:
        return None
    try:
        import re
        nums = re.findall(r'\d+', box_str)
        if len(nums) >= 4:
            coords = [int(x) for x in nums[:4]]
            return tools.Geometry.normalized_to_pixel(coords, img.width, img.height)
    except Exception as e:
        print(f"Error parsing box: {e}")
    return None

stool_box = get_px_box(stool_box_str, InputImages[stool_fi])

if stool_box:
    seg_stool = tools.SAM3.segment_image_by_box(InputImages[stool_fi], stool_box, "stool")
    show([InputImages[stool_fi], seg_stool.visualize(seg_stool.frame_indices[0])])
else:
    print("Stool box could not be determined.")
    seg_stool = None

 [image: show_0004] 
[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 red stool. Reply with ONLY the numbers.
[VLM A | locate] Not visible
Stool box string: Not visible
Stool box could not be determined.

# Candidate reconstruction frames for the stool
stool_candidates = [1500, 1550, 1600]
seg_stool = None
stool_fi = None

for fi in stool_candidates:
    print(f"Checking frame {fi} for the red stool...")
    # Use vlm.ask_with_thinking to find the stool and get the box
    res = vlm.ask_with_thinking(InputImages[fi], "Is the red stool visible in this image? If yes, provide its bounding box (x1, y1, x2, y2) in 0-1000 normalized scale. Reply with 'No' or the numbers.")
    print(f"Frame {fi} result: {res}")
    
    if "No" not in res and any(char.isdigit() for char in res):
        import re
        nums = re.findall(r'\d+', res)
        if len(nums) >= 4:
            coords = [int(x) for x in nums[:4]]
            px_box = tools.Geometry.normalized_to_pixel(coords, InputImages[fi].width, InputImages[fi].height)
            seg_stool = tools.SAM3.segment_image_by_box(InputImages[fi], px_box, "stool")
            stool_fi = fi
            print(f"Successfully segmented stool in frame {fi}")
            show([InputImages[fi], seg_stool.visualize(seg_stool.frame_indices[0])])
            break

if seg_stool is None:
    print("Could not find the stool in any of the candidate reconstruction frames.")

Checking frame 1500 for the red stool...
[VLM Q | thinking] Is the red stool visible in this image? If yes, provide its bounding box (x1, y1, x2, y2) in 0-1000 normalized scale. Reply with 'No' or the numbers.
[VLM A | thinking] [408, 0, 537, 132]
Frame 1500 result: [408, 0, 537, 132]
Successfully segmented stool in frame 1500
 [image: show_0005] 

# Frame indices
fi_washer = 300
fi_stool = 1500

# Extract 3D points using the PerFrameMask helper
# This method handles the mapping from absolute frame index to reconstruction index
points_washer = seg_washer.get_masked_points(recon, frame=fi_washer)
points_stool = seg_stool.get_masked_points(recon, frame=fi_stool)

# To filter by confidence, we need the local index in the reconstruction
local_idx_washer = recon.frame_indices.index(fi_washer)
local_idx_stool = recon.frame_indices.index(fi_stool)

# Get confidence maps for these frames
conf_washer = recon.points.confidence[local_idx_washer]
conf_stool = recon.points.confidence[local_idx_stool]

# Get the masks to index into the confidence maps
mask_washer = seg_washer.get_mask(frame=fi_washer, object=0)
mask_stool = seg_stool.get_mask(frame=fi_stool, object=0)

# Filter points by confidence > 0.5
# We can do this by getting the points and confidence for the masked area
def get_filtered_points(recon, seg, fi, threshold=0.5):
    local_idx = recon.frame_indices.index(fi)
    mask = seg.get_mask(frame=fi, object=0)
    points = recon.points.points[local_idx][mask]
    conf = recon.points.confidence[local_idx][mask]
    return points[conf > threshold]

f_points_washer = get_filtered_points(recon, seg_washer, fi_washer)
f_points_stool = get_filtered_points(recon, seg_stool, fi_stool)

print(f"Filtered washer points: {len(f_points_washer)}")
print(f"Filtered stool points: {len(f_points_stool)}")

# Compute minimum distance
from scipy.spatial import distance
if len(f_points_washer) > 0 and len(f_points_stool) > 0:
    # Use cdist for efficient all-pairs distance
    dists = distance.cdist(f_points_washer, f_points_stool)
    min_dist = np.min(dists)
    print(f"Minimum distance: {min_dist:.4f} meters")
else:
    print("Not enough filtered points to compute distance.")
    min_dist = None

# Sanity check: Distance between centroids
c_washer = seg_washer.get_centroid_3d(recon, frame=fi_washer)
c_stool = seg_stool.get_centroid_3d(recon, frame=fi_stool)
if c_washer is not None and c_stool is not None:
    centroid_dist = tools.Geometry.euclidean_distance(c_washer, c_stool)
    print(f"Centroid distance: {centroid_dist:.4f} meters")

Filtered washer points: 71651
Filtered stool points: 1620
Minimum distance: 1.1568 meters
Centroid distance: 1.5782 meters

ReturnAnswer(1.1568)

[ReturnAnswer] Answer submitted: 1.1568