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workaround for large overlapping bboxes

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Dillon DuPont
2025-03-27 13:22:55 -04:00
parent 85404cccab
commit 0836bb7e8f
2 changed files with 35 additions and 12 deletions
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26
libs/som/som/detect.py
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@@ -232,17 +232,31 @@ class OmniParser:
],
)
# Merge detections using NMS
if elements and text_elements:
# Get all bounding boxes and scores
# Filter out non-OCR elements that have OCR elements with center points colliding with them
filtered_elements = []
for elem in elements: # elements at this point contains only non-OCR elements
should_keep = True
for text_elem in text_elements:
# Calculate center point of the text element
center_x = (text_elem.bbox.x1 + text_elem.bbox.x2) / 2
center_y = (text_elem.bbox.y1 + text_elem.bbox.y2) / 2
# Check if this center point is inside the non-OCR element
if (center_x >= elem.bbox.x1 and center_x <= elem.bbox.x2 and
center_y >= elem.bbox.y1 and center_y <= elem.bbox.y2):
should_keep = False
break
if should_keep:
filtered_elements.append(elem)
elements = filtered_elements
# Merge detections using NMS
all_elements = elements + text_elements
boxes = torch.tensor([elem.bbox.coordinates for elem in all_elements])
scores = torch.tensor([elem.confidence for elem in all_elements])
# Apply NMS with iou_threshold
keep_indices = torchvision.ops.nms(boxes, scores, iou_threshold)
# Keep only the elements that passed NMS
elements = [all_elements[i] for i in keep_indices]
else:
# Just add text elements to the list if IOU doesn't need to be applied

21
libs/som/som/visualization.py
View File

@@ -174,22 +174,31 @@ class BoxAnnotator:
lambda: (x1 - box_width - spacing, y2 + spacing),
]
def check_collision(x, y):
"""Check if a label box collides with any existing ones or is inside bbox."""
def check_occlusion(x, y):
"""Check if a label box occludes any existing ones or is inside bbox."""
# First check if it's inside the bounding box
if is_inside_bbox(x, y):
return True
# Then check collision with other labels
new_box = (x, y, x + box_width, y + box_height)
label_width = new_box[2] - new_box[0]
label_height = new_box[3] - new_box[1]
for used_box in used_areas:
if not (
new_box[2] < used_box[0] # new box is left of used box
or new_box[0] > used_box[2] # new box is right of used box
or new_box[3] < used_box[1] # new box is above used box
or new_box[1] > used_box[3]
): # new box is below used box
return True
or new_box[1] > used_box[3] # new box is below used box
):
# Calculate dimensions of the used box
used_box_width = used_box[2] - used_box[0]
used_box_height = used_box[3] - used_box[1]
# Only consider as collision if used box is NOT more than 5x bigger in both dimensions
if not (used_box_width > 5 * label_width and used_box_height > 5 * label_height):
return True
return False
# Try each position until we find one without collision
@@ -201,7 +210,7 @@ class BoxAnnotator:
# Ensure position is within image bounds
if x < 0 or y < 0 or x + box_width > image.width or y + box_height > image.height:
continue
if not check_collision(x, y):
if not check_occlusion(x, y):
label_x = x
label_y = y
break