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import numpy as np
def calculate_iou(box1, box2):
x1_min, y1_min, x1_max, y1_max = box1
x2_min, y2_min, x2_max, y2_max = box2
inter_x_min = max(x1_min, x2_min)
inter_y_min = max(y1_min, y2_min)
inter_x_max = min(x1_max, x2_max)
inter_y_max = min(y1_max, y2_max)
inter_area = max(0, inter_x_max - inter_x_min) * max(0, inter_y_max - inter_y_min)
box1_area = (x1_max - x1_min) * (y1_max - y1_min)
box2_area = (x2_max - x2_min) * (y2_max - y2_min)
union_area = box1_area + box2_area - inter_area
iou = inter_area / union_area
return iou
def compute_iou(box1, box2):
"""
Compute the Intersection over Union (IoU) of two bounding boxes.
Parameters:
- box1: list or array [x1, y1, x2, y2]
- box2: list or array [x1, y1, x2, y2]
Returns:
- iou: float, IoU value
"""
x1_inter = max(box1[0], box2[0])
y1_inter = max(box1[1], box2[1])
x2_inter = min(box1[2], box2[2])
y2_inter = min(box1[3], box2[3])
# print(x2_inter, x1_inter, y2_inter, y1_inter)
inter_area = max(0, x2_inter - x1_inter + 1) * max(0, y2_inter - y1_inter + 1)
box1_area = (box1[2] - box1[0] + 1) * (box1[3] - box1[1] + 1)
box2_area = (box2[2] - box2[0] + 1) * (box2[3] - box2[1] + 1)
iou = inter_area / float(box1_area + box2_area - inter_area)
return iou
def merge_boxes(box1, box2):
x1_min, y1_min, x1_max, y1_max = box1
x2_min, y2_min, x2_max, y2_max = box2
merged_box = [min(x1_min, x2_min), min(y1_min, y2_min), max(x1_max, x2_max), max(y1_max, y2_max)]
return merged_box
def merge_boxes_and_texts(texts, boxes, iou_threshold=0):
"""
Merge bounding boxes and their corresponding texts based on IoU threshold.
Parameters:
- boxes: List of bounding boxes, with each box represented as [x1, y1, x2, y2].
- texts: List of texts corresponding to each bounding box.
- iou_threshold: Intersection-over-Union threshold for merging boxes.
Returns:
- merged_boxes: List of merged bounding boxes.
- merged_texts: List of merged texts corresponding to the bounding boxes.
"""
if len(boxes) == 0:
return [], []
# boxes = np.array(boxes)
merged_boxes = []
merged_texts = []
while len(boxes) > 0:
box = boxes[0]
text = texts[0]
boxes = boxes[1:]
texts = texts[1:]
to_merge_boxes = [box]
to_merge_texts = [text]
keep_boxes = []
keep_texts = []
for i, other_box in enumerate(boxes):
if compute_iou(box, other_box) > iou_threshold:
to_merge_boxes.append(other_box)
to_merge_texts.append(texts[i])
else:
keep_boxes.append(other_box)
keep_texts.append(texts[i])
# Merge the to_merge boxes into a single box
if len(to_merge_boxes) > 1:
x1 = min(b[0] for b in to_merge_boxes)
y1 = min(b[1] for b in to_merge_boxes)
x2 = max(b[2] for b in to_merge_boxes)
y2 = max(b[3] for b in to_merge_boxes)
merged_box = [x1, y1, x2, y2]
merged_text = " ".join(to_merge_texts) # You can change the merging strategy here
merged_boxes.append(merged_box)
merged_texts.append(merged_text)
else:
merged_boxes.extend(to_merge_boxes)
merged_texts.extend(to_merge_texts)
# boxes = np.array(keep_boxes)
boxes = keep_boxes
texts = keep_texts
return merged_texts, merged_boxes
def is_contained(bbox1, bbox2):
x1_min, y1_min, x1_max, y1_max = bbox1
x2_min, y2_min, x2_max, y2_max = bbox2
if (x1_min >= x2_min and y1_min >= y2_min and x1_max <= x2_max and y1_max <= y2_max):
return True
elif (x2_min >= x1_min and y2_min >= y1_min and x2_max <= x1_max and y2_max <= y1_max):
return True
return False
def is_overlapping(bbox1, bbox2):
x1_min, y1_min, x1_max, y1_max = bbox1
x2_min, y2_min, x2_max, y2_max = bbox2
inter_xmin = max(x1_min, x2_min)
inter_ymin = max(y1_min, y2_min)
inter_xmax = min(x1_max, x2_max)
inter_ymax = min(y1_max, y2_max)
if inter_xmin < inter_xmax and inter_ymin < inter_ymax:
return True
return False
def get_area(bbox):
x_min, y_min, x_max, y_max = bbox
return (x_max - x_min) * (y_max - y_min)
def merge_all_icon_boxes(bboxes):
result_bboxes = []
while bboxes:
bbox = bboxes.pop(0)
to_add = True
for idx, existing_bbox in enumerate(result_bboxes):
if is_contained(bbox, existing_bbox):
if get_area(bbox) > get_area(existing_bbox):
result_bboxes[idx] = existing_bbox
to_add = False
break
elif is_overlapping(bbox, existing_bbox):
if get_area(bbox) < get_area(existing_bbox):
result_bboxes[idx] = bbox
to_add = False
break
if to_add:
result_bboxes.append(bbox)
return result_bboxes
def merge_all_icon_boxes_new(elements):
result_elements = []
while elements:
ele = elements.pop(0)
bbox = [ele['position'][0], ele['position'][1], ele['position'][0]+ele['size'][0], ele['position'][1]+ele['size'][1]]
# bbox = bboxes.pop(0)
to_add = True
for idx, existing_ele in enumerate(result_elements):
existing_bbox = [existing_ele['position'][0], existing_ele['position'][1], existing_ele['position'][0]+existing_ele['size'][0], existing_ele['position'][1]+existing_ele['size'][1]]
if is_contained(bbox, existing_bbox):
if get_area(bbox) > get_area(existing_bbox):
result_elements[idx] = existing_ele
to_add = False
break
elif is_overlapping(bbox, existing_bbox):
if get_area(bbox) < get_area(existing_bbox):
result_elements[idx] = ele
to_add = False
break
if to_add:
result_elements.append(ele)
return result_elements
def merge_bbox_groups(A, B, iou_threshold=0.8):
i = 0
while i < len(A):
box_a = A[i]
has_merged = False
for j in range(len(B)):
box_b = B[j]
iou = calculate_iou(box_a, box_b)
if iou > iou_threshold:
merged_box = merge_boxes(box_a, box_b)
A[i] = merged_box
B.pop(j)
has_merged = True
break
if has_merged:
i -= 1
i += 1
return A, B
def bbox_iou(boxA, boxB):
# Calculate Intersection over Union (IoU) between two bounding boxes
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
iou = interArea / float(boxAArea + boxBArea - interArea)
return iou
def merge_boxes_and_texts_new(texts, bounding_boxes, iou_threshold=0):
if not bounding_boxes:
return [], []
bounding_boxes = np.array(bounding_boxes)
merged_boxes = []
merged_texts = []
used = np.zeros(len(bounding_boxes), dtype=bool)
for i, boxA in enumerate(bounding_boxes):
if used[i]:
continue
x_min, y_min, x_max, y_max = boxA
# text = texts[i]
text = ''
overlapping_indices = [i] # []
for j, boxB in enumerate(bounding_boxes):
# print(i,j, bbox_iou(boxA, boxB))
if i != j and not used[j] and bbox_iou(boxA, boxB) > iou_threshold:
overlapping_indices.append(j)
# Sort overlapping boxes by vertical position (top to bottom)
overlapping_indices.sort(key=lambda idx: (bounding_boxes[idx][1] + bounding_boxes[idx][3])/2) # TODO
for idx in overlapping_indices:
boxB = bounding_boxes[idx]
x_min = min(x_min, boxB[0])
y_min = min(y_min, boxB[1])
x_max = max(x_max, boxB[2])
y_max = max(y_max, boxB[3])
# text += " " + texts[idx]
text += texts[idx]
used[idx] = True
merged_boxes.append([x_min, y_min, x_max, y_max])
merged_texts.append(text)
used[i] = True
return merged_texts, merged_boxes
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