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import re
import numpy as np
def find_patch_index_combinations(s):
# The regular expression pattern for matching the required formats
pattern = r'(?:(<phrase>([^<]+)</phrase>))?<object>((?:<patch_index_\d+><patch_index_\d+></delimiter_of_multi_objects/>)*<patch_index_\d+><patch_index_\d+>)</object>'
# Find all matches in the given string
matches = re.findall(pattern, s)
# Initialize an empty list to store the valid patch_index combinations
valid_combinations = []
for match in matches:
phrase_tag, phrase, match_content = match
if not phrase_tag:
phrase = None
# Split the match_content by the delimiter to get individual patch_index pairs
patch_index_pairs = match_content.split('</delimiter_of_multi_objects/>')
for pair in patch_index_pairs:
# Extract the xxxx and yyyy values from the patch_index pair
x = re.search(r'<patch_index_(\d+)>', pair)
y = re.search(r'<patch_index_(\d+)>', pair[1:])
if x and y:
if phrase:
valid_combinations.append((phrase, int(x.group(1)), int(y.group(1))))
else:
valid_combinations.append((f"<{x.group(1)}><{y.group(1)}>", int(x.group(1)), int(y.group(1))))
return valid_combinations
def get_box_coords_from_index(P, ul_idx, lr_idx):
"""
Given a grid of length P and the indices of the upper-left and lower-right corners of a bounding box,
returns the normalized coordinates of the bounding box, in the form [x1, y1, x2, y2].
Args:
- P (int): the length of the grid
- ul_idx (int): the index of the grid cell that corresponds to the upper-left corner of the bounding box
- lr_idx (int): the index of the grid cell that corresponds to the lower-right corner of the bounding box
Returns:
- box_coords (np.array of shape (4,)): the normalized coordinates of the bounding box, in the form [x1, y1, x2, y2]
"""
# Compute the size of each cell in the grid
cell_size = 1.0 / P
# Compute the x and y indices of the upper-left and lower-right corners of the bounding box
ul_x = ul_idx % P
ul_y = ul_idx // P
lr_x = lr_idx % P
lr_y = lr_idx // P
# Compute the normalized coordinates of the bounding box
if ul_idx == lr_idx:
x1 = ul_x * cell_size
y1 = ul_y * cell_size
x2 = lr_x * cell_size + cell_size
y2 = lr_y * cell_size + cell_size
elif ul_x == lr_x or ul_y == lr_y:
x1 = ul_x * cell_size
y1 = ul_y * cell_size
x2 = lr_x * cell_size + cell_size
y2 = lr_y * cell_size + cell_size
else:
x1 = ul_x * cell_size + cell_size / 2
y1 = ul_y * cell_size + cell_size / 2
x2 = lr_x * cell_size + cell_size / 2
y2 = lr_y * cell_size + cell_size / 2
return np.array([x1, y1, x2, y2])
def decode_bbox_from_caption(caption, quantized_size=32, **kwargs):
valid_combinations = find_patch_index_combinations(caption)
entity_names = list(map(lambda x: x[0], valid_combinations))
patch_index_coords = list(map(lambda pair: get_box_coords_from_index(quantized_size, pair[1], pair[2]), valid_combinations))
collect_entity_location = []
for entity_name, patch_index_coord in zip(entity_names, patch_index_coords):
collect_entity_location.append([entity_name,] + patch_index_coord.tolist())
return collect_entity_location
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