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from transformers import ConditionalDetrImageProcessor, TrOCRProcessor, ViTImageProcessor
from transformers.image_transforms import center_to_corners_format
import torch
from typing import List
from shapely.geometry import box
from .utils import UnionFind, sort_panels, sort_text_boxes_in_reading_order, x1y1x2y2_to_xywh
import numpy as np
class MagiProcessor():
def __init__(self, config):
self.config = config
self.detection_image_preprocessor = None
self.ocr_preprocessor = None
self.crop_embedding_image_preprocessor = None
if not config.disable_detections:
assert config.detection_image_preprocessing_config is not None
self.detection_image_preprocessor = ConditionalDetrImageProcessor.from_dict(config.detection_image_preprocessing_config)
if not config.disable_ocr:
assert config.ocr_pretrained_processor_path is not None
self.ocr_preprocessor = TrOCRProcessor.from_pretrained(config.ocr_pretrained_processor_path)
if not config.disable_crop_embeddings:
assert config.crop_embedding_image_preprocessing_config is not None
self.crop_embedding_image_preprocessor = ViTImageProcessor.from_dict(config.crop_embedding_image_preprocessing_config)
def preprocess_inputs_for_detection(self, images, annotations=None):
images = list(images)
assert isinstance(images[0], np.ndarray)
annotations = self._convert_annotations_to_coco_format(annotations)
inputs = self.detection_image_preprocessor(images, annotations=annotations, return_tensors="pt")
return inputs
def preprocess_inputs_for_ocr(self, images):
images = list(images)
assert isinstance(images[0], np.ndarray)
return self.ocr_preprocessor(images, return_tensors="pt").pixel_values
def preprocess_inputs_for_crop_embeddings(self, images):
images = list(images)
assert isinstance(images[0], np.ndarray)
return self.crop_embedding_image_preprocessor(images, return_tensors="pt").pixel_values
def postprocess_detections_and_associations(
self,
predicted_bboxes,
predicted_class_scores,
original_image_sizes,
get_character_character_matching_scores,
get_text_character_matching_scores,
get_dialog_confidence_scores,
character_detection_threshold=0.3,
panel_detection_threshold=0.2,
text_detection_threshold=0.25,
character_character_matching_threshold=0.65,
text_character_matching_threshold=0.4,
):
assert self.config.disable_detections is False
batch_scores, batch_labels = predicted_class_scores.max(-1)
batch_scores = batch_scores.sigmoid()
batch_labels = batch_labels.long()
batch_bboxes = center_to_corners_format(predicted_bboxes)
# scale the bboxes back to the original image size
if isinstance(original_image_sizes, List):
img_h = torch.Tensor([i[0] for i in original_image_sizes])
img_w = torch.Tensor([i[1] for i in original_image_sizes])
else:
img_h, img_w = original_image_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(batch_bboxes.device)
batch_bboxes = batch_bboxes * scale_fct[:, None, :]
batch_panel_indices = self._get_indices_of_panels_to_keep(batch_scores, batch_labels, batch_bboxes, panel_detection_threshold)
batch_character_indices = self._get_indices_of_characters_to_keep(batch_scores, batch_labels, batch_bboxes, character_detection_threshold)
batch_text_indices = self._get_indices_of_texts_to_keep(batch_scores, batch_labels, batch_bboxes, text_detection_threshold)
batch_character_character_matching_scores = get_character_character_matching_scores(batch_character_indices, batch_bboxes)
batch_text_character_matching_scores = get_text_character_matching_scores(batch_text_indices, batch_character_indices)
batch_dialog_confidence_scores = get_dialog_confidence_scores(batch_text_indices)
# sort panels and texts in the reading order
for batch_index in range(len(batch_scores)):
panel_bboxes = batch_bboxes[batch_index][batch_panel_indices[batch_index]]
panel_scores = batch_scores[batch_index][batch_panel_indices[batch_index]]
text_bboxes = batch_bboxes[batch_index][batch_text_indices[batch_index]]
text_scores = batch_scores[batch_index][batch_text_indices[batch_index]]
sorted_panel_indices = sort_panels(panel_bboxes)
batch_bboxes[batch_index][batch_panel_indices[batch_index]] = panel_bboxes[sorted_panel_indices]
batch_scores[batch_index][batch_panel_indices[batch_index]] = panel_scores[sorted_panel_indices]
sorted_panels = batch_bboxes[batch_index][batch_panel_indices[batch_index]]
sorted_text_indices = sort_text_boxes_in_reading_order(text_bboxes, sorted_panels)
batch_bboxes[batch_index][batch_text_indices[batch_index]] = text_bboxes[sorted_text_indices]
batch_scores[batch_index][batch_text_indices[batch_index]] = text_scores[sorted_text_indices]
batch_text_character_matching_scores[batch_index] = batch_text_character_matching_scores[batch_index][sorted_text_indices]
batch_dialog_confidence_scores[batch_index] = batch_dialog_confidence_scores[batch_index][sorted_text_indices]
results = []
for batch_index in range(len(batch_scores)):
panel_bboxes = batch_bboxes[batch_index][batch_panel_indices[batch_index]]
panel_scores = batch_scores[batch_index][batch_panel_indices[batch_index]]
text_bboxes = batch_bboxes[batch_index][batch_text_indices[batch_index]]
text_scores = batch_scores[batch_index][batch_text_indices[batch_index]]
character_bboxes = batch_bboxes[batch_index][batch_character_indices[batch_index]]
character_scores = batch_scores[batch_index][batch_character_indices[batch_index]]
char_i, char_j = torch.where(batch_character_character_matching_scores[batch_index] > character_character_matching_threshold)
character_character_associations = torch.stack([char_i, char_j], dim=1)
text_boxes_to_match = batch_dialog_confidence_scores[batch_index] > text_character_matching_threshold
if 0 in batch_text_character_matching_scores[batch_index].shape:
text_character_associations = torch.zeros((0, 2), dtype=torch.long)
else:
most_likely_speaker_for_each_text = torch.argmax(batch_text_character_matching_scores[batch_index], dim=1)[text_boxes_to_match]
text_indices = torch.arange(len(text_bboxes)).type_as(most_likely_speaker_for_each_text)[text_boxes_to_match]
text_character_associations = torch.stack([text_indices, most_likely_speaker_for_each_text], dim=1)
character_ufds = UnionFind.from_adj_matrix(
batch_character_character_matching_scores[batch_index] > character_character_matching_threshold
)
results.append({
"panels": panel_bboxes.tolist(),
"panel_scores": panel_scores.tolist(),
"texts": text_bboxes.tolist(),
"text_scores": text_scores.tolist(),
"characters": character_bboxes.tolist(),
"character_scores": character_scores.tolist(),
"character_character_associations": character_character_associations.tolist(),
"text_character_associations": text_character_associations.tolist(),
"character_cluster_labels": character_ufds.get_labels_for_connected_components(),
"dialog_confidences": batch_dialog_confidence_scores[batch_index].tolist(),
})
return results
def postprocess_ocr_tokens(self, generated_ids, skip_special_tokens=True):
return self.ocr_preprocessor.batch_decode(generated_ids, skip_special_tokens=skip_special_tokens)
def crop_image(self, image, bboxes):
crops_for_image = []
for bbox in bboxes:
x1, y1, x2, y2 = bbox
# fix the bounding box in case it is out of bounds or too small
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
x1, y1, x2, y2 = min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2) # just incase
x1, y1 = max(0, x1), max(0, y1)
x1, y1 = min(image.shape[1], x1), min(image.shape[0], y1)
x2, y2 = max(0, x2), max(0, y2)
x2, y2 = min(image.shape[1], x2), min(image.shape[0], y2)
if x2 - x1 < 10:
if image.shape[1] - x1 > 10:
x2 = x1 + 10
else:
x1 = x2 - 10
if y2 - y1 < 10:
if image.shape[0] - y1 > 10:
y2 = y1 + 10
else:
y1 = y2 - 10
crop = image[y1:y2, x1:x2]
crops_for_image.append(crop)
return crops_for_image
def _get_indices_of_characters_to_keep(self, batch_scores, batch_labels, batch_bboxes, character_detection_threshold):
indices_of_characters_to_keep = []
for scores, labels, _ in zip(batch_scores, batch_labels, batch_bboxes):
indices = torch.where((labels == 0) & (scores > character_detection_threshold))[0]
indices_of_characters_to_keep.append(indices)
return indices_of_characters_to_keep
def _get_indices_of_panels_to_keep(self, batch_scores, batch_labels, batch_bboxes, panel_detection_threshold):
indices_of_panels_to_keep = []
for scores, labels, bboxes in zip(batch_scores, batch_labels, batch_bboxes):
indices = torch.where(labels == 2)[0]
bboxes = bboxes[indices]
scores = scores[indices]
labels = labels[indices]
if len(indices) == 0:
indices_of_panels_to_keep.append([])
continue
scores, labels, indices, bboxes = zip(*sorted(zip(scores, labels, indices, bboxes), reverse=True))
panels_to_keep = []
union_of_panels_so_far = box(0, 0, 0, 0)
for ps, pb, pl, pi in zip(scores, bboxes, labels, indices):
panel_polygon = box(pb[0], pb[1], pb[2], pb[3])
if ps < panel_detection_threshold:
continue
if union_of_panels_so_far.intersection(panel_polygon).area / panel_polygon.area > 0.5:
continue
panels_to_keep.append((ps, pl, pb, pi))
union_of_panels_so_far = union_of_panels_so_far.union(panel_polygon)
indices_of_panels_to_keep.append([p[3].item() for p in panels_to_keep])
return indices_of_panels_to_keep
def _get_indices_of_texts_to_keep(self, batch_scores, batch_labels, batch_bboxes, text_detection_threshold):
indices_of_texts_to_keep = []
for scores, labels, bboxes in zip(batch_scores, batch_labels, batch_bboxes):
indices = torch.where((labels == 1) & (scores > text_detection_threshold))[0]
bboxes = bboxes[indices]
scores = scores[indices]
labels = labels[indices]
if len(indices) == 0:
indices_of_texts_to_keep.append([])
continue
scores, labels, indices, bboxes = zip(*sorted(zip(scores, labels, indices, bboxes), reverse=True))
texts_to_keep = []
texts_to_keep_as_shapely_objects = []
for ts, tb, tl, ti in zip(scores, bboxes, labels, indices):
text_polygon = box(tb[0], tb[1], tb[2], tb[3])
should_append = True
for t in texts_to_keep_as_shapely_objects:
if t.intersection(text_polygon).area / t.union(text_polygon).area > 0.5:
should_append = False
break
if should_append:
texts_to_keep.append((ts, tl, tb, ti))
texts_to_keep_as_shapely_objects.append(text_polygon)
indices_of_texts_to_keep.append([t[3].item() for t in texts_to_keep])
return indices_of_texts_to_keep
def _convert_annotations_to_coco_format(self, annotations):
if annotations is None:
return None
self._verify_annotations_are_in_correct_format(annotations)
coco_annotations = []
for annotation in annotations:
coco_annotation = {
"image_id": annotation["image_id"],
"annotations": [],
}
for bbox, label in zip(annotation["bboxes_as_x1y1x2y2"], annotation["labels"]):
coco_annotation["annotations"].append({
"bbox": x1y1x2y2_to_xywh(bbox),
"category_id": label,
"area": (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]),
})
coco_annotations.append(coco_annotation)
return coco_annotations
def _verify_annotations_are_in_correct_format(self, annotations):
error_msg = """
Annotations must be in the following format:
[
{
"image_id": 0,
"bboxes_as_x1y1x2y2": [[0, 0, 10, 10], [10, 10, 20, 20], [20, 20, 30, 30]],
"labels": [0, 1, 2],
},
...
]
Labels: 0 for characters, 1 for text, 2 for panels.
"""
if annotations is None:
return
if not isinstance(annotations, List) and not isinstance(annotations, tuple):
raise ValueError(
f"{error_msg} Expected a List/Tuple, found {type(annotations)}."
)
if len(annotations) == 0:
return
if not isinstance(annotations[0], dict):
raise ValueError(
f"{error_msg} Expected a List[Dict], found {type(annotations[0])}."
)
if "image_id" not in annotations[0]:
raise ValueError(
f"{error_msg} Dict must contain 'image_id'."
)
if "bboxes_as_x1y1x2y2" not in annotations[0]:
raise ValueError(
f"{error_msg} Dict must contain 'bboxes_as_x1y1x2y2'."
)
if "labels" not in annotations[0]:
raise ValueError(
f"{error_msg} Dict must contain 'labels'."
)
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