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from typing import Protocol, Tuple
import gradio as gr
import mmcv
import numpy as np
import torch
from transformers import AutoImageProcessor, TrOCRProcessor, VisionEncoderDecoderModel
from src.htr_pipeline.models import HtrModels
from src.htr_pipeline.utils.filter_segmask import FilterSegMask
from src.htr_pipeline.utils.helper import timer_func
from src.htr_pipeline.utils.order_of_object import OrderObject
from src.htr_pipeline.utils.preprocess_img import Preprocess
from src.htr_pipeline.utils.process_segmask import SegMaskHelper
class Inferencer:
def __init__(self, local_run=False):
htr_models = HtrModels(local_run)
self.seg_model = htr_models.load_region_model()
self.line_model = htr_models.load_line_model()
self.htr_model_inferencer = htr_models.load_htr_model()
self.process_seg_mask = SegMaskHelper()
self.postprocess_seg_mask = FilterSegMask()
self.ordering = OrderObject()
self.preprocess_img = Preprocess()
@timer_func
def predict_regions(self, input_image, pred_score_threshold=0.5, containments_threshold=0.5, visualize=True):
input_image = self.preprocess_img.binarize_img(input_image)
image = mmcv.imread(input_image)
result = self.seg_model(image, return_datasample=True)
result_pred = result["predictions"][0]
filtered_result_pred = self.postprocess_seg_mask.filter_on_pred_threshold(
result_pred, pred_score_threshold=pred_score_threshold
)
if len(filtered_result_pred.pred_instances.masks) == 0:
raise gr.Error("No Regions were predicted by the model")
else:
result_align = self.process_seg_mask.align_masks_with_image(filtered_result_pred, image)
result_clean = self.postprocess_seg_mask.remove_overlapping_masks(
predicted_mask=result_align, containments_threshold=containments_threshold
)
if visualize:
result_viz = self.seg_model.visualize(
inputs=[image], preds=[result_clean], return_vis=True, no_save_vis=True
)[0]
else:
result_viz = None
regions_cropped, polygons = self.process_seg_mask.crop_masks(result_clean, image)
order = self.ordering.order_regions_marginalia(result_clean)
regions_cropped_ordered = [regions_cropped[i] for i in order]
polygons_ordered = [polygons[i] for i in order]
masks_ordered = [result_clean.pred_instances.masks[i] for i in order]
return result_viz, regions_cropped_ordered, polygons_ordered, masks_ordered
@timer_func
def predict_lines(
self,
image,
pred_score_threshold=0.5,
containments_threshold=0.5,
line_spacing_factor=0.5,
visualize=True,
custom_track=True,
):
result_tl = self.line_model(image, return_datasample=True)
result_tl_pred = result_tl["predictions"][0]
filtered_result_tl_pred = self.postprocess_seg_mask.filter_on_pred_threshold(
result_tl_pred, pred_score_threshold=pred_score_threshold
)
if len(filtered_result_tl_pred.pred_instances.masks) == 0 and custom_track:
raise gr.Error("No Lines were predicted by the model")
elif len(filtered_result_tl_pred.pred_instances.masks) == 0 and not custom_track:
return None, None, None
else:
result_tl_align = self.process_seg_mask.align_masks_with_image(filtered_result_tl_pred, image)
result_tl_clean = self.postprocess_seg_mask.remove_overlapping_masks(
predicted_mask=result_tl_align, containments_threshold=containments_threshold
)
if visualize:
result_viz = self.seg_model.visualize(
inputs=[image],
preds=[result_tl_clean],
return_vis=True,
no_save_vis=True,
)[0]
else:
result_viz = None
lines_cropped, lines_polygons = self.process_seg_mask.crop_masks(result_tl_clean, image)
ordered_indices = self.ordering.order_lines(
line_image=result_tl_clean, line_spacing_factor=line_spacing_factor
)
lines_cropped_ordered = [lines_cropped[i] for i in ordered_indices]
lines_polygons_ordered = [lines_polygons[i] for i in ordered_indices]
return result_viz, lines_cropped_ordered, lines_polygons_ordered
@timer_func
def transcribe(self, line_cropped):
result_rec = self.htr_model_inferencer(line_cropped)
return result_rec["predictions"][0]["text"], round(result_rec["predictions"][0]["scores"], 4)
@timer_func
def transcribe_different_model(self, image, htr_tool_transcriber_model_dropdown):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if htr_tool_transcriber_model_dropdown == "pstroe/bullinger-general-model":
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
image_processor = AutoImageProcessor.from_pretrained("pstroe/bullinger-general-model")
model = VisionEncoderDecoderModel.from_pretrained("pstroe/bullinger-general-model")
pixel_values = image_processor(image, return_tensors="pt").pixel_values.to(device)
else:
processor = TrOCRProcessor.from_pretrained(htr_tool_transcriber_model_dropdown)
model = VisionEncoderDecoderModel.from_pretrained(htr_tool_transcriber_model_dropdown)
pixel_values = processor(image, return_tensors="pt").pixel_values.to(device)
model.to(device)
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_text, 1.0
class InferencerInterface(Protocol):
def predict_regions(
self,
image: np.array,
pred_score_threshold: float,
containments_threshold: float,
visualize: bool = False,
) -> Tuple:
...
def predict_lines(
self,
text_region: np.array,
pred_score_threshold: float,
containments_threshold: float,
visualize: bool = False,
custom_track: bool = False,
) -> Tuple:
...
def transcribe(
self,
line: np.array,
) -> Tuple[str, float]:
...
if __name__ == "__main__":
prediction_model = Inferencer()
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