Spaces:
Running
on
T4
Running
on
T4
File size: 6,604 Bytes
5ebeb73 c60ebd1 5ebeb73 76f8319 5ebeb73 c9a1c2d 5ebeb73 4bb1cba 5ebeb73 c60ebd1 52b2b0b c60ebd1 5ebeb73 c60ebd1 5ebeb73 c60ebd1 5ebeb73 c60ebd1 5ebeb73 c60ebd1 5ebeb73 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 |
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()
|