107 lines
4.4 KiB
Python
107 lines
4.4 KiB
Python
import torch
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from transformers import \
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TableTransformerForObjectDetection,\
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AutoImageProcessor
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from PIL import ImageDraw
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from random import randint
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class TableTransformer:
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def __init__(self,
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rec_mdlnm="microsoft/table-transformer-structure-recognition"):
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"""
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If you have trouble downloading HuggingFace models, -_^ this might help!!
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For Linux:
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export HF_ENDPOINT=https://hf-mirror.com
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For Windows:
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Good luck
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^_-
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"""
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self.rec_img_pro = AutoImageProcessor.from_pretrained(rec_mdlnm)
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self.rec_mdl = TableTransformerForObjectDetection.from_pretrained(
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rec_mdlnm)
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if torch.cuda.is_available():
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self.rec_mdl.cuda()
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self.batch_size = 1 # batch_size
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def __friendly(self, batch_res, id2label):
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res = []
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for r in batch_res:
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feas = []
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for score, label, box in zip(r["scores"], r["labels"], r["boxes"]):
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if label.item() == 0:
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continue
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box = [round(x, 2) for x in box.tolist()]
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feas.append({
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"top": box[1], "bottom": box[-1],
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"x0": box[0], "x1": box[2],
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"score": score.item(),
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"label": id2label[label.item()]
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})
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wids = [f["x1"] - f["x0"]
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for f in feas if f["label"].find("row") > 0]
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if wids:
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mw = max(wids) / 2
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for f in feas:
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if f["label"].find("row") > 0 and f["x1"] - f["x0"] < mw:
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f["x1"] += mw
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res.append(feas)
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return res
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def __draw(self, bres, imgs, id2label):
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for i, (img, r) in enumerate(zip(imgs, bres)):
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draw = ImageDraw.Draw(img, "RGB")
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for score, label, box in zip(r["scores"], r["labels"], r["boxes"]):
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if label.item() == 0:
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continue
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r = randint(0, 255)
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g = randint(0, 255)
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b = randint(0, 255)
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x0, y0, x1, y1 = box[0], box[1], box[2], box[-1]
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draw.rectangle((x0, y0, x1, y1), outline=(r, g, b), width=1)
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draw.text((x0, y0), id2label[label.item(
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)] + ":{:.2f}".format(score), fill=(r, g, b))
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img.save(f"./t{i}.%d.jpg" % randint(0, 1000))
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def __call__(self, images):
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res = []
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for i in range(0, len(images), self.batch_size):
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imgs = images[i: i + self.batch_size]
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inputs = self.rec_img_pro(imgs, return_tensors="pt")
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inputs = {k: inputs[k].to(self.rec_mdl.device)
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if isinstance(inputs[k], torch.Tensor)
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else inputs[k] for k in inputs.keys()}
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outputs = self.rec_mdl(**inputs)
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target_sizes = torch.tensor([img.size[::-1] for img in imgs])
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# [scores, labels, boxes}]
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with torch.no_grad():
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bres = self.rec_img_pro.post_process_object_detection(outputs,
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threshold=0.80,
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target_sizes=target_sizes)
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self.__draw(bres, imgs, self.rec_mdl.config.id2label)
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res.extend(self.__friendly(bres, self.rec_mdl.config.id2label))
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return res
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def detect(self, images):
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res = []
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for i in range(0, len(images), self.batch_size):
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imgs = images[i: i + self.batch_size]
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inputs = self.det_img_pro(imgs, return_tensors="pt")
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inputs = {k: inputs[k].to(self.det_mdl.device)
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if isinstance(inputs[k], torch.Tensor)
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else inputs[k] for k in inputs.keys()}
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outputs = self.det_mdl(**inputs)
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target_sizes = torch.tensor([img.size[::-1] for img in imgs])
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# [scores, labels, boxes}]
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with torch.no_grad():
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res.extend(self.__friendly(self.det_img_pro.post_process_object_detection(outputs,
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threshold=0.9,
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target_sizes=target_sizes),
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self.det_mdl.config.id2label
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))
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return res
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