mirror of
https://github.com/yuruotong1/autoMate.git
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540 lines
24 KiB
Python
540 lines
24 KiB
Python
# # from ultralytics import YOLO
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# import os
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# import io
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# import base64
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# import time
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# from PIL import Image, ImageDraw, ImageFont
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# import json
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# import requests
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# # utility function
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# import os
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# from openai import AzureOpenAI
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# import json
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# import sys
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# import os
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# import cv2
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# import numpy as np
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# # %matplotlib inline
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# from matplotlib import pyplot as plt
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# import easyocr
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# from paddleocr import PaddleOCR
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# reader = easyocr.Reader(['en', 'ch_sim'])
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# paddle_ocr = PaddleOCR(
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# lang='ch', # other lang also available
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# use_angle_cls=False,
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# use_gpu=False, # using cuda will conflict with pytorch in the same process
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# show_log=False,
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# max_batch_size=1024,
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# use_dilation=True, # improves accuracy
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# det_db_score_mode='slow', # improves accuracy
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# rec_batch_num=1024)
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# import time
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# import base64
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# import os
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# import ast
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# import torch
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# from typing import Tuple, List, Union
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# from torchvision.ops import box_convert
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# import re
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# from torchvision.transforms import ToPILImage
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# import supervision as sv
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# import torchvision.transforms as T
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# from util.box_annotator import BoxAnnotator
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# def get_caption_model_processor(model_name, model_name_or_path="Salesforce/blip2-opt-2.7b", device=None):
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# if not device:
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# device = "cuda" if torch.cuda.is_available() else "cpu"
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# if model_name == "blip2":
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# from transformers import Blip2Processor, Blip2ForConditionalGeneration
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# processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
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# if device == 'cpu':
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# model = Blip2ForConditionalGeneration.from_pretrained(
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# model_name_or_path, device_map=None, torch_dtype=torch.float32
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# )
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# else:
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# model = Blip2ForConditionalGeneration.from_pretrained(
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# model_name_or_path, device_map=None, torch_dtype=torch.float16
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# ).to(device)
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# elif model_name == "florence2":
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# from transformers import AutoProcessor, AutoModelForCausalLM
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# processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True)
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# if device == 'cpu':
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# model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float32, trust_remote_code=True)
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# else:
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# model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, trust_remote_code=True).to(device)
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# return {'model': model.to(device), 'processor': processor}
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# def get_yolo_model(model_path):
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# from ultralytics import YOLO
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# # Load the model.
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# model = YOLO(model_path)
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# return model
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# @torch.inference_mode()
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# def get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=None, batch_size=128):
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# # Number of samples per batch, --> 128 roughly takes 4 GB of GPU memory for florence v2 model
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# to_pil = ToPILImage()
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# if starting_idx:
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# non_ocr_boxes = filtered_boxes[starting_idx:]
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# else:
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# non_ocr_boxes = filtered_boxes
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# croped_pil_image = []
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# for i, coord in enumerate(non_ocr_boxes):
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# try:
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# xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1])
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# ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0])
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# cropped_image = image_source[ymin:ymax, xmin:xmax, :]
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# cropped_image = cv2.resize(cropped_image, (64, 64))
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# croped_pil_image.append(to_pil(cropped_image))
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# except:
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# continue
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# model, processor = caption_model_processor['model'], caption_model_processor['processor']
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# if not prompt:
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# if 'florence' in model.config.model_type:
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# prompt = "<CAPTION>"
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# else:
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# prompt = "The image shows"
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# generated_texts = []
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# device = model.device
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# for i in range(0, len(croped_pil_image), batch_size):
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# start = time.time()
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# batch = croped_pil_image[i:i+batch_size]
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# t1 = time.time()
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# if model.device.type == 'cuda':
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# inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt", do_resize=False).to(device=device, dtype=torch.float16)
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# else:
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# inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device)
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# if 'florence' in model.config.model_type:
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# generated_ids = model.generate(input_ids=inputs["input_ids"],pixel_values=inputs["pixel_values"],max_new_tokens=20,num_beams=1, do_sample=False)
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# else:
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# generated_ids = model.generate(**inputs, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=True, num_return_sequences=1) # temperature=0.01, do_sample=True,
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# generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
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# generated_text = [gen.strip() for gen in generated_text]
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# generated_texts.extend(generated_text)
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# return generated_texts
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# def get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor):
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# to_pil = ToPILImage()
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# if ocr_bbox:
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# non_ocr_boxes = filtered_boxes[len(ocr_bbox):]
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# else:
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# non_ocr_boxes = filtered_boxes
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# croped_pil_image = []
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# for i, coord in enumerate(non_ocr_boxes):
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# xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1])
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# ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0])
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# cropped_image = image_source[ymin:ymax, xmin:xmax, :]
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# croped_pil_image.append(to_pil(cropped_image))
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# model, processor = caption_model_processor['model'], caption_model_processor['processor']
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# device = model.device
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# messages = [{"role": "user", "content": "<|image_1|>\ndescribe the icon in one sentence"}]
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# prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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# batch_size = 5 # Number of samples per batch
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# generated_texts = []
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# for i in range(0, len(croped_pil_image), batch_size):
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# images = croped_pil_image[i:i+batch_size]
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# image_inputs = [processor.image_processor(x, return_tensors="pt") for x in images]
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# inputs ={'input_ids': [], 'attention_mask': [], 'pixel_values': [], 'image_sizes': []}
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# texts = [prompt] * len(images)
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# for i, txt in enumerate(texts):
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# input = processor._convert_images_texts_to_inputs(image_inputs[i], txt, return_tensors="pt")
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# inputs['input_ids'].append(input['input_ids'])
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# inputs['attention_mask'].append(input['attention_mask'])
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# inputs['pixel_values'].append(input['pixel_values'])
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# inputs['image_sizes'].append(input['image_sizes'])
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# max_len = max([x.shape[1] for x in inputs['input_ids']])
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# for i, v in enumerate(inputs['input_ids']):
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# inputs['input_ids'][i] = torch.cat([processor.tokenizer.pad_token_id * torch.ones(1, max_len - v.shape[1], dtype=torch.long), v], dim=1)
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# inputs['attention_mask'][i] = torch.cat([torch.zeros(1, max_len - v.shape[1], dtype=torch.long), inputs['attention_mask'][i]], dim=1)
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# inputs_cat = {k: torch.concatenate(v).to(device) for k, v in inputs.items()}
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# generation_args = {
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# "max_new_tokens": 25,
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# "temperature": 0.01,
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# "do_sample": False,
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# }
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# generate_ids = model.generate(**inputs_cat, eos_token_id=processor.tokenizer.eos_token_id, **generation_args)
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# # # remove input tokens
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# generate_ids = generate_ids[:, inputs_cat['input_ids'].shape[1]:]
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# response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
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# response = [res.strip('\n').strip() for res in response]
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# generated_texts.extend(response)
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# return generated_texts
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# def remove_overlap(boxes, iou_threshold, ocr_bbox=None):
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# assert ocr_bbox is None or isinstance(ocr_bbox, List)
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# def box_area(box):
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# return (box[2] - box[0]) * (box[3] - box[1])
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# def intersection_area(box1, box2):
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# x1 = max(box1[0], box2[0])
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# y1 = max(box1[1], box2[1])
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# x2 = min(box1[2], box2[2])
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# y2 = min(box1[3], box2[3])
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# return max(0, x2 - x1) * max(0, y2 - y1)
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# def IoU(box1, box2):
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# intersection = intersection_area(box1, box2)
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# union = box_area(box1) + box_area(box2) - intersection + 1e-6
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# if box_area(box1) > 0 and box_area(box2) > 0:
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# ratio1 = intersection / box_area(box1)
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# ratio2 = intersection / box_area(box2)
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# else:
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# ratio1, ratio2 = 0, 0
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# return max(intersection / union, ratio1, ratio2)
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# def is_inside(box1, box2):
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# # return box1[0] >= box2[0] and box1[1] >= box2[1] and box1[2] <= box2[2] and box1[3] <= box2[3]
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# intersection = intersection_area(box1, box2)
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# ratio1 = intersection / box_area(box1)
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# return ratio1 > 0.95
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# boxes = boxes.tolist()
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# filtered_boxes = []
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# if ocr_bbox:
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# filtered_boxes.extend(ocr_bbox)
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# # print('ocr_bbox!!!', ocr_bbox)
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# for i, box1 in enumerate(boxes):
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# # if not any(IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2) for j, box2 in enumerate(boxes) if i != j):
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# is_valid_box = True
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# for j, box2 in enumerate(boxes):
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# # keep the smaller box
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# if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2):
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# is_valid_box = False
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# break
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# if is_valid_box:
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# # add the following 2 lines to include ocr bbox
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# if ocr_bbox:
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# # only add the box if it does not overlap with any ocr bbox
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# if not any(IoU(box1, box3) > iou_threshold and not is_inside(box1, box3) for k, box3 in enumerate(ocr_bbox)):
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# filtered_boxes.append(box1)
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# else:
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# filtered_boxes.append(box1)
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# return torch.tensor(filtered_boxes)
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# def remove_overlap_new(boxes, iou_threshold, ocr_bbox=None):
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# '''
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# ocr_bbox format: [{'type': 'text', 'bbox':[x,y], 'interactivity':False, 'content':str }, ...]
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# boxes format: [{'type': 'icon', 'bbox':[x,y], 'interactivity':True, 'content':None }, ...]
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# '''
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# assert ocr_bbox is None or isinstance(ocr_bbox, List)
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# def box_area(box):
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# return (box[2] - box[0]) * (box[3] - box[1])
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# def intersection_area(box1, box2):
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# x1 = max(box1[0], box2[0])
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# y1 = max(box1[1], box2[1])
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# x2 = min(box1[2], box2[2])
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# y2 = min(box1[3], box2[3])
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# return max(0, x2 - x1) * max(0, y2 - y1)
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# def IoU(box1, box2):
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# intersection = intersection_area(box1, box2)
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# union = box_area(box1) + box_area(box2) - intersection + 1e-6
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# if box_area(box1) > 0 and box_area(box2) > 0:
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# ratio1 = intersection / box_area(box1)
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# ratio2 = intersection / box_area(box2)
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# else:
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# ratio1, ratio2 = 0, 0
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# return max(intersection / union, ratio1, ratio2)
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# def is_inside(box1, box2):
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# # return box1[0] >= box2[0] and box1[1] >= box2[1] and box1[2] <= box2[2] and box1[3] <= box2[3]
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# intersection = intersection_area(box1, box2)
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# ratio1 = intersection / box_area(box1)
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# return ratio1 > 0.80
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# # boxes = boxes.tolist()
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# filtered_boxes = []
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# if ocr_bbox:
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# filtered_boxes.extend(ocr_bbox)
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# # print('ocr_bbox!!!', ocr_bbox)
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# for i, box1_elem in enumerate(boxes):
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# box1 = box1_elem['bbox']
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# is_valid_box = True
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# for j, box2_elem in enumerate(boxes):
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# # keep the smaller box
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# box2 = box2_elem['bbox']
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# if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2):
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# is_valid_box = False
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# break
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# if is_valid_box:
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# if ocr_bbox:
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# # keep yolo boxes + prioritize ocr label
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# box_added = False
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# ocr_labels = ''
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# for box3_elem in ocr_bbox:
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# if not box_added:
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# box3 = box3_elem['bbox']
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# if is_inside(box3, box1): # ocr inside icon
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# # box_added = True
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# # delete the box3_elem from ocr_bbox
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# try:
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# # gather all ocr labels
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# ocr_labels += box3_elem['content'] + ' '
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# filtered_boxes.remove(box3_elem)
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# except:
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# continue
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# # break
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# elif is_inside(box1, box3): # icon inside ocr, don't added this icon box, no need to check other ocr bbox bc no overlap between ocr bbox, icon can only be in one ocr box
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# box_added = True
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# break
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# else:
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# continue
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# if not box_added:
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# if ocr_labels:
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# filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': ocr_labels, 'source':'box_yolo_content_ocr'})
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# else:
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# filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None, 'source':'box_yolo_content_yolo'})
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# else:
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# filtered_boxes.append(box1)
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# return filtered_boxes # torch.tensor(filtered_boxes)
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# def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
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# transform = T.Compose(
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# [
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# T.RandomResize([800], max_size=1333),
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# T.ToTensor(),
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# T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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# ]
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# )
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# image_source = Image.open(image_path).convert("RGB")
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# image = np.asarray(image_source)
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# image_transformed, _ = transform(image_source, None)
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# return image, image_transformed
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# def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str], text_scale: float,
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# text_padding=5, text_thickness=2, thickness=3) -> np.ndarray:
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# """
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# This function annotates an image with bounding boxes and labels.
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# Parameters:
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# image_source (np.ndarray): The source image to be annotated.
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# boxes (torch.Tensor): A tensor containing bounding box coordinates. in cxcywh format, pixel scale
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# logits (torch.Tensor): A tensor containing confidence scores for each bounding box.
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# phrases (List[str]): A list of labels for each bounding box.
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# text_scale (float): The scale of the text to be displayed. 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web
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# Returns:
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# np.ndarray: The annotated image.
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# """
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# h, w, _ = image_source.shape
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# boxes = boxes * torch.Tensor([w, h, w, h])
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# xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
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# xywh = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xywh").numpy()
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# detections = sv.Detections(xyxy=xyxy)
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# labels = [f"{phrase}" for phrase in range(boxes.shape[0])]
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# box_annotator = BoxAnnotator(text_scale=text_scale, text_padding=text_padding,text_thickness=text_thickness,thickness=thickness) # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web
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# annotated_frame = image_source.copy()
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# annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels, image_size=(w,h))
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# label_coordinates = {f"{phrase}": v for phrase, v in zip(phrases, xywh)}
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# return annotated_frame, label_coordinates
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# def predict(model, image, caption, box_threshold, text_threshold):
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# """ Use huggingface model to replace the original model
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# """
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# model, processor = model['model'], model['processor']
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# device = model.device
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# inputs = processor(images=image, text=caption, return_tensors="pt").to(device)
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# with torch.no_grad():
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# outputs = model(**inputs)
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# results = processor.post_process_grounded_object_detection(
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# outputs,
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# inputs.input_ids,
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# box_threshold=box_threshold, # 0.4,
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# text_threshold=text_threshold, # 0.3,
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# target_sizes=[image.size[::-1]]
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# )[0]
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# boxes, logits, phrases = results["boxes"], results["scores"], results["labels"]
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# return boxes, logits, phrases
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# def predict_yolo(model, image, box_threshold, imgsz, scale_img, iou_threshold=0.7):
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# """ Use huggingface model to replace the original model
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# """
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# # model = model['model']
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# if scale_img:
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# result = model.predict(
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# source=image,
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# conf=box_threshold,
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# imgsz=imgsz,
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# iou=iou_threshold, # default 0.7
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# )
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# else:
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# result = model.predict(
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# source=image,
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# conf=box_threshold,
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# iou=iou_threshold, # default 0.7
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# )
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# boxes = result[0].boxes.xyxy#.tolist() # in pixel space
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# conf = result[0].boxes.conf
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# phrases = [str(i) for i in range(len(boxes))]
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# return boxes, conf, phrases
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# def int_box_area(box, w, h):
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# x1, y1, x2, y2 = box
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# int_box = [int(x1*w), int(y1*h), int(x2*w), int(y2*h)]
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# area = (int_box[2] - int_box[0]) * (int_box[3] - int_box[1])
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# return area
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|
|
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# def get_som_labeled_img(image_source: Union[str, Image.Image], model=None, BOX_TRESHOLD=0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=128):
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# """Process either an image path or Image object
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|
|
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# Args:
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# image_source: Either a file path (str) or PIL Image object
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# ...
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|
# """
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# if isinstance(image_source, str):
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# image_source = Image.open(image_source)
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# image_source = image_source.convert("RGB") # for CLIP
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# w, h = image_source.size
|
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# if not imgsz:
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# imgsz = (h, w)
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|
# # print('image size:', w, h)
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|
# xyxy, logits, phrases = predict_yolo(model=model, image=image_source, box_threshold=BOX_TRESHOLD, imgsz=imgsz, scale_img=scale_img, iou_threshold=0.1)
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# xyxy = xyxy / torch.Tensor([w, h, w, h]).to(xyxy.device)
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# image_source = np.asarray(image_source)
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|
# phrases = [str(i) for i in range(len(phrases))]
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|
|
|
# # annotate the image with labels
|
|
# if ocr_bbox:
|
|
# ocr_bbox = torch.tensor(ocr_bbox) / torch.Tensor([w, h, w, h])
|
|
# ocr_bbox=ocr_bbox.tolist()
|
|
# else:
|
|
# print('no ocr bbox!!!')
|
|
# ocr_bbox = None
|
|
|
|
# ocr_bbox_elem = [{'type': 'text', 'bbox':box, 'interactivity':False, 'content':txt, 'source': 'box_ocr_content_ocr'} for box, txt in zip(ocr_bbox, ocr_text) if int_box_area(box, w, h) > 0]
|
|
# xyxy_elem = [{'type': 'icon', 'bbox':box, 'interactivity':True, 'content':None} for box in xyxy.tolist() if int_box_area(box, w, h) > 0]
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|
# filtered_boxes = remove_overlap_new(boxes=xyxy_elem, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox_elem)
|
|
|
|
# # sort the filtered_boxes so that the one with 'content': None is at the end, and get the index of the first 'content': None
|
|
# filtered_boxes_elem = sorted(filtered_boxes, key=lambda x: x['content'] is None)
|
|
# # get the index of the first 'content': None
|
|
# starting_idx = next((i for i, box in enumerate(filtered_boxes_elem) if box['content'] is None), -1)
|
|
# filtered_boxes = torch.tensor([box['bbox'] for box in filtered_boxes_elem])
|
|
# print('len(filtered_boxes):', len(filtered_boxes), starting_idx)
|
|
|
|
# # get parsed icon local semantics
|
|
# time1 = time.time()
|
|
# if use_local_semantics:
|
|
# caption_model = caption_model_processor['model']
|
|
# if 'phi3_v' in caption_model.config.model_type:
|
|
# parsed_content_icon = get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor)
|
|
# else:
|
|
# parsed_content_icon = get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=prompt,batch_size=batch_size)
|
|
# ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)]
|
|
# icon_start = len(ocr_text)
|
|
# parsed_content_icon_ls = []
|
|
# # fill the filtered_boxes_elem None content with parsed_content_icon in order
|
|
# for i, box in enumerate(filtered_boxes_elem):
|
|
# if box['content'] is None:
|
|
# box['content'] = parsed_content_icon.pop(0)
|
|
# for i, txt in enumerate(parsed_content_icon):
|
|
# parsed_content_icon_ls.append(f"Icon Box ID {str(i+icon_start)}: {txt}")
|
|
# parsed_content_merged = ocr_text + parsed_content_icon_ls
|
|
# else:
|
|
# ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)]
|
|
# parsed_content_merged = ocr_text
|
|
# print('time to get parsed content:', time.time()-time1)
|
|
|
|
# filtered_boxes = box_convert(boxes=filtered_boxes, in_fmt="xyxy", out_fmt="cxcywh")
|
|
|
|
# phrases = [i for i in range(len(filtered_boxes))]
|
|
|
|
# # draw boxes
|
|
# if draw_bbox_config:
|
|
# annotated_frame, label_coordinates = annotate(image_source=image_source, boxes=filtered_boxes, logits=logits, phrases=phrases, **draw_bbox_config)
|
|
# else:
|
|
# annotated_frame, label_coordinates = annotate(image_source=image_source, boxes=filtered_boxes, logits=logits, phrases=phrases, text_scale=text_scale, text_padding=text_padding)
|
|
|
|
# pil_img = Image.fromarray(annotated_frame)
|
|
# buffered = io.BytesIO()
|
|
# pil_img.save(buffered, format="PNG")
|
|
# encoded_image = base64.b64encode(buffered.getvalue()).decode('ascii')
|
|
# if output_coord_in_ratio:
|
|
# label_coordinates = {k: [v[0]/w, v[1]/h, v[2]/w, v[3]/h] for k, v in label_coordinates.items()}
|
|
# assert w == annotated_frame.shape[1] and h == annotated_frame.shape[0]
|
|
|
|
# return encoded_image, label_coordinates, filtered_boxes_elem
|
|
|
|
|
|
# def get_xywh(input):
|
|
# x, y, w, h = input[0][0], input[0][1], input[2][0] - input[0][0], input[2][1] - input[0][1]
|
|
# x, y, w, h = int(x), int(y), int(w), int(h)
|
|
# return x, y, w, h
|
|
|
|
# def get_xyxy(input):
|
|
# x, y, xp, yp = input[0][0], input[0][1], input[2][0], input[2][1]
|
|
# x, y, xp, yp = int(x), int(y), int(xp), int(yp)
|
|
# return x, y, xp, yp
|
|
|
|
# def get_xywh_yolo(input):
|
|
# x, y, w, h = input[0], input[1], input[2] - input[0], input[3] - input[1]
|
|
# x, y, w, h = int(x), int(y), int(w), int(h)
|
|
# return x, y, w, h
|
|
|
|
# def check_ocr_box(image_source: Union[str, Image.Image], display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False):
|
|
# if isinstance(image_source, str):
|
|
# image_source = Image.open(image_source)
|
|
# if image_source.mode == 'RGBA':
|
|
# # Convert RGBA to RGB to avoid alpha channel issues
|
|
# image_source = image_source.convert('RGB')
|
|
# image_np = np.array(image_source)
|
|
# w, h = image_source.size
|
|
# if use_paddleocr:
|
|
# if easyocr_args is None:
|
|
# text_threshold = 0.5
|
|
# else:
|
|
# text_threshold = easyocr_args['text_threshold']
|
|
# result = paddle_ocr.ocr(image_np, cls=False)[0]
|
|
# coord = [item[0] for item in result if item[1][1] > text_threshold]
|
|
# text = [item[1][0] for item in result if item[1][1] > text_threshold]
|
|
# else: # EasyOCR
|
|
# if easyocr_args is None:
|
|
# easyocr_args = {}
|
|
# result = reader.readtext(image_np, **easyocr_args)
|
|
# coord = [item[0] for item in result]
|
|
# text = [item[1] for item in result]
|
|
# if display_img:
|
|
# opencv_img = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
|
|
# bb = []
|
|
# for item in coord:
|
|
# x, y, a, b = get_xywh(item)
|
|
# bb.append((x, y, a, b))
|
|
# cv2.rectangle(opencv_img, (x, y), (x+a, y+b), (0, 255, 0), 2)
|
|
# # matplotlib expects RGB
|
|
# plt.imshow(cv2.cvtColor(opencv_img, cv2.COLOR_BGR2RGB))
|
|
# else:
|
|
# if output_bb_format == 'xywh':
|
|
# bb = [get_xywh(item) for item in coord]
|
|
# elif output_bb_format == 'xyxy':
|
|
# bb = [get_xyxy(item) for item in coord]
|
|
# return (text, bb), goal_filtering |