Clean up folder structure
This commit is contained in:
Thomas Dhome Casanova (from Dev Box)
543
util/utils.py
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543
util/utils.py
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# 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'])
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paddle_ocr = PaddleOCR(
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lang='en', # 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=None):
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# Number of samples per batch, --> 256 roughly takes 23 GB of GPU memory for florence 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.name_or_path:
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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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# batch_size = 64
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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.name_or_path:
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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
|
||||
conf = result[0].boxes.conf
|
||||
phrases = [str(i) for i in range(len(boxes))]
|
||||
|
||||
return boxes, conf, phrases
|
||||
|
||||
def int_box_area(box, w, h):
|
||||
x1, y1, x2, y2 = box
|
||||
int_box = [int(x1*w), int(y1*h), int(x2*w), int(y2*h)]
|
||||
area = (int_box[2] - int_box[0]) * (int_box[3] - int_box[1])
|
||||
return area
|
||||
|
||||
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=64):
|
||||
"""Process either an image path or Image object
|
||||
|
||||
Args:
|
||||
image_source: Either a file path (str) or PIL Image object
|
||||
...
|
||||
"""
|
||||
if isinstance(image_source, str):
|
||||
image_source = Image.open(image_source).convert("RGB")
|
||||
|
||||
w, h = image_source.size
|
||||
if not imgsz:
|
||||
imgsz = (h, w)
|
||||
# print('image size:', w, h)
|
||||
xyxy, logits, phrases = predict_yolo(model=model, image=image_source, box_threshold=BOX_TRESHOLD, imgsz=imgsz, scale_img=scale_img, iou_threshold=0.1)
|
||||
xyxy = xyxy / torch.Tensor([w, h, w, h]).to(xyxy.device)
|
||||
image_source = np.asarray(image_source)
|
||||
phrases = [str(i) for i in range(len(phrases))]
|
||||
|
||||
# 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]
|
||||
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
|
||||
|
||||
|
||||
Reference in New Issue
Block a user