941 lines
33 KiB
Python
Executable File
941 lines
33 KiB
Python
Executable File
# Have SwinIR upsample
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# Have BLIP auto caption
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# Have CLIPSeg auto mask concept
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import gc
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import fnmatch
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import mimetypes
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import os
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import time
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import re
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import shutil
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import tarfile
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import base64
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import requests
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import concurrent.futures
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from pathlib import Path
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from typing import List, Literal, Optional, Tuple, Union
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from zipfile import ZipFile
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from PIL import ImageEnhance, ImageFilter, Image
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import random
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import cv2
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import mediapipe as mp
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import numpy as np
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import pandas as pd
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import torch
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from tqdm import tqdm
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from transformers import (
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BlipForConditionalGeneration,
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Blip2ForConditionalGeneration,
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BlipProcessor,
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Blip2Processor,
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CLIPSegForImageSegmentation,
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CLIPSegProcessor,
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Swin2SRForImageSuperResolution,
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Swin2SRImageProcessor,
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)
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from io_utils import download_and_prep_training_data
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import re
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import openai
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from openai import OpenAI
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from dotenv import load_dotenv
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load_dotenv()
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try:
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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client = OpenAI(api_key=OPENAI_API_KEY)
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print("OpenAI API key loaded")
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except:
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OPENAI_API_KEY = None
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client = None
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print("WARNING: Could not find OPENAI_API_KEY in .env, disabling gpt prompt generation.")
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MODEL_PATH = "./cache"
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MAX_GPT_PROMPTS = 40
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import re
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def fix_prompt(prompt: str):
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# Remove extra commas and spaces, and fix space before punctuation
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prompt = re.sub(r"\s+", " ", prompt) # Replace multiple spaces with a single space
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prompt = re.sub(r",,", ",", prompt) # Replace double commas with a single comma
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prompt = re.sub(r"\s?,\s?", ", ", prompt) # Fix spaces around commas
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prompt = re.sub(r"\s?\.\s?", ". ", prompt) # Fix spaces around periods
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return prompt.strip() # Remove leading and trailing whitespace
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def _find_files(pattern, dir="."):
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"""Return list of files matching pattern in a given directory, in absolute format.
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Unlike glob, this is case-insensitive.
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"""
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rule = re.compile(fnmatch.translate(pattern), re.IGNORECASE)
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return [os.path.join(dir, f) for f in os.listdir(dir) if rule.match(f)]
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def preprocess(
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working_directory,
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concept_mode,
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input_zip_path: Path,
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caption_text: str,
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mask_target_prompts: str,
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target_size: int,
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crop_based_on_salience: bool,
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use_face_detection_instead: bool,
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temp: float,
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left_right_flip_augmentation: bool = False,
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augment_imgs_up_to_n: int = 0,
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seed: int = 0,
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) -> Path:
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if os.path.exists(working_directory):
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print(f"working_directory {working_directory} already existed.. deleting and recreating!")
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shutil.rmtree(working_directory)
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os.makedirs(working_directory)
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# Setup directories for the training data:
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TEMP_IN_DIR = os.path.join(working_directory, "images_in")
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TEMP_OUT_DIR = os.path.join(working_directory, "images_out")
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for path in [TEMP_OUT_DIR, TEMP_IN_DIR]:
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if os.path.exists(path):
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shutil.rmtree(path)
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os.makedirs(path)
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download_and_prep_training_data(input_zip_path, TEMP_IN_DIR)
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n_training_imgs, trigger_text, segmentation_prompt, captions = load_and_save_masks_and_captions(
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concept_mode,
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files=TEMP_IN_DIR,
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output_dir=TEMP_OUT_DIR,
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seed=seed,
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caption_text=caption_text,
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mask_target_prompts=mask_target_prompts,
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target_size=target_size,
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crop_based_on_salience=crop_based_on_salience,
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use_face_detection_instead=use_face_detection_instead,
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temp=temp,
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add_lr_flips = left_right_flip_augmentation,
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augment_imgs_up_to_n = augment_imgs_up_to_n,
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)
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return Path(TEMP_OUT_DIR), n_training_imgs, trigger_text, segmentation_prompt, captions
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@torch.no_grad()
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@torch.cuda.amp.autocast()
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def swin_ir_sr(
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images: List[Image.Image],
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model_id: Literal[
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"caidas/swin2SR-classical-sr-x2-64",
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"caidas/swin2SR-classical-sr-x4-48",
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"caidas/swin2SR-realworld-sr-x4-64-bsrgan-psnr",
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] = "caidas/swin2SR-realworld-sr-x4-64-bsrgan-psnr",
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target_size: Optional[Tuple[int, int]] = None,
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device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),
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**kwargs,
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) -> List[Image.Image]:
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"""
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Upscales images using SwinIR. Returns a list of PIL images.
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If the image is already larger than the target size, it will not be upscaled
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and will be returned as is.
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"""
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model = Swin2SRForImageSuperResolution.from_pretrained(
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model_id, cache_dir=MODEL_PATH
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).to(device)
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processor = Swin2SRImageProcessor()
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out_images = []
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for image in tqdm(images):
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ori_w, ori_h = image.size
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if target_size is not None:
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if ori_w >= target_size[0] and ori_h >= target_size[1]:
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out_images.append(image)
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continue
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inputs = processor(image, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = model(**inputs)
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output = (
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outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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)
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output = np.moveaxis(output, source=0, destination=-1)
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output = (output * 255.0).round().astype(np.uint8)
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output = Image.fromarray(output)
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out_images.append(output)
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return out_images
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@torch.no_grad()
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@torch.cuda.amp.autocast()
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def clipseg_mask_generator(
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images: List[Image.Image],
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target_prompts: Union[List[str], str],
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model_id: Literal[
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"CIDAS/clipseg-rd64-refined", "CIDAS/clipseg-rd16"
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] = "CIDAS/clipseg-rd64-refined",
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device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),
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bias: float = 0.01,
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temp: float = 1.0,
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**kwargs,
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) -> List[Image.Image]:
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"""
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Returns a greyscale mask for each image, where the mask is the probability of the target prompt being present in the image
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"""
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if isinstance(target_prompts, str):
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print(
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f'Warning: only one target prompt "{target_prompts}" was given, so it will be used for all images'
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)
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target_prompts = [target_prompts] * len(images)
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if any(target_prompts):
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processor = CLIPSegProcessor.from_pretrained(model_id, cache_dir=MODEL_PATH)
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model = CLIPSegForImageSegmentation.from_pretrained(
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model_id, cache_dir=MODEL_PATH
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).to(device)
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masks = []
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for image, prompt in tqdm(zip(images, target_prompts)):
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original_size = image.size
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if prompt != "":
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inputs = processor(
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text=[prompt, ""],
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images=[image] * 2,
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padding="max_length",
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truncation=True,
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return_tensors="pt",
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).to(device)
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outputs = model(**inputs)
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logits = outputs.logits
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probs = torch.nn.functional.softmax(logits / temp, dim=0)[0]
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probs = (probs + bias).clamp_(0, 1)
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probs = 255 * probs / probs.max()
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# make mask greyscale
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mask = Image.fromarray(probs.cpu().numpy()).convert("L")
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# resize mask to original size
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mask = mask.resize(original_size)
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else:
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mask = Image.new("L", original_size, 255)
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masks.append(mask)
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return masks
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def cleanup_prompts_with_chatgpt(
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prompts,
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concept_mode, # face / object / style
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seed, # seed for chatgpt reproducibility
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verbose = True):
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if concept_mode == "object_injection":
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chat_gpt_prompt_1 = """
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I have a set of images, each containing the same concept / figure. I have the following (poor) descriptions for each image:
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"""
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chat_gpt_prompt_2 = """
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I want you to:
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1. Find a good, short name/description of the single central concept that's in all the images. This [Concept Name] might eg already be present in the descriptions above, pick the most obvious name or words that would fit in all descriptions.
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2. Insert the text "TOK, [Concept Name]" into all the descriptions above by rephrasing them where needed to naturally contain the text TOK, [Concept Name] while keeping as much of the description as possible.
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Reply by first stating the "Concept Name:", followed by an enumerated list (using "-") of all the revised "Descriptions:".
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"""
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if concept_mode == "object":
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chat_gpt_prompt_1 = """
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Analyze a set of (poor) image descriptions each featuring the same concept, figure or thing.
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Tasks:
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1. Deduce a concise, fitting name for the concept that is visually descriptive (Concept Name).
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2. Substitute the concept in each description with the placeholder "TOK", rearranging or adjusting the text where needed. Hallucinate TOK into the description if necessary (but dont mention when doing so, simply provide the final description)!
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3. Streamline each description to its core elements, ensuring clarity and mandatory inclusion of the placeholder string "TOK".
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The descriptions are:
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"""
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chat_gpt_prompt_2 = """
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Respond with the chosen "Concept Name:" followed by a list (using "-") of all the revised descriptions, each mentioning "TOK".
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"""
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elif concept_mode == "face":
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chat_gpt_prompt_1 = """
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Analyze a set of (poor) image descriptions, each featuring a person named TOK.
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Tasks:
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1. Rewrite each description, ensuring it refers only to a single person or character.
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2. Integrate "a photo of TOK" naturally into each description, rearranging or adjusting where needed.
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3. Streamline each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
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The descriptions are:
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"""
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chat_gpt_prompt_2 = """
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Respond with "Concept Name: TOK" followed by a list (using "-") of all the revised descriptions, each mentioning "a photo of TOK".
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"""
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elif concept_mode == "style":
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chat_gpt_prompt_1 = """
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Analyze a set of (poor) image descriptions, each featuring the same style named TOK.
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Tasks:
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1. Rewrite each description to focus solely on the TOK style.
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2. Integrate "in the style of TOK" naturally into each description, typically at the beginning.
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3. Summarize each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
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The descriptions are:
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"""
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chat_gpt_prompt_2 = """
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Respond with "Style Name: TOK" followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
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"""
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final_chatgpt_prompt = chat_gpt_prompt_1 + "\n- " + "\n- ".join(prompts) + "\n\n" + chat_gpt_prompt_2
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print("Final chatgpt prompt:")
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print(final_chatgpt_prompt)
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print("--------------------------")
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print(f"Calling chatgpt with seed {seed}...")
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response = client.chat.completions.create(
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model="gpt-4-1106-preview",
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seed=seed,
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": final_chatgpt_prompt},
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])
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gpt_completion = response.choices[0].message.content
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if verbose: # pretty print the full response json:
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print("----- GPT response: -----")
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print(gpt_completion)
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print("--------------------------")
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# extract the final rephrased prompts from the response:
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prompts = []
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for line in gpt_completion.split("\n"):
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if line.startswith("-"):
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prompts.append(line[2:])
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gpt_concept_name = extract_gpt_concept_name(gpt_completion, concept_mode)
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trigger_text = "TOK, " + gpt_concept_name if concept_mode == 'object_injection' else "TOK"
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if concept_mode == 'style':
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trigger_text = ", in the style of TOK"
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gpt_concept_name = "" # Disables segmentation for style (use full img)
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return prompts, gpt_concept_name, trigger_text
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def extract_gpt_concept_name(gpt_completion, concept_mode):
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"""
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Extracts the concept name from the GPT completion based on the concept mode.
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"""
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concept_name = ""
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prefix = ""
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if concept_mode in ['face', 'style']:
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concept_name = concept_mode
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prefix = "Style Name:" if concept_mode == 'style' else ""
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elif concept_mode in ['object_injection', 'object']:
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prefix = "Concept Name:"
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concept_mode = 'object_injection'
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if prefix:
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for line in gpt_completion.split("\n"):
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if line.startswith(prefix):
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concept_name = line[len(prefix):].strip()
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break
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return concept_name
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def post_process_captions(captions, text, concept_mode, job_seed):
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text = text.strip()
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print(f"Input captioning text: {text}")
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if len(captions) > 3 and len(captions) < MAX_GPT_PROMPTS and not text and client:
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retry_count = 0
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while retry_count < 10:
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try:
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gpt_captions, gpt_concept_name, trigger_text = cleanup_prompts_with_chatgpt(captions, concept_mode, job_seed + retry_count)
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n_toks = sum("TOK" in caption for caption in gpt_captions)
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if n_toks > int(0.8 * len(captions)) and (len(gpt_captions) == len(captions)):
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# Ensure every caption contains "TOK"
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gpt_captions = ["TOK, " + caption if "TOK" not in caption else caption for caption in gpt_captions]
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captions = gpt_captions
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break
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else:
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retry_count += 1
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except Exception as e:
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retry_count += 1
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print(f"An error occurred after try {retry_count}: {e}")
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time.sleep(1)
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else:
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gpt_concept_name, trigger_text = None, "TOK"
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else:
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# simple concat of trigger text with rest of prompt:
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if len(text) == 0:
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print("WARNING: no captioning text was given and we're not doing chatgpt cleanup...")
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print("Concept mode: ", concept_mode)
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if concept_mode == "style":
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trigger_text = "in the style of TOK, "
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captions = [trigger_text + caption for caption in captions]
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else:
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trigger_text = "a photo of TOK, "
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captions = [trigger_text + caption for caption in captions]
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else:
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trigger_text = text
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captions = [trigger_text + ", " + caption for caption in captions]
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gpt_concept_name = None
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captions = [fix_prompt(caption) for caption in captions]
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return captions, trigger_text, gpt_concept_name
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def blip_caption_dataset(
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images: List[Image.Image],
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captions: List[str],
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model_id: Literal[
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"Salesforce/blip-image-captioning-large",
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"Salesforce/blip-image-captioning-base",
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"Salesforce/blip2-opt-2.7b",
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] = "Salesforce/blip-image-captioning-large"
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):
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print(f"Using model {model_id} for image captioning...")
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device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
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if "blip2" in model_id:
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processor = Blip2Processor.from_pretrained(model_id, cache_dir=MODEL_PATH)
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model = Blip2ForConditionalGeneration.from_pretrained(
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model_id, cache_dir=MODEL_PATH, torch_dtype=torch.float16
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).to(device)
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else:
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processor = BlipProcessor.from_pretrained(model_id, cache_dir=MODEL_PATH)
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model = BlipForConditionalGeneration.from_pretrained(
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model_id, cache_dir=MODEL_PATH, torch_dtype=torch.float16
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).to(device)
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|
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for i, image in enumerate(tqdm(images)):
|
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if captions[i] is None:
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inputs = processor(image, return_tensors="pt").to(device, torch.float16)
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out = model.generate(**inputs, max_length=100, do_sample=True, top_k=40, temperature=0.65)
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captions[i] = processor.decode(out[0], skip_special_tokens=True)
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return captions
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|
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def encode_image(image_path):
|
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode('utf-8')
|
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|
|
import uuid
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def prep_img_for_gpt_api(pil_img, max_size=(512, 512)):
|
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# create a temporary file to save the resized image:
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resized_img = pil_img.copy()
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resized_img.thumbnail(max_size, Image.Resampling.LANCZOS)
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output_path = f"temp_{uuid.uuid4()}.jpg"
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resized_img.save(output_path, quality=95)
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base64_image = encode_image(output_path)
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os.remove(output_path)
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return base64_image
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|
|
def gpt4_v_caption_dataset(
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images, captions,
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batch_size=4,
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):
|
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|
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if not OPENAI_API_KEY:
|
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print(f"Skipping GPT-4 Vision captioning because OPENAI_API_KEY is not set.")
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return captions
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|
|
|
prompt = "Accurate describe the contents of this image without assumptions. Avoid starting with statements like 'The image features...', just describe what you see."
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|
|
|
headers = {
|
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"Content-Type": "application/json",
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|
"Authorization": f"Bearer {OPENAI_API_KEY}"
|
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}
|
|
|
|
def fetch_caption(index, img):
|
|
base64_image = prep_img_for_gpt_api(img, max_size=(512, 512))
|
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|
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payload = {
|
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"model": "gpt-4-vision-preview",
|
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"messages": [
|
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{
|
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"role": "user",
|
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"content": [
|
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{"type": "text", "text": prompt},
|
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}", "detail": "low"}}
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|
]
|
|
}
|
|
],
|
|
"max_tokens": 100
|
|
}
|
|
|
|
response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
|
|
return index, response.json()["choices"][0]["message"]["content"]
|
|
|
|
with concurrent.futures.ThreadPoolExecutor(max_workers=batch_size) as executor:
|
|
future_to_index = {executor.submit(fetch_caption, i, img): i for i, img in enumerate(images) if captions[i] is None}
|
|
for future in concurrent.futures.as_completed(future_to_index):
|
|
index = future_to_index[future]
|
|
try:
|
|
captions[index] = future.result()[1]
|
|
print(f"Caption {index + 1}/{len(images)}: {captions[index]}")
|
|
except Exception as exc:
|
|
captions[index] = None
|
|
print(f"Caption generation for image {index + 1} failed with exception: {exc}")
|
|
|
|
return captions
|
|
|
|
|
|
|
|
@torch.no_grad()
|
|
def caption_dataset(
|
|
images: List[Image.Image],
|
|
captions: List[str],
|
|
model_name: Literal[str] = "blip"
|
|
) -> List[str]:
|
|
|
|
if "blip" in model_name:
|
|
captions = blip_caption_dataset(images, captions)
|
|
elif "gpt4-v" in model_name:
|
|
captions = gpt4_v_caption_dataset(images, captions)
|
|
|
|
return captions
|
|
|
|
|
|
|
|
def _crop_to_square(
|
|
image: Image.Image, com: List[Tuple[int, int]], resize_to: Optional[int] = None
|
|
):
|
|
cx, cy = com
|
|
width, height = image.size
|
|
if width > height:
|
|
left_possible = max(cx - height / 2, 0)
|
|
left = min(left_possible, width - height)
|
|
right = left + height
|
|
top = 0
|
|
bottom = height
|
|
else:
|
|
left = 0
|
|
right = width
|
|
top_possible = max(cy - width / 2, 0)
|
|
top = min(top_possible, height - width)
|
|
bottom = top + width
|
|
|
|
image = image.crop((left, top, right, bottom))
|
|
|
|
if resize_to:
|
|
image = image.resize((resize_to, resize_to), Image.Resampling.LANCZOS)
|
|
|
|
return image
|
|
|
|
|
|
def _center_of_mass(mask: Image.Image):
|
|
"""
|
|
Returns the center of mass of the mask
|
|
"""
|
|
x, y = np.meshgrid(np.arange(mask.size[0]), np.arange(mask.size[1]))
|
|
mask_np = np.array(mask) + 0.01
|
|
x_ = x * mask_np
|
|
y_ = y * mask_np
|
|
|
|
x = np.sum(x_) / np.sum(mask_np)
|
|
y = np.sum(y_) / np.sum(mask_np)
|
|
|
|
return x, y
|
|
|
|
|
|
def load_image_with_orientation(path, mode = "RGB"):
|
|
image = Image.open(path)
|
|
|
|
# Try to get the Exif orientation tag (0x0112), if it exists
|
|
try:
|
|
exif_data = image._getexif()
|
|
orientation = exif_data.get(0x0112)
|
|
except (AttributeError, KeyError, IndexError):
|
|
orientation = None
|
|
|
|
# Apply the orientation, if it's present
|
|
if orientation:
|
|
if orientation == 2:
|
|
image = image.transpose(Image.FLIP_LEFT_RIGHT)
|
|
elif orientation == 3:
|
|
image = image.rotate(180)
|
|
elif orientation == 4:
|
|
image = image.transpose(Image.FLIP_TOP_BOTTOM)
|
|
elif orientation == 5:
|
|
image = image.rotate(-90).transpose(Image.FLIP_LEFT_RIGHT)
|
|
elif orientation == 6:
|
|
image = image.rotate(-90)
|
|
elif orientation == 7:
|
|
image = image.rotate(90).transpose(Image.FLIP_LEFT_RIGHT)
|
|
elif orientation == 8:
|
|
image = image.rotate(90)
|
|
|
|
return image.convert(mode)
|
|
|
|
def hue_augmentation(image, hue_change_max = 4):
|
|
"""
|
|
Apply hue augmentation to the input image.
|
|
|
|
:param image: PIL Image object
|
|
:param hue_change: Amount to change the hue (0-360)
|
|
:return: Augmented PIL Image object
|
|
"""
|
|
hue_change = random.uniform(1, hue_change_max)
|
|
# Convert the image to HSV color space
|
|
hsv_image = image.convert('HSV')
|
|
|
|
# Split into individual channels
|
|
h, s, v = hsv_image.split()
|
|
|
|
# Apply the hue change
|
|
h = h.point(lambda i: (i + hue_change) % 256)
|
|
hsv_image = Image.merge('HSV', (h, s, v))
|
|
return hsv_image.convert('RGB')
|
|
|
|
def color_jitter(image):
|
|
enhancers = [ImageEnhance.Brightness, ImageEnhance.Contrast, ImageEnhance.Color]
|
|
factor_ranges = [[0.9, 1.1], [0.9, 1.25], [0.9, 1.2]]
|
|
for i, enhancer in enumerate(enhancers):
|
|
low, high = factor_ranges[i]
|
|
factor = random.uniform(low, high)
|
|
image = enhancer(image).enhance(factor)
|
|
return image
|
|
|
|
def random_crop(image, scale=(0.85, 0.95)):
|
|
width, height = image.size
|
|
new_width, new_height = width * random.uniform(*scale), height * random.uniform(*scale)
|
|
|
|
left = random.uniform(0, width - new_width)
|
|
top = random.uniform(0, height - new_height)
|
|
|
|
return image.crop((left, top, left + new_width, top + new_height))
|
|
|
|
def gaussian_blur(image):
|
|
return image.filter(ImageFilter.GaussianBlur(radius=1))
|
|
|
|
def augment_image(image):
|
|
image = hue_augmentation(image)
|
|
image = color_jitter(image)
|
|
image = random_crop(image)
|
|
if random.random() < 0.5:
|
|
image = gaussian_blur(image)
|
|
return image
|
|
|
|
|
|
|
|
def load_and_save_masks_and_captions(
|
|
concept_mode: str,
|
|
files: Union[str, List[str]],
|
|
output_dir: str = "tmp_out",
|
|
seed: int = 0,
|
|
caption_text: Optional[str] = None,
|
|
mask_target_prompts: Optional[Union[List[str], str]] = None,
|
|
target_size: int = 1024,
|
|
crop_based_on_salience: bool = True,
|
|
use_face_detection_instead: bool = False,
|
|
temp: float = 1.0,
|
|
n_length: int = -1,
|
|
add_lr_flips: bool = False,
|
|
augment_imgs_up_to_n: int = 0,
|
|
use_dataset_captions: bool = True, # load captions from the dataset if they exist
|
|
):
|
|
"""
|
|
Loads images from the given files, generates masks for them, and saves the masks and captions and upscale images
|
|
to output dir. If mask_target_prompts is given, it will generate kinda-segmentation-masks for the prompts and save them as well.
|
|
"""
|
|
os.makedirs(output_dir, exist_ok=True)
|
|
|
|
# load images
|
|
if isinstance(files, str):
|
|
if os.path.isdir(files):
|
|
print("Scanning directory for images...")
|
|
files = (
|
|
_find_files("*.png", files)
|
|
+ _find_files("*.jpg", files)
|
|
+ _find_files("*.jpeg", files)
|
|
)
|
|
|
|
if len(files) == 0:
|
|
raise Exception(
|
|
f"No files found in {files}. Either {files} is not a directory or it does not contain any .png or .jpg/jpeg files."
|
|
)
|
|
if n_length == -1:
|
|
n_length = len(files)
|
|
files = sorted(files)[:n_length]
|
|
|
|
images, captions = [], []
|
|
for file in files:
|
|
images.append(load_image_with_orientation(file))
|
|
caption_file = os.path.splitext(file)[0] + ".txt"
|
|
if os.path.exists(caption_file) and use_dataset_captions:
|
|
with open(caption_file, "r") as f:
|
|
captions.append(f.read())
|
|
else:
|
|
captions.append(None)
|
|
|
|
n_training_imgs = len(images)
|
|
n_captions = len([c for c in captions if c is not None])
|
|
print(f"Loaded {n_training_imgs} images, {n_captions} of which have captions.")
|
|
|
|
if len(images) < 50: # upscale images that are smaller than target_size:
|
|
print("upscaling imgs..")
|
|
upscale_margin = 0.75
|
|
images = swin_ir_sr(images, target_size=(int(target_size*upscale_margin), int(target_size*upscale_margin)))
|
|
|
|
if add_lr_flips and len(images) < 40:
|
|
print(f"Adding LR flips... (doubling the number of images from {n_training_imgs} to {n_training_imgs*2})")
|
|
images = images + [image.transpose(Image.FLIP_LEFT_RIGHT) for image in images]
|
|
captions = captions + captions
|
|
|
|
# It's nice if we can achieve the gpt pass, so if we're not losing too much, cut-off the n_images to just match what we're allowed to give to gpt:
|
|
if (len(images) > MAX_GPT_PROMPTS) and (len(images) < MAX_GPT_PROMPTS*1.33):
|
|
images = images[:MAX_GPT_PROMPTS-1]
|
|
captions = captions[:MAX_GPT_PROMPTS-1]
|
|
|
|
# Use BLIP for autocaptioning:
|
|
print(f"Generating {len(images)} captions using mode: {concept_mode}...")
|
|
captions = caption_dataset(images, captions)
|
|
|
|
# Cleanup prompts using chatgpt:
|
|
captions, trigger_text, gpt_concept_name = post_process_captions(captions, caption_text, concept_mode, seed)
|
|
|
|
aug_imgs, aug_caps = [],[]
|
|
while len(images) + len(aug_imgs) < augment_imgs_up_to_n: # if we still have a very small amount of imgs, do some basic augmentation:
|
|
print(f"Adding augmented version of each training img...")
|
|
aug_imgs.extend([augment_image(image) for image in images])
|
|
aug_caps.extend(captions)
|
|
|
|
images.extend(aug_imgs)
|
|
captions.extend(aug_caps)
|
|
|
|
if (gpt_concept_name is not None) and ((mask_target_prompts is None) or (mask_target_prompts == "")):
|
|
print(f"Using GPT concept name as CLIP-segmentation prompt: {gpt_concept_name}")
|
|
mask_target_prompts = gpt_concept_name
|
|
|
|
if mask_target_prompts is None:
|
|
print("Disabling CLIP-segmentation")
|
|
mask_target_prompts = ""
|
|
temp = 999
|
|
|
|
print(f"Generating {len(images)} masks...")
|
|
if not use_face_detection_instead:
|
|
seg_masks = clipseg_mask_generator(
|
|
images=images, target_prompts=mask_target_prompts, temp=temp
|
|
)
|
|
else:
|
|
mask_target_prompts = "FACE detection was used"
|
|
if add_lr_flips:
|
|
print("WARNING you are applying face detection while also doing left-right flips, this might not be what you intended?")
|
|
seg_masks = face_mask_google_mediapipe(images=images)
|
|
|
|
print("Masks generated! Cropping images to center of mass...")
|
|
# find the center of mass of the mask
|
|
if crop_based_on_salience:
|
|
coms = [_center_of_mass(mask) for mask in seg_masks]
|
|
else:
|
|
coms = [(image.size[0] / 2, image.size[1] / 2) for image in images]
|
|
|
|
# based on the center of mass, crop the image to a square
|
|
print("Cropping squares...")
|
|
images = [
|
|
_crop_to_square(image, com, resize_to=None)
|
|
for image, com in zip(images, coms)
|
|
]
|
|
|
|
seg_masks = [
|
|
_crop_to_square(mask, com, resize_to=target_size)
|
|
for mask, com in zip(seg_masks, coms)
|
|
]
|
|
|
|
print("Resizing images to training size...")
|
|
images = [
|
|
image.resize((target_size, target_size), Image.Resampling.LANCZOS)
|
|
for image in images
|
|
]
|
|
|
|
data = []
|
|
# clean TEMP_OUT_DIR first
|
|
if os.path.exists(output_dir):
|
|
for file in os.listdir(output_dir):
|
|
os.remove(os.path.join(output_dir, file))
|
|
|
|
os.makedirs(output_dir, exist_ok=True)
|
|
|
|
# Make sure we've correctly inserted the TOK into every caption:
|
|
captions = ["TOK, " + caption if "TOK" not in caption else caption for caption in captions]
|
|
for caption in captions:
|
|
print(caption)
|
|
|
|
# iterate through the images, masks, and captions and add a row to the dataframe for each
|
|
print("Saving final training dataset...")
|
|
for idx, (image, mask, caption) in enumerate(zip(images, seg_masks, captions)):
|
|
|
|
image_name = f"{idx}.src.jpg"
|
|
mask_file = f"{idx}.mask.jpg"
|
|
# save the image and mask files
|
|
image.save(os.path.join(output_dir, image_name), quality=95)
|
|
mask.save(os.path.join(output_dir, mask_file), quality=95)
|
|
|
|
# add a new row to the dataframe with the file names and caption
|
|
data.append(
|
|
{"image_path": image_name, "mask_path": mask_file, "caption": caption},
|
|
)
|
|
|
|
df = pd.DataFrame(columns=["image_path", "mask_path", "caption"], data=data)
|
|
# save the dataframe to a CSV file
|
|
df.to_csv(os.path.join(output_dir, "captions.csv"), index=False)
|
|
print("---> Training data 100% ready to go!")
|
|
|
|
return n_training_imgs, trigger_text, mask_target_prompts, captions
|
|
|
|
|
|
|
|
def face_mask_google_mediapipe(
|
|
images: List[Image.Image], blur_amount: float = 0.0, bias: float = 50.0
|
|
) -> List[Image.Image]:
|
|
"""
|
|
Returns a list of images with masks on the face parts.
|
|
"""
|
|
mp_face_detection = mp.solutions.face_detection
|
|
mp_face_mesh = mp.solutions.face_mesh
|
|
|
|
face_detection = mp_face_detection.FaceDetection(
|
|
model_selection=1, min_detection_confidence=0.1
|
|
)
|
|
face_mesh = mp_face_mesh.FaceMesh(
|
|
static_image_mode=True, max_num_faces=1, min_detection_confidence=0.1
|
|
)
|
|
|
|
masks = []
|
|
for image in tqdm(images):
|
|
image_np = np.array(image)
|
|
|
|
# Perform face detection
|
|
results_detection = face_detection.process(image_np)
|
|
ih, iw, _ = image_np.shape
|
|
if results_detection.detections:
|
|
for detection in results_detection.detections:
|
|
bboxC = detection.location_data.relative_bounding_box
|
|
|
|
bbox = (
|
|
int(bboxC.xmin * iw),
|
|
int(bboxC.ymin * ih),
|
|
int(bboxC.width * iw),
|
|
int(bboxC.height * ih),
|
|
)
|
|
|
|
# make sure bbox is within image
|
|
bbox = (
|
|
max(0, bbox[0]),
|
|
max(0, bbox[1]),
|
|
min(iw - bbox[0], bbox[2]),
|
|
min(ih - bbox[1], bbox[3]),
|
|
)
|
|
|
|
print(bbox)
|
|
|
|
# Extract face landmarks
|
|
face_landmarks = face_mesh.process(
|
|
image_np[bbox[1] : bbox[1] + bbox[3], bbox[0] : bbox[0] + bbox[2]]
|
|
).multi_face_landmarks
|
|
|
|
# https://github.com/google/mediapipe/issues/1615
|
|
# This was def helpful
|
|
indexes = [
|
|
10,
|
|
338,
|
|
297,
|
|
332,
|
|
284,
|
|
251,
|
|
389,
|
|
356,
|
|
454,
|
|
323,
|
|
361,
|
|
288,
|
|
397,
|
|
365,
|
|
379,
|
|
378,
|
|
400,
|
|
377,
|
|
152,
|
|
148,
|
|
176,
|
|
149,
|
|
150,
|
|
136,
|
|
172,
|
|
58,
|
|
132,
|
|
93,
|
|
234,
|
|
127,
|
|
162,
|
|
21,
|
|
54,
|
|
103,
|
|
67,
|
|
109,
|
|
]
|
|
|
|
if face_landmarks:
|
|
mask = Image.new("L", (iw, ih), 0)
|
|
mask_np = np.array(mask)
|
|
|
|
for face_landmark in face_landmarks:
|
|
face_landmark = [face_landmark.landmark[idx] for idx in indexes]
|
|
landmark_points = [
|
|
(int(l.x * bbox[2]) + bbox[0], int(l.y * bbox[3]) + bbox[1])
|
|
for l in face_landmark
|
|
]
|
|
mask_np = cv2.fillPoly(
|
|
mask_np, [np.array(landmark_points)], 255
|
|
)
|
|
|
|
mask = Image.fromarray(mask_np)
|
|
|
|
# Apply blur to the mask
|
|
if blur_amount > 0:
|
|
mask = mask.filter(ImageFilter.GaussianBlur(blur_amount))
|
|
|
|
# Apply bias to the mask
|
|
if bias > 0:
|
|
mask = np.array(mask)
|
|
mask = mask + bias * np.ones(mask.shape, dtype=mask.dtype)
|
|
mask = np.clip(mask, 0, 255)
|
|
mask = Image.fromarray(mask)
|
|
|
|
# Convert mask to 'L' mode (grayscale) before saving
|
|
mask = mask.convert("L")
|
|
|
|
masks.append(mask)
|
|
else:
|
|
# If face landmarks are not available, add a black mask of the same size as the image
|
|
masks.append(Image.new("L", (iw, ih), 255))
|
|
|
|
else:
|
|
print("No face detected, adding full mask")
|
|
# If no face is detected, add a white mask of the same size as the image
|
|
masks.append(Image.new("L", (iw, ih), 255))
|
|
|
|
return masks
|