# Have SwinIR upsample # Have BLIP auto caption # Have CLIPSeg auto mask concept import gc import fnmatch import mimetypes import os import time import re import shutil import tarfile import base64 import requests import concurrent.futures from pathlib import Path from typing import List, Literal, Optional, Tuple, Union from zipfile import ZipFile from PIL import ImageEnhance, ImageFilter, Image import random import cv2 import mediapipe as mp import numpy as np import pandas as pd import torch from tqdm import tqdm from transformers import ( BlipForConditionalGeneration, Blip2ForConditionalGeneration, BlipProcessor, Blip2Processor, CLIPSegForImageSegmentation, CLIPSegProcessor, Swin2SRForImageSuperResolution, Swin2SRImageProcessor, ) from trainer.utils.io import download_and_prep_training_data from trainer.utils.utils import fix_prompt import re import openai from openai import OpenAI from dotenv import load_dotenv load_dotenv() try: OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") client = OpenAI(api_key=OPENAI_API_KEY) print("OpenAI API key loaded") except: OPENAI_API_KEY = None client = None print("WARNING: Could not find OPENAI_API_KEY in .env, disabling gpt prompt generation.") MODEL_PATH = "./cache" MAX_GPT_PROMPTS = 50 def _find_files(pattern, dir="."): """Return list of files matching pattern in a given directory, in absolute format. Unlike glob, this is case-insensitive. """ rule = re.compile(fnmatch.translate(pattern), re.IGNORECASE) return [os.path.join(dir, f) for f in os.listdir(dir) if rule.match(f)] def preprocess( config, working_directory, concept_mode, input_zip_path: Path, caption_text: str, mask_target_prompts: str, target_size: int, crop_based_on_salience: bool, use_face_detection_instead: bool, temp: float, left_right_flip_augmentation: bool = False, augment_imgs_up_to_n: int = 0, caption_model: str = "blip", seed: int = 0, ) -> Path: if os.path.exists(working_directory): print(f"working_directory {working_directory} already existed.. deleting and recreating!") shutil.rmtree(working_directory) os.makedirs(working_directory) # Setup directories for the training data: TEMP_IN_DIR = os.path.join(working_directory, "images_in") TEMP_OUT_DIR = os.path.join(working_directory, "images_out") for path in [TEMP_OUT_DIR, TEMP_IN_DIR]: if os.path.exists(path): shutil.rmtree(path) os.makedirs(path) download_and_prep_training_data(input_zip_path, TEMP_IN_DIR) config = load_and_save_masks_and_captions( config, concept_mode, files=TEMP_IN_DIR, output_dir=TEMP_OUT_DIR, seed=seed, caption_text=caption_text, mask_target_prompts=mask_target_prompts, target_size=target_size, crop_based_on_salience=crop_based_on_salience, use_face_detection_instead=use_face_detection_instead, temp=temp, add_lr_flips = left_right_flip_augmentation, augment_imgs_up_to_n = augment_imgs_up_to_n, caption_model = caption_model ) return config, Path(TEMP_OUT_DIR) @torch.no_grad() @torch.cuda.amp.autocast() def swin_ir_sr( images: List[Image.Image], model_id: Literal[ "caidas/swin2SR-classical-sr-x2-64", "caidas/swin2SR-classical-sr-x4-48", "caidas/swin2SR-realworld-sr-x4-64-bsrgan-psnr", ] = "caidas/swin2SR-realworld-sr-x4-64-bsrgan-psnr", target_size: Optional[Tuple[int, int]] = None, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"), **kwargs, ) -> List[Image.Image]: """ Upscales images using SwinIR. Returns a list of PIL images. If the image is already larger than the target size, it will not be upscaled and will be returned as is. """ model = Swin2SRForImageSuperResolution.from_pretrained( model_id, cache_dir=MODEL_PATH ).to(device) processor = Swin2SRImageProcessor() out_images = [] for image in tqdm(images): ori_w, ori_h = image.size if target_size is not None: if ori_w >= target_size[0] and ori_h >= target_size[1]: out_images.append(image) continue inputs = processor(image, return_tensors="pt").to(device) with torch.no_grad(): outputs = model(**inputs) output = ( outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1).numpy() ) output = np.moveaxis(output, source=0, destination=-1) output = (output * 255.0).round().astype(np.uint8) output = Image.fromarray(output) out_images.append(output) return out_images @torch.no_grad() @torch.cuda.amp.autocast() def clipseg_mask_generator( images: List[Image.Image], target_prompts: Union[List[str], str], model_id: Literal[ "CIDAS/clipseg-rd64-refined", "CIDAS/clipseg-rd16" ] = "CIDAS/clipseg-rd64-refined", device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"), bias: float = 0.01, temp: float = 1.0, **kwargs, ) -> List[Image.Image]: """ Returns a greyscale mask for each image, where the mask is the probability of the target prompt being present in the image """ if isinstance(target_prompts, str): print( f'Using "{target_prompts}" as CLIP-segmentation prompt for all images.' ) target_prompts = [target_prompts] * len(images) model = None if any(target_prompts): processor = CLIPSegProcessor.from_pretrained(model_id, cache_dir=MODEL_PATH) model = CLIPSegForImageSegmentation.from_pretrained( model_id, cache_dir=MODEL_PATH ).to(device) masks = [] for image, prompt in tqdm(zip(images, target_prompts)): original_size = image.size if prompt != "": inputs = processor( text=[prompt, ""], images=[image] * 2, padding="max_length", truncation=True, return_tensors="pt", ).to(device) outputs = model(**inputs) logits = outputs.logits probs = torch.nn.functional.softmax(logits / temp, dim=0)[0] probs = (probs + bias).clamp_(0, 1) probs = 255 * probs / probs.max() # make mask greyscale mask = Image.fromarray(probs.cpu().numpy()).convert("L") # resize mask to original size mask = mask.resize(original_size) else: mask = Image.new("L", original_size, 255) masks.append(mask) # cleanup del model gc.collect() torch.cuda.empty_cache() return masks import textwrap def cleanup_prompts_with_chatgpt( prompts, concept_mode, # face / object / style seed, # seed for chatgpt reproducibility verbose = True): if concept_mode == "object": chat_gpt_prompt_1 = textwrap.dedent(""" Analyze a set of (poor) image descriptions each featuring the same concept, figure or thing. Tasks: 1. Deduce a concise (max 10 words) visual description of just the concept TOK (Concept Description). 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)! 3. Streamline each description to its core elements, ensuring clarity and mandatory inclusion of the placeholder string "TOK". The descriptions are:""") chat_gpt_prompt_2 = textwrap.dedent(""" Respond with the "Concept Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "TOK". """) elif concept_mode == "face": chat_gpt_prompt_1 = textwrap.dedent(""" Analyze a set of (poor) image descriptions, each featuring a person named TOK. Tasks: 1. Deduce a concise (max 10 words) visual description of TOK (TOK Description), hallucinate a basic description if necessary (eg black man with long beard). 2. Rewrite each description, injecting "TOK" naturally into each description, adjusting where needed. 3. Streamline each description to its core elements, ensuring clarity and mandatory inclusion of "TOK". The descriptions are:""") chat_gpt_prompt_2 = textwrap.dedent(""" Respond with the "TOK Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "TOK". """) elif concept_mode == "style": chat_gpt_prompt_1 = textwrap.dedent(""" Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK. Tasks: 1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description). 2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK. 3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK". The descriptions are:""") chat_gpt_prompt_2 = textwrap.dedent(""" Respond with the "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK". """) final_chatgpt_prompt = chat_gpt_prompt_1 + "\n- " + "\n- ".join(prompts) + "\n" + chat_gpt_prompt_2 print("Final chatgpt prompt:") print(final_chatgpt_prompt) print("--------------------------") print(f"Calling chatgpt with seed {seed}...") response = client.chat.completions.create( model="gpt-4-1106-preview", seed=seed, messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": final_chatgpt_prompt}, ]) gpt_completion = response.choices[0].message.content if verbose: # pretty print the full response json: print("----- GPT response: -----") print(gpt_completion) print("--------------------------") # extract the final rephrased prompts from the response: prompts = [] for line in gpt_completion.split("\n"): if line.startswith("-"): prompts.append(line[2:]) gpt_concept_description = extract_gpt_concept_description(gpt_completion, concept_mode) trigger_text = "TOK" if concept_mode == 'style': trigger_text = "in the style of TOK, " return prompts, gpt_concept_description, trigger_text def extract_gpt_concept_description(gpt_completion, concept_mode): """ Extracts the concept name from the GPT completion based on the concept mode. """ if concept_mode == 'face': prefix = "TOK Description:" elif concept_mode == 'style': prefix = "Style Description:" elif concept_mode == 'object': prefix = "Concept Description:" for line in gpt_completion.split("\n"): if line.startswith(prefix): concept_name = line[len(prefix):].strip() break return concept_name def post_process_captions(captions, text, concept_mode, job_seed): text = text.strip() print(f"Input captioning text: {text}") if len(captions) > 3 and len(captions) < MAX_GPT_PROMPTS and not text and client: retry_count = 0 while retry_count < 10: try: gpt_captions, gpt_concept_description, trigger_text = cleanup_prompts_with_chatgpt(captions, concept_mode, job_seed + retry_count) n_toks = sum("TOK" in caption for caption in gpt_captions) if n_toks > int(0.8 * len(captions)) and (len(gpt_captions) == len(captions)): # Ensure every caption contains "TOK" gpt_captions = ["TOK, " + caption if "TOK" not in caption else caption for caption in gpt_captions] captions = gpt_captions break else: if len(gpt_captions) == len(captions): print(f'GPT-4 did not return enough {n_toks}/{len(captions)} prompts containing "TOK", retrying...') else: print(f'GPT-4 returned the wrong number of prompts {len(gpt_captions)} instead of {len(captions)}, retrying...') retry_count += 1 except Exception as e: retry_count += 1 print(f"An error occurred after try {retry_count}: {e}") time.sleep(1) else: gpt_concept_description, trigger_text = None, "TOK" else: # simple concat of trigger text with rest of prompt: if len(text) == 0: print("WARNING: no captioning text was given and we're not doing chatgpt cleanup...") print("Concept mode: ", concept_mode) if concept_mode == "style": trigger_text = "in the style of TOK, " captions = [trigger_text + caption for caption in captions] else: trigger_text = "TOK, " captions = [trigger_text + caption for caption in captions] else: trigger_text = text captions = [trigger_text + ", " + caption for caption in captions] gpt_concept_description = None captions = [fix_prompt(caption) for caption in captions] return captions, trigger_text, gpt_concept_description def blip_caption_dataset( images: List[Image.Image], captions: List[str], model_id: Literal[ "Salesforce/blip-image-captioning-large", "Salesforce/blip-image-captioning-base", "Salesforce/blip2-opt-2.7b", ] = "Salesforce/blip-image-captioning-large" ): print(f"Using model {model_id} for image captioning...") device=torch.device("cuda" if torch.cuda.is_available() else "cpu") if "blip2" in model_id: processor = Blip2Processor.from_pretrained(model_id, cache_dir=MODEL_PATH) model = Blip2ForConditionalGeneration.from_pretrained( model_id, cache_dir=MODEL_PATH, torch_dtype=torch.float16 ).to(device) else: processor = BlipProcessor.from_pretrained(model_id, cache_dir=MODEL_PATH) model = BlipForConditionalGeneration.from_pretrained( model_id, cache_dir=MODEL_PATH, torch_dtype=torch.float16 ).to(device) for i, image in enumerate(tqdm(images)): if captions[i] is None: inputs = processor(image, return_tensors="pt").to(device, torch.float16) out = model.generate(**inputs, max_length=100, do_sample=True, top_k=40, temperature=0.65) captions[i] = processor.decode(out[0], skip_special_tokens=True) del model gc.collect() torch.cuda.empty_cache() return captions def encode_image(image_path): with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode('utf-8') import uuid def prep_img_for_gpt_api(pil_img, max_size=(512, 512)): # create a temporary file to save the resized image: resized_img = pil_img.copy() resized_img.thumbnail(max_size, Image.Resampling.LANCZOS) output_path = f"temp_{uuid.uuid4()}.jpg" resized_img.save(output_path, quality=95) base64_image = encode_image(output_path) os.remove(output_path) return base64_image def gpt4_v_get_description(config, images): if config.concept_mode == "object": description = "object" prompt = "Give a concise visual descriptioni of the object/figure/thing that all the grid-images have in common with at most 10 words. Dont start with statements like 'The image features...', just describe what you see." elif config.concept_mode == "face": description = "face" prompt = "All the grid images depict a single person. Visually describe this person with at most 10 words. Dont start with statements like 'The image features...', just describe what you see. (eg an asian woman with long black hair)" elif config.concept_mode == "style": description = "" prompt = "All these images share a common aesthetic style. Describe this style with at most 7 words. Dont start with statements like 'The image features...', just describe what you see. (eg impressionism collage surrealism)" if not OPENAI_API_KEY: print(f"Skipping GPT-4 Vision description because OPENAI_API_KEY is not set.") return description headers = { "Content-Type": "application/json", "Authorization": f"Bearer {OPENAI_API_KEY}" } # TODO sample a grid img: # .... TODO base64_image = prep_img_for_gpt_api(img, max_size=(1024, 1024)) payload = { "model": "gpt-4-vision-preview", "messages": [ { "role": "user", "content": [ {"type": "text", "text": prompt}, {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}", "detail": "high"}} ] } ], "max_tokens": 60 } response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload) answer = response.json()["choices"][0]["message"]["content"] return captions def gpt4_v_caption_dataset( images, captions, batch_size=4, ): if not OPENAI_API_KEY: print(f"Skipping GPT-4 Vision captioning because OPENAI_API_KEY is not set.") return captions prompt = "Concisely describe this image without assumptions with at most 20 words. Dont start with statements like 'The image features...', just describe what you see." headers = { "Content-Type": "application/json", "Authorization": f"Bearer {OPENAI_API_KEY}" } def fetch_caption(index, img): base64_image = prep_img_for_gpt_api(img, max_size=(512, 512)) payload = { "model": "gpt-4-vision-preview", "messages": [ { "role": "user", "content": [ {"type": "text", "text": prompt}, {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}", "detail": "low"}} ] } ], "max_tokens": 60 } 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], caption_model: Literal[str] = "blip" ) -> List[str]: if "blip" in caption_model: captions = blip_caption_dataset(images, captions) elif "gpt4-v" in caption_model: captions = gpt4_v_caption_dataset(images, captions) return captions def _crop_to_aspect_ratio( image: Image.Image, com: List[Tuple[int, int]], target_aspect_ratio: float = 1.0, # width / height resize_to: Optional[int] = None ): """ Crops the image to the specified aspect ratio around the center of mass of the mask. """ cx, cy = com width, height = image.size if target_aspect_ratio > 1: # Wider than tall new_width = min(width, height * target_aspect_ratio) new_height = new_width / target_aspect_ratio else: # Taller than wide or square new_height = min(height, width / target_aspect_ratio) new_width = new_height * target_aspect_ratio left = max(cx - new_width / 2, 0) right = min(left + new_width, width) top = max(cy - new_height / 2, 0) bottom = min(top + new_height, height) # Adjust if the crop goes beyond the image boundaries if right > width: right = width left = right - new_width if bottom > height: bottom = height top = bottom - new_height image = image.crop((left, top, right, bottom)) if resize_to: if target_aspect_ratio > 1: resize_height = int(resize_to / target_aspect_ratio) image = image.resize((resize_to, resize_height), Image.Resampling.LANCZOS) else: resize_width = int(resize_to * target_aspect_ratio) image = image.resize((resize_width, resize_to), Image.Resampling.LANCZOS) return image def _crop_to_square( image: Image.Image, com: List[Tuple[int, int]], resize_to: Optional[int] = None ): """ Crops the image to a square around the center of mass of the mask. """ 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 calculate_new_dimensions(target_size, target_aspect_ratio): """ Calculate the new width and height given a target size and aspect ratio. """ # Calculate the total number of pixels n_pixels = target_size ** 2 # Calculate the new width and height based on the target aspect ratio new_width = (n_pixels * target_aspect_ratio) ** 0.5 new_height = (n_pixels / new_width) # round up/down to the nearest multiple of 64: new_width = int(64 * round(new_width / 64.0)) new_height = int(64 * round(new_height / 64.0)) return [new_width, new_height] def load_and_save_masks_and_captions( config, 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 caption_model: str = "blip" ): """ 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) # Compute average aspect ratio of images: aspect_ratios = [image.size[0] / image.size[1] for image in images] avg_aspect_ratio = sum(aspect_ratios) / len(aspect_ratios) print(f"Average aspect ratio of images: {avg_aspect_ratio}") config.train_img_size = calculate_new_dimensions(target_size, avg_aspect_ratio) config.train_aspect_ratio = config.train_img_size[0] / config.train_img_size[1] target_size = max(config.train_img_size) print(f"New train_img_size: {config.train_img_size}") if config.validation_img_size is None: print(f"Validation_img_size was not specified in config, so also setting this to {config.train_img_size}") config.validation_img_size = config.train_img_size 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(config.train_img_size[0]*upscale_margin), int(config.train_img_size[0]*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, caption_model = caption_model) # Cleanup prompts using chatgpt: captions = [fix_prompt(caption) for caption in captions] captions, trigger_text, gpt_concept_description = 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_description 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_description}") mask_target_prompts = gpt_concept_description if mask_target_prompts is None or config.concept_mode == "style": 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 and resizing images...") images = [ _crop_to_aspect_ratio(image, com, target_aspect_ratio = config.train_aspect_ratio, # width / height resize_to = target_size) for image, com in zip(images, coms) ] seg_masks = [ _crop_to_aspect_ratio(mask, com, target_aspect_ratio = config.train_aspect_ratio, # width / height resize_to = target_size) for mask, com in zip(seg_masks, coms) ] 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) if config.remove_ti_token_from_prompts: print('------------ WARNING ------------') print("Removing 'TOK, ' from captions...") print("This will completely break textual_inversion!!") print('------------ WARNING ------------') captions = [caption.replace("TOK, ", "") for caption in captions] # 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!") # Update the training attributes with some info from the pre-processing: config.training_attributes["n_training_imgs"] = n_training_imgs config.training_attributes["trigger_text"] = trigger_text config.training_attributes["segmentation_prompt"] = mask_target_prompts config.training_attributes["gpt_description"] = gpt_concept_description config.training_attributes["captions"] = captions return config 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