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# Have SwinIR upsample
# Have BLIP auto caption
# Have CLIPSeg auto mask concept
from typing import List, Literal, Union, Optional, Tuple
import os
from PIL import Image, ImageFilter
import torch
import numpy as np
import fire
from tqdm import tqdm
import glob
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
@torch.no_grad()
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-classical-sr-x2-64",
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.
"""
# So this is currently in main branch, so this can be used in the future I guess?
from transformers import Swin2SRForImageSuperResolution, Swin2SRImageProcessor
model = Swin2SRForImageSuperResolution.from_pretrained(
model_id,
).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()
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'Warning: only one target prompt "{target_prompts}" was given, so it will be used for all images'
)
target_prompts = [target_prompts] * len(images)
processor = CLIPSegProcessor.from_pretrained(model_id)
model = CLIPSegForImageSegmentation.from_pretrained(model_id).to(device)
masks = []
for image, prompt in tqdm(zip(images, target_prompts)):
original_size = image.size
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)
masks.append(mask)
return masks
@torch.no_grad()
def blip_captioning_dataset(
images: List[Image.Image],
text: Optional[str] = None,
model_id: Literal[
"Salesforce/blip-image-captioning-large",
"Salesforce/blip-image-captioning-base",
] = "Salesforce/blip-image-captioning-large",
device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
**kwargs,
) -> List[str]:
"""
Returns a list of captions for the given images
"""
from transformers import BlipProcessor, BlipForConditionalGeneration
processor = BlipProcessor.from_pretrained(model_id)
model = BlipForConditionalGeneration.from_pretrained(model_id).to(device)
captions = []
for image in tqdm(images):
inputs = processor(image, text=text, return_tensors="pt").to("cuda")
out = model.generate(
**inputs, max_length=150, do_sample=True, top_k=50, temperature=0.7
)
caption = processor.decode(out[0], skip_special_tokens=True)
captions.append(caption)
return captions
def face_mask_google_mediapipe(
images: List[Image.Image], blur_amount: float = 80.0, bias: float = 0.05
) -> List[Image.Image]:
"""
Returns a list of images with mask on the face parts.
"""
import mediapipe as mp
mp_face_detection = mp.solutions.face_detection
face_detection = mp_face_detection.FaceDetection(
model_selection=1, min_detection_confidence=0.5
)
masks = []
for image in tqdm(images):
image = np.array(image)
results = face_detection.process(image)
black_image = np.ones((image.shape[0], image.shape[1]), dtype=np.uint8)
if results.detections:
for detection in results.detections:
x_min = int(
detection.location_data.relative_bounding_box.xmin * image.shape[1]
)
y_min = int(
detection.location_data.relative_bounding_box.ymin * image.shape[0]
)
width = int(
detection.location_data.relative_bounding_box.width * image.shape[1]
)
height = int(
detection.location_data.relative_bounding_box.height
* image.shape[0]
)
# draw the colored rectangle
black_image[y_min : y_min + height, x_min : x_min + width] = 255
black_image = Image.fromarray(black_image)
masks.append(black_image)
return masks
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]))
x_ = x * np.array(mask)
y_ = y * np.array(mask)
x = np.sum(x_) / np.sum(mask)
y = np.sum(y_) / np.sum(mask)
return x, y
def load_and_save_masks_and_captions(
files: Union[str, List[str]],
output_dir: str,
caption_text: Optional[str] = None,
target_prompts: Optional[Union[List[str], str]] = None,
target_size: int = 512,
crop_based_on_salience: bool = True,
use_face_detection_instead: bool = False,
temp: float = 1.0,
n_length: int = -1,
):
"""
Loads images from the given files, generates masks for them, and saves the masks and captions and upscale images
to output dir.
"""
os.makedirs(output_dir, exist_ok=True)
# load images
if isinstance(files, str):
# check if it is a directory
if os.path.isdir(files):
# get all the .png .jpg in the directory
files = glob.glob(os.path.join(files, "*.png")) + glob.glob(
os.path.join(files, "*.jpg")
)
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 files."
)
if n_length == -1:
n_length = len(files)
files = sorted(files)[:n_length]
images = [Image.open(file) for file in files]
# captions
print(f"Generating {len(images)} captions...")
captions = blip_captioning_dataset(images, text=caption_text)
if target_prompts is None:
target_prompts = captions
print(f"Generating {len(images)} masks...")
if not use_face_detection_instead:
seg_masks = clipseg_mask_generator(
images=images, target_prompts=target_prompts, temp=temp
)
else:
seg_masks = face_mask_google_mediapipe(images=images)
# 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
images = [
_crop_to_square(image, com, resize_to=None) for image, com in zip(images, coms)
]
print(f"Upscaling {len(images)} images...")
# upscale images anyways
images = swin_ir_sr(images, target_size=(target_size, target_size))
images = [
image.resize((target_size, target_size), Image.Resampling.LANCZOS)
for image in images
]
seg_masks = [
_crop_to_square(mask, com, resize_to=target_size)
for mask, com in zip(seg_masks, coms)
]
with open(os.path.join(output_dir, "caption.txt"), "w") as f:
# save images and masks
for idx, (image, mask, caption) in enumerate(zip(images, seg_masks, captions)):
image.save(os.path.join(output_dir, f"{idx}.src.jpg"), quality=99)
mask.save(os.path.join(output_dir, f"{idx}.mask.png"))
f.write(caption + "\n")
def main():
fire.Fire(load_and_save_masks_and_captions)
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