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import base64
import logging
import math
import os
import sys
import time
import warnings
from functools import lru_cache
from io import BytesIO
import io
import requests
import torch
import torchvision
from packaging import version
from PIL import Image
from torchvision import transforms
from torchvision.transforms import InterpolationMode
from typing import Optional
import random
logger = logging.getLogger(__name__)
IMAGE_FACTOR = 32
MIN_PIXELS = 4 * 32 * 32
MAX_PIXELS = 50 * 32 * 32
MAX_RATIO = 200
VIDEO_MIN_PIXELS = 128 * 28 * 28
VIDEO_MAX_PIXELS = 768 * 28 * 28
FRAME_FACTOR = 2
FPS = 2.0
FPS_MIN_FRAMES = 4
FPS_MAX_FRAMES = 768
VIDEO_TOTAL_PIXELS = int(float(os.environ.get('VIDEO_MAX_PIXELS', 128000 * 28 * 28 * 0.9)))
def round_by_factor(number: int, factor: int) -> int:
return round(number / factor) * factor
def ceil_by_factor(number: int, factor: int) -> int:
return math.ceil(number / factor) * factor
def floor_by_factor(number: int, factor: int) -> int:
return math.floor(number / factor) * factor
def smart_resize(
height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS
) -> tuple[int, int]:
if max(height, width) / min(height, width) > MAX_RATIO:
raise ValueError(
f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}"
)
h_bar = max(factor, round_by_factor(height, factor))
w_bar = max(factor, round_by_factor(width, factor))
if h_bar * w_bar > max_pixels:
beta = math.sqrt((height * width) / max_pixels)
h_bar = floor_by_factor(height / beta, factor)
w_bar = floor_by_factor(width / beta, factor)
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (height * width))
h_bar = ceil_by_factor(height * beta, factor)
w_bar = ceil_by_factor(width * beta, factor)
if h_bar / w_bar > MAX_RATIO:
h_bar = w_bar * MAX_RATIO
elif w_bar / h_bar > MAX_RATIO:
w_bar = h_bar * MAX_RATIO
return h_bar, w_bar
def to_rgb(pil_image: Image.Image) -> Image.Image:
if pil_image.mode == 'RGBA':
white_background = Image.new("RGB", pil_image.size, (255, 255, 255))
white_background.paste(pil_image, mask=pil_image.split()[3])
return white_background
else:
return pil_image.convert("RGB")
def resize_bbox(bbox, w, h, scale=None):
x1, y1, x2, y2 = bbox
orig_center_x = (x1 + x2) / 2
orig_center_y = (y1 + y2) / 2
orig_w = x2 - x1
orig_h = y2 - y1
max_scale_x = min(orig_center_x / max(orig_w / 2, 1e-6), (w - orig_center_x) / max(orig_w / 2, 1e-6))
max_scale_y = min(orig_center_y / max(orig_h / 2, 1e-6), (h - orig_center_y) / max(orig_h / 2, 1e-6))
max_scale = min(max_scale_x, max_scale_y)
if scale is not None:
scale = min(max_scale, scale)
else:
scale = 1.0
if max_scale > 1.0:
scale = random.uniform(1.0, max_scale)
new_w = orig_w * scale
new_h = orig_h * scale
new_x1 = int(math.floor(orig_center_x - new_w / 2))
new_y1 = int(math.floor(orig_center_y - new_h / 2))
new_x2 = int(math.ceil(orig_center_x + new_w / 2))
new_y2 = int(math.ceil(orig_center_y + new_h / 2))
new_x1 = max(0, new_x1)
new_y1 = max(0, new_y1)
new_x2 = min(w, new_x2)
new_y2 = min(h, new_y2)
if new_x1 >= new_x2 or new_y1 >= new_y2 or x2 > w or y2 > h:
return bbox
return [new_x1, new_y1, new_x2, new_y2]
def fetch_image(ele, size_factor: int = IMAGE_FACTOR) -> Image.Image:
if "image" in ele:
image = ele["image"]
else:
image = ele["image_url"]
image_obj = None
if isinstance(image, Image.Image):
image_obj = image
elif image.startswith("http://") or image.startswith("https://"):
response = requests.get(image, stream=True)
image_obj = Image.open(BytesIO(response.content))
elif image.startswith("file://"):
image_obj = Image.open(image[7:])
elif image.startswith("data:image"):
if "base64," in image:
_, base64_data = image.split("base64,", 1)
data = base64.b64decode(base64_data)
image_obj = Image.open(BytesIO(data))
else:
image_obj = Image.open(image)
bbox = ele.get("bbox", None)
if bbox:
w, h = image_obj.width, image_obj.height
if len(bbox) == 6:
x1_prop, y1_prop, x2_prop, y2_prop, w_bar, h_bar = bbox
x1 = int(x1_prop / w_bar * w)
y1 = int(y1_prop / h_bar * h)
x2 = int(x2_prop / w_bar * w)
y2 = int(y2_prop / h_bar * h)
bbox = [x1, y1, x2, y2]
if ele.get("dynamic_crop", False):
bbox = resize_bbox(bbox, w, h)
image_obj = image_obj.crop(bbox)
if image_obj is None:
raise ValueError(f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}")
image = to_rgb(image_obj)
if "hflip" in ele and ele["hflip"]:
image = image.transpose(Image.FLIP_LEFT_RIGHT)
if "rotate" in ele and ele["rotate"]:
angle = random.choice([-15, -10, -5, 5, 10, 15])
image = image.rotate(angle)
if "resized_height" in ele and "resized_width" in ele:
resized_height, resized_width = smart_resize(
ele["resized_height"],
ele["resized_width"],
factor=size_factor,
)
else:
width, height = image.size
min_pixels = ele.get("min_pixels", MIN_PIXELS)
max_pixels = ele.get("max_pixels", MAX_PIXELS)
if max_pixels is None:
max_pixels = MAX_PIXELS
resized_height, resized_width = smart_resize(
height,
width,
factor=size_factor,
min_pixels=min_pixels,
max_pixels=max_pixels,
)
image = image.resize((resized_width, resized_height))
return image
def smart_nframes(ele: dict, total_frames: int, video_fps: int | float) -> int:
assert not ("fps" in ele and "nframes" in ele), "Only accept either `fps` or `nframes`"
if "nframes" in ele:
nframes = round_by_factor(ele["nframes"], FRAME_FACTOR)
else:
fps = ele.get("fps", FPS)
min_frames = ceil_by_factor(ele.get("min_frames", FPS_MIN_FRAMES), FRAME_FACTOR)
max_frames = floor_by_factor(ele.get("max_frames", min(FPS_MAX_FRAMES, total_frames)), FRAME_FACTOR)
nframes = total_frames / video_fps * fps
if nframes > total_frames:
logger.warning(f"smart_nframes: nframes[{nframes}] > total_frames[{total_frames}]")
nframes = min(min(max(nframes, min_frames), max_frames), total_frames)
nframes = floor_by_factor(nframes, FRAME_FACTOR)
if not (FRAME_FACTOR <= nframes and nframes <= total_frames):
raise ValueError(f"nframes should in interval [{FRAME_FACTOR}, {total_frames}], but got {nframes}.")
return nframes
def fetch_video(ele: dict, image_factor: int = IMAGE_FACTOR, return_video_sample_fps: bool = False):
if isinstance(ele["video"], str):
video, audio, info = torchvision.io.read_video(
ele["video"],
start_pts=ele.get("video_start", 0.0),
end_pts=ele.get("video_end", None),
pts_unit="sec",
output_format="TCHW",
)
total_frames, video_fps = video.size(0), info["video_fps"]
nframes = smart_nframes(ele, total_frames=total_frames, video_fps=video_fps)
idx = torch.linspace(0, total_frames - 1, nframes).round().long()
sample_fps = nframes / max(total_frames, 1e-6) * video_fps
video = video[idx]
nframes, _, height, width = video.shape
min_pixels = ele.get("min_pixels", VIDEO_MIN_PIXELS)
total_pixels = ele.get("total_pixels", VIDEO_TOTAL_PIXELS)
max_pixels = max(min(VIDEO_MAX_PIXELS, total_pixels / nframes * FRAME_FACTOR), int(min_pixels * 1.05))
max_pixels_supposed = ele.get("max_pixels", max_pixels)
max_pixels = min(max_pixels_supposed, max_pixels)
if "resized_height" in ele and "resized_width" in ele:
resized_height, resized_width = smart_resize(ele["resized_height"], ele["resized_width"], factor=image_factor)
else:
resized_height, resized_width = smart_resize(height, width, factor=image_factor, min_pixels=min_pixels, max_pixels=max_pixels)
video = transforms.functional.resize(video, [resized_height, resized_width], interpolation=InterpolationMode.BICUBIC, antialias=True).float()
if return_video_sample_fps:
return video, sample_fps
return video
else:
assert isinstance(ele["video"], (list, tuple))
process_info = ele.copy()
process_info.pop("type", None)
process_info.pop("video", None)
images = [fetch_image({"image": video_element, **process_info}, size_factor=image_factor) for video_element in ele["video"]]
nframes = ceil_by_factor(len(images), FRAME_FACTOR)
if len(images) < nframes:
images.extend([images[-1]] * (nframes - len(images)))
if return_video_sample_fps:
return images, process_info.pop("fps", 2.0)
return images
def extract_vision_info(conversations: list[dict] | list[list[dict]]) -> list[dict]:
vision_infos = []
if isinstance(conversations[0], dict):
conversations = [conversations]
for conversation in conversations:
for message in conversation:
if isinstance(message["content"], list):
for ele in message["content"]:
if (
"image" in ele
or "image_url" in ele
or "video" in ele
or ele["type"] in ("image", "image_url", "video")
):
vision_infos.append(ele)
return vision_infos
def process_vision_info(
conversations: list[dict] | list[list[dict]],
return_video_kwargs: bool = False,
):
vision_infos = extract_vision_info(conversations)
image_inputs = []
video_inputs = []
video_sample_fps_list = []
for vision_info in vision_infos:
if "image" in vision_info or "image_url" in vision_info:
image_inputs.append(fetch_image(vision_info))
elif "video" in vision_info:
video_input, video_sample_fps = fetch_video(vision_info, return_video_sample_fps=True)
video_sample_fps_list.append(video_sample_fps)
video_inputs.append(video_input)
else:
raise ValueError("image, image_url or video should in content.")
if len(image_inputs) == 0:
image_inputs = None
if len(video_inputs) == 0:
video_inputs = None
if return_video_kwargs:
return image_inputs, video_inputs, {'fps': video_sample_fps_list}
return image_inputs, video_inputs
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