apps/FastDeploy
1
0
Fork 0
mirror of https://github.com/PaddlePaddle/FastDeploy.git synced 2026-04-24 09:44:10 +08:00
Code Issues Actions 9 Packages Projects Releases Wiki Activity

[Optimization] Deduplicate shared image/video utilities across VL processors (#6988)

Browse Source 
* step1~3

* fix import path

* 删除重复代码

* 删除重复代码

* 删除重复代码

* fix import path

* update

* fix import path

* add unit test

* fix

* update

* fix unit test
This commit is contained in:
luukunn
2026-03-26 09:49:33 +08:00
committed by GitHub
parent 1502b6f43e
commit d5cb2767d7
16 changed files with 882 additions and 593 deletions
Download Patch File Download Diff File Expand all files Collapse all files
fastdeploy/input/qwen3_vl_processor/image_processor.py
+1 -81
View File
@@ -14,7 +14,6 @@
# limitations under the License.
"""
import math
from typing import List, Optional, Union
import numpy as np
@@ -41,6 +40,7 @@ from paddleformers.transformers.image_utils import (
from paddleformers.transformers.legacy.tokenizer_utils_base import TensorType
from PIL import Image
from fastdeploy.input.image_processors.common import is_scaled_image, smart_resize
from fastdeploy.utils import data_processor_logger
IMAGE_MEAN = [0.5, 0.5, 0.5]
@@ -62,86 +62,6 @@ VideoInput = Union[
]
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, min_pixels: int, max_pixels: int, max_ratio: int = 200):
"""
Smart image resizing that maintains aspect ratio and respects constraints.
Args:
height: Original image height
width: Original image width
factor: Patch size factor
min_pixels: Minimum allowed pixels
max_pixels: Maximum allowed pixels
max_ratio: Maximum allowed aspect ratio
Returns:
tuple: (new_height, new_width)
Raises:
ValueError: If calculated dimensions are invalid
"""
if max(height, width) / min(height, width) > max_ratio:
if height > width:
new_width = max(factor, round_by_factor(width, factor))
new_height = floor_by_factor(new_width * max_ratio, factor)
else:
new_height = max(factor, round_by_factor(height, factor))
new_width = floor_by_factor(new_height * max_ratio, factor)
data_processor_logger.info(
f"absolute aspect ratio must be smaller than {max_ratio}, got {max(height, width) / min(height, width)},\
resize to {max(new_height, new_width) / min(new_height, new_width)}"
)
height = new_height
width = new_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 min_pixels > h_bar * w_bar or h_bar * w_bar > max_pixels:
raise ValueError(f"encounter invalid h_bar: {h_bar}, w_bar: {w_bar}")
return h_bar, w_bar
def is_scaled_image(image: np.ndarray) -> bool:
"""
Check if image pixel values are already normalized to [0, 1] range.
Args:
image: Input image array
Returns:
bool: True if image is already scaled
"""
if image.dtype == np.uint8:
return False
# It's possible the image has pixel values in [0, 255] but is of floating type
return np.min(image) >= 0 and np.max(image) <= 1
class ImageProcessor(BaseImageProcessor):
"""
Adaptive image processor for dynamic image resizing and preprocessing.