高效处理超大图像一次存结果复用及OpenCV拼接性能优化咨询

我正在重构OpenCV Stitcher API的stitching_detailed.py文件，程序支持用户自主选择待拼接的图像数量。我的使用场景是拼接单张最高可达300MP的历史高分辨率TIF扫描件，运行过程中很容易触发cv::OutOfMemoryError错误，因此内存管理是核心需求，同时需要在计算耗时和内存占用之间取得最优平衡。

我的拼接工作流在不同阶段需要用到不同分辨率的图像：

• 第一阶段用中分辨率图像做预估
• 第二阶段用低分辨率图像做其他预估
• 最终阶段用全分辨率图像完成拼接

当前的几种实现方案各有优劣，我做了如下性能测试：

性能测试基准代码 #

import time
from functools import wraps
import tracemalloc
import cv2
import numpy as np

def performance_test(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        tracemalloc.start()
        start = time.time()
        result = func(*args, **kwargs)
        current, peak = tracemalloc.get_traced_memory()
        print(f"Peak was {peak / 10**6} MB")
        end = time.time()
        print(f"Time was {end - start} s")
        tracemalloc.stop()
        return result
    return wrapper

测试用虚拟图像生成代码 #

img_names = ["dummy1.png", "dummy2.png", "dummy3.png"]
image = np.zeros((10000, 10000, 3), np.uint8)
image[:] = (0, 0, 255)
for img_name in img_names:
    cv2.imwrite(img_name, image)

方案1：按需加载（低内存，高耗时） #

用到对应分辨率图像时才读取原图生成，内存占用最低，但需要反复读取原图，耗时最高：

def process_medium_resolution_images(img_names):
    for big_image in img_names:
        high = cv2.imread(big_image)
        medium = cv2.resize(high, (5000, 5000))
        print(medium.shape)

def process_low_resolution_images(img_names):
    for big_image in img_names:
        high = cv2.imread(big_image)
        low = cv2.resize(high, (1000, 1000))
        print(low.shape)

def process_high_resolution_images(img_names):
    for big_image in img_names:
        high = cv2.imread(big_image)
        print(high.shape)

@performance_test
def main(img_names):
    process_medium_resolution_images(img_names)
    process_low_resolution_images(img_names)
    process_high_resolution_images(img_names)

main(img_names)
# 测试结果：内存峰值675.15MB，耗时8.50s

方案2：全量缓存（低耗时，高内存） #

一次性读取所有原图并生成所有分辨率的图像存入内存，速度最快，但内存占用远超阈值，不符合使用需求：

def img_reading_and_processing(img_names):
    high_imgs, medium_imgs, low_imgs = [], [], []
    for big_image in img_names:
        high = cv2.imread(big_image)
        high_imgs.append(high)
        medium = cv2.resize(high, (5000, 5000))
        medium_imgs.append(medium)
        low = cv2.resize(high, (1000, 1000))
        low_imgs.append(low)
    return high_imgs, medium_imgs, low_imgs

def process_imgs(imgs):
    for img in imgs:
        print(img.shape)

@performance_test
def main2(img_names):
    high_imgs, medium_imgs, low_imgs = img_reading_and_processing(img_names)
    process_imgs(medium_imgs)
    process_imgs(low_imgs)
    process_imgs(high_imgs)

main2(img_names)
# 测试结果：内存峰值1134.16MB，耗时2.83s

方案3：中间结果落盘（折中） #

参考社区建议，将生成的中、低分辨率图像写入磁盘，后续阶段直接读取中间文件，内存占用和耗时都处于折中水平：

def img_reading_and_processing(img_names):
    for idx, big_image in enumerate(img_names):
        high = cv2.imread(big_image)
        medium = cv2.resize(high, (5000, 5000))
        low = cv2.resize(high, (1000, 1000))
        cv2.imwrite(f"medium{idx}.png", medium)
        cv2.imwrite(f"low{idx}.png", low)

def process_medium_resolution_images2(img_names):
    for idx, img in enumerate(img_names):
        medium = cv2.imread(f"medium{idx}.png")
        print(medium.shape)

def process_low_resolution_images2(img_names):
    for idx, img in enumerate(img_names):
        low = cv2.imread(f"low{idx}.png")
        print(low.shape)

@performance_test
def main3(img_names):
    process_medium_resolution_images2(img_names)
    process_low_resolution_images2(img_names)
    process_high_resolution_images(img_names)

main3(img_names)
# 测试结果：内存峰值678.15MB，耗时6.77s

期望找到能结合两种方案优势的实现方法，在内存不超阈值的前提下尽可能降低计算耗时。

内容的提问来源于stack exchange，提问作者Lukas Weber