# 查找已安装的 OpenCV 版本
pip list | grep opencv
pip show opencv-python opencv-contrib-python

# 卸载所有版本后重装
pip uninstall opencv-python opencv-contrib-python \
              opencv-python-headless opencv-contrib-python-headless -y
pip install opencv-contrib-python

import cv2

# 问题：imread 失败不抛异常
img = cv2.imread("nonexistent.jpg")
print(img)  # None
print(img.shape)  # AttributeError: 'NoneType' has no attribute 'shape'

# 解决方案 1：防御性检查
def safe_read(path, flags=cv2.IMREAD_COLOR):
    img = cv2.imread(path, flags)
    if img is None:
        raise FileNotFoundError(f"无法读取: {path}")
    return img

# 解决方案 2：检查文件存在
import os
if not os.path.exists(path):
    print(f"文件不存在: {path}")

# 解决方案 3：中文路径问题（Windows）
# imread 不支持中文路径
img = cv2.imdecode(
    np.fromfile("中文路径/image.jpg", dtype=np.uint8),
    cv2.IMREAD_COLOR
)

# 问题：Matplotlib 显示 OpenCV 图像颜色异常
# 原因：OpenCV 是 BGR，Matplotlib 是 RGB
import cv2
import matplotlib.pyplot as plt

img = cv2.imread("photo.jpg")
# 错误：plt.imshow(img)  # 蓝绿色反转
# 正确：
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.show()

# 保存时也注意格式
cv2.imwrite("output.png", img)  # 保持 BGR，用 OpenCV 打开正常
# 如果要用 PIL 保存：
from PIL import Image
Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)).save("output.png")

# 错误：忘记释放资源
cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    # ... 处理 ...
# 缺少 cap.release() — 内存泄漏！

# 正确：使用上下文管理器或 finally
cap = cv2.VideoCapture(0)
try:
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        # ... 处理 ...
finally:
    cap.release()
    cv2.destroyAllWindows()

# 更好：使用自定义上下文管理器
class VideoCapture:
    def __init__(self, *args):
        self.cap = cv2.VideoCapture(*args)
    def __enter__(self):
        return self.cap
    def __exit__(self, *args):
        self.cap.release()
        cv2.destroyAllWindows()

# 使用
with VideoCapture(0) as cap:
    while True:
        ret, frame = cap.read()

import cv2
import numpy as np
import gc

# 问题：同时加载大量大图像
images = [cv2.imread(f"large_{i}.tiff") for i in range(100)]  # 100 张 4K = ~12GB

# 解决方案：逐张处理
for i in range(100):
    img = cv2.imread(f"large_{i}.tiff")
    result = process(img)
    cv2.imwrite(f"output_{i}.tiff", result)
    del img, result  # 显式释放
    gc.collect()     # 垃圾回收

# 使用生成器
def image_generator(paths):
    for path in paths:
        yield cv2.imread(path)

for img in image_generator(file_list):
    process(img)

import psutil
import os

def get_memory_usage():
    process = psutil.Process(os.getpid())
    return process.memory_info().rss / 1024**2  # MB

print(f"初始内存: {get_memory_usage():.0f} MB")
img = cv2.imread("large.jpg")
print(f"读取后: {get_memory_usage():.0f} MB")
del img
print(f"释放后: {get_memory_usage():.0f} MB")

import cv2
import time

class Timer:
    def __init__(self, name=""):
        self.name = name
    def __enter__(self):
        self.start = time.perf_counter()
        return self
    def __exit__(self, *args):
        self.elapsed = (time.perf_counter() - self.start) * 1000
        print(f"{self.name}: {self.elapsed:.2f} ms")

# 使用
with Timer("读取"):
    img = cv2.imread("photo.jpg")
with Timer("灰度"):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
with Timer("高斯模糊"):
    blurred = cv2.GaussianBlur(gray, (15, 15), 0)
with Timer("Canny"):
    edges = cv2.Canny(blurred, 50, 150)

import cv2

# 启用 OpenCV 性能分析
cv2.setUseOptimized(True)
print(f"优化已启用: {cv2.useOptimized()}")

# 使用 TickMeter
tm = cv2.TickMeter()
tm.start()
# ... 处理 ...
tm.stop()
print(f"时间: {tm.getTimeMilli():.2f} ms")
print(f"Tick 数: {tm.getTicks()}")

import cv2
import numpy as np
import time

img = cv2.imread("photo.jpg")
h, w = img.shape[:2]

# ❌ 慢：逐像素操作
start = time.perf_counter()
result = np.zeros_like(img)
for y in range(h):
    for x in range(w):
        result[y, x] = img[y, x] * 0.5
print(f"循环: {(time.perf_counter()-start)*1000:.0f} ms")

# ✅ 快：NumPy 向量化
start = time.perf_counter()
result = (img * 0.5).astype(np.uint8)
print(f"向量化: {(time.perf_counter()-start)*1000:.0f} ms")

# ✅ 更快：OpenCV 函数
start = time.perf_counter()
result = cv2.convertScaleAbs(img, alpha=0.5, beta=0)
print(f"OpenCV: {(time.perf_counter()-start)*1000:.0f} ms")

def debug_image(img, name="image"):
    """图像调试信息"""
    if img is None:
        print(f"[{name}] NULL")
        return
    print(f"[{name}] shape={img.shape}, dtype={img.dtype}, "
          f"range=[{img.min()}, {img.max()}], "
          f"mean={img.mean():.1f}, "
          f"size={img.nbytes / 1024:.1f} KB")

# 使用
debug_image(img, "input")
debug_image(edges, "edges")

def visualize_pipeline(stages, names=None):
    """将多个处理阶段拼接显示"""
    if names is None:
        names = [f"Stage {i}" for i in range(len(stages))]

    # 统一为三通道
    images = []
    for img in stages:
        if len(img.shape) == 2:
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
        images.append(img)

    # 调整为相同大小
    max_h = max(img.shape[0] for img in images)
    max_w = max(img.shape[1] for img in images)
    resized = []
    for img in images:
        if img.shape[:2] != (max_h, max_w):
            img = cv2.resize(img, (max_w, max_h))
        resized.append(img)

    # 拼接
    result = np.hstack(resized)
    cv2.imshow("Pipeline", result)
    cv2.waitKey(0)

import os

class DebugSaver:
    def __init__(self, output_dir="debug_output"):
        os.makedirs(output_dir, exist_ok=True)
        self.dir = output_dir
        self.counter = 0

    def save(self, img, name=None):
        if name is None:
            name = f"{self.counter:04d}"
        path = os.path.join(self.dir, f"{name}.png")
        cv2.imwrite(path, img)
        self.counter += 1
        print(f"[Debug] 保存: {path}")

# 使用
dbg = DebugSaver("debug_output")
dbg.save(img, "01_input")
dbg.save(edges, "02_edges")
dbg.save(result, "03_result")

import cv2
import numpy as np

def robust_imread(path, flags=cv2.IMREAD_COLOR):
    """健壮的图像读取"""
    if not os.path.exists(path):
        raise FileNotFoundError(f"文件不存在: {path}")

    # 中文路径支持
    try:
        img = cv2.imdecode(
            np.fromfile(path, dtype=np.uint8), flags
        )
    except Exception:
        img = cv2.imread(path, flags)

    if img is None:
        raise IOError(f"无法解码图像: {path}")

    return img

def safe_process(img, process_func, fallback=None):
    """安全执行图像处理"""
    try:
        result = process_func(img)
        if result is None:
            return fallback
        return result
    except cv2.error as e:
        print(f"OpenCV 错误: {e}")
        return fallback
    except Exception as e:
        print(f"处理错误: {e}")
        return fallback

import cv2
import logging

# 设置日志级别
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger("opencv_debug")

# 条件断言
def assert_image_valid(img, name="image"):
    assert img is not None, f"{name} is None"
    assert img.size > 0, f"{name} is empty"
    assert img.dtype == np.uint8, f"{name} dtype={img.dtype}, expected uint8"
    logger.debug(f"{name}: shape={img.shape}, dtype={img.dtype}")

# 使用
img = cv2.imread("photo.jpg")
assert_image_valid(img, "input")