如何在Python中使用OpenCV执行模糊检测-FlyAI

如何在Python中使用OpenCV执行模糊检测

目标检测

最后更新 2020-10-12 14:23 阅读 11189

最后更新 2020-10-12 14:23

阅读 11189

目标检测

##FlyAI文献翻译
**英文原文：**[：How to perform Blur Detection using OpenCV in Python](https://www.analyticsvidhya.com/blog/2020/09/how-to-perform-blur-detection-using-opencv-in-python/)
> **标签**：目标检测

**Introduction**

In the corona days (still not over) I would walk near my house for an hour a day and come back home. One day, when I went out for a walk again, I thought of unnecessary photos on my phone. Thousands of photos from university Whatshap groups. It would take me a long time to find unnecessary photos among them. But I had an idea to lighten this workload.

>**介绍**

>在电晕的日子里（还没结束），我每天都会在我家附近走一个小时，然后回家。有一天，当我再次出去散步的时候，我想到了手机里不必要的照片。大学Whashap群里的几千张照片。要在其中找出不必要的照片，我得花很长时间。但我有一个想法，可以减轻这个工作量。

![](https://static.leiphone.com/uploads/new/sns/article/202009/1601195242210503.jpg)

I probably have nearly ten thousand photos in my gallery. Of these, I thought at least 300–400 photos were blurry (there are 378 blurry photos in my gallery). “How can I handle this?” When I thought, it came to my mind that I was a software engineer. I immediately finished my walk and went back home and sat down at my computer and made a small project called “Blur Detection” that I will tell you below.

>我的图库里大概有近万张照片。其中，我认为至少有300-400张照片是模糊的（我的图库里有378张模糊的照片）。"我该怎么处理这个问题呢？" 我一想，我想到了我是一个软件工程师。我马上走完了路，回到家里，坐在电脑前，做了一个小项目，叫 "模糊检测"，下面我就告诉大家。

**Part 1: Logic**

After about 3 hours, I had written 117 lines of code and the algorithm was not working as I wanted. Realizing that he couldn’t handle it like that, I started to think a little. I thought of a topic that had been covered in the lesson about 3 years ago: “filter of the Laplacian”.

>**第一部分: 逻辑**

>大约3个小时后，我写了117行代码，算法却不能如我所愿。意识到自己不能这样处理，我开始有点思考。我想到了3年前课上讲过的一个题目。"Laplacian的过滤器"。

![](https://static.leiphone.com/uploads/new/sns/article/202009/1601195081423955.png)

The Laplace filter is mainly used to define the edge lines in a picture. What is meant here by the edge are the sharp color separations that usually separate objects from the background. The Laplace filter, also known as the Sharpening Filter, uses a window while operating. You can understand the event better with the short video below.

For example, below is a 3 × 3 (3 * 3) window and a coefficient value in each cell that can be used during this process:

> 拉普拉斯滤镜主要用于定义图片中的边缘线。这里所说的边缘是指通常将物体与背景分开的尖锐的颜色分离。拉普拉斯滤镜也叫锐化滤镜，在操作的时候会使用一个窗口。通过下面的短视频，你可以更好的理解这个事件。

>例如，下面是一个3×3(3*3)的窗口和每个单元中的系数值，在这个过程中可以使用。

```
1  1 11 -8 11  1 1
```

In this window above, the pixel value in each cell is simply multiplied by the coefficient in that cell, and the results are added to the new value of the pixel in the middle.
For example, consider a matrix whose current values are given below:

>在上面这个窗口中，将每个单元格中的像素值简单地乘以该单元格中的系数，然后将结果加到中间像素的新值上。

>例如，考虑一个矩阵，其当前值如下。

```
4 5 63 3 82 1 7
```

The new value in the middle of this matrix is calculated by the formula:
1 x 4 + 1 x 5 + 1 x 6 + 1 x 3 + -8 x 3 + 1 x 8 + 1 x 2 + 1 x 1 + 1 x 7 = 12

Filtered matrix:

>这个矩阵中间的新值由公式计算。
1×4+1×5+1×6+1×3+-8×3+1×8+1×2+1×1+1×7=12。

>过滤后的矩阵。

```
4 5 63 12 82 1 7
```

found as. Below is a normal version of a picture with a Laplace filter applied:

>被发现作为：下面是一张应用了拉普拉斯滤镜的普通版图片。

![](https://static.leiphone.com/uploads/new/sns/article/202009/1601195080905486.png)

![](https://static.leiphone.com/uploads/new/sns/article/202009/1601195080274206.png)

**Part 2: Application**

First, let’s import the necessary libraries.

>**第二部分：应用**

>首先，让我们导入必要的库。

```
import cv2
import argparse
import glob
```

Let’s open a folder called “images” that contains enough photos. Photos:

>让我们打开一个名为 "图片 "的文件夹，里面有足够多的照片。

![](https://static.leiphone.com/uploads/new/sns/article/202009/1601195081376024.png)

Let’s go back to coding now. Let’s make the necessary adjustments with the “argparse” package.

>现在让我们回到编码中去。让我们用 "argparse "包进行必要的调整。

```
ap = argparse.ArgumentParser()
ap.add_argument('-i', '--images', required=True,)
ap.add_argument('-t', '--threshold', type=float)
args = vars(ap.parse_args())
```

To keep the images in the folder in a single array, we write the following code:

>为了将文件夹中的图片保存在一个数组中，我们编写以下代码。

```
images = [cv2.imread(file) for file in glob.glob("{}/*.jpeg".format(args['images']))]
```

Now it is time to take the pictures in the folder one by one and apply the Laplacian method to find blur.

>现在要把文件夹里的图片一张一张的拍下来，应用拉普拉斯法寻找模糊。

```
for image in images:
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	fm = cv2.Laplacian(gray, cv2.CV_64F).var()
	text = "Not Blurry"

if fm < args["threshold"]:
		text = "Blurry"

cv2.putText(image, "{}: {:.2f}".format(text, fm), (10, 30),
		cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 3)
	cv2.imshow("Image", image)
	cv2.waitKey(0)
```

1.line: We start a loop to rotate the photos in the folder one by one.

2.line: We turn the photo into grayscale.

3.line: We use the Laplacian method. As a result, a float type number will be returned. (For example 4.312563 or 764.471094)

6.line: We compare the result inline 3 with the threshold value we have determined. If the result is below the threshold value, we perceive it as “blurry”. In general, the threshold value gives very good results at 100.

>1.我们开始一个循环，把文件夹里的照片一张张地旋转。

>2.我们把照片变成灰度。

>3.我们使用Laplacian方法。我们使用Laplacian方法。结果，将返回一个浮动类型的数字。(例如4.312563或764.471094)

>6.我们将第3行的结果与我们确定的阈值进行比较。如果结果低于阈值，我们认为它是 "模糊 "的。一般来说，阈值在100的时候会有非常好的结果。

Type the following on the console screen for it to run

```
python blur_detection.py -i images -t 100
```

And it finished. We did a little project for blur detection using OpenCV so easily.

>并且它完成了。我们用OpenCV做了一个模糊检测的小项目，如此简单。

项目Github：https://github.com/Furkan-Gulsen/OpenCV-Projects/tree/master/Blur%20Detection
WebApp Github: https://github.com/Furkan-Gulsen/Blur-Detection-Web-App

本作品采用知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议进行许可，转载请附上原文出处链接和本声明。
本文链接地址：https://flyai.com/article/713

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