使用 YOLOv 和 OpenCV 從圖像中提取所有檢測到的物件並將它們保存為單獨文件的簡單方法

目錄

簡介
範例圖片
使用 YOLOv8 偵測物體
    有關不同 YOLOv8 模型的更多資訊
    運作模型來偵測物件
解析檢測結果
    擷取有背景的物件
    擷取沒有背景的物件
結論

介紹

在本教程中，我將展示如何使用神經網路檢測圖像上的所有對象，提取它們並保存到單獨的檔案中。

這是一項常見任務，有許多不同的選項可以完成此任務。在本文中，我將展示一種非常簡單的方法，使用 YOLOv8 神經網路和 OpenCV。

本教學僅涵蓋此主題，因此如果您想深入了解 YOLOv8 神經網路和電腦視覺，請閱讀我的 YOLOv8 系列之前的文章。

我將使用Python來編寫本文中的所有程式碼，因此，我假設您能夠使用Python進行開發。另外，我使用 Jupyter Notebook，但這不是必需的。您可以使用任何 IDE 或文字編輯器來編寫和執行程式碼。

範例圖片

在本教程中，我們將使用 YOLOv8 神經網路和 OpenCV 檢測並提取影像中的物件。作為範例圖像，我們將使用我從維基百科頁面獲得的以下圖像：

資料來源：https://en.wikipedia.org/wiki/Vehicle_cycling

我們將檢測該影像上的所有人和汽車，將其提取並保存到單獨的影像檔案中。我將展示如何保存提取的帶或不帶背景的物件。您可以根據需要選擇第一個或第二個選項。

使用 YOLOv8 偵測物體

那麼，讓我們開始吧。您需要做的第一個操作是安裝 YOLOv8.2 軟體包（如果您還沒有）。為此，請在 Jupyter 筆記本中執行以下命令：

%pip install ultralytics

然後導入YOLOv8 API：

from ultralytics import YOLO

完成後，我們來載入YOLOv8神經網路模型：

model = YOLO("yolov8m-seg.pt")

這行程式碼將下載yolov8m-seg.pt神經網路模型並載入到模型變數中。

有關不同 YOLOv8 模型的更多信息

在本教程中，我們將使用預先訓練的 YOLOv8 模型之一，該模型可用於偵測 80 個常見物件類別。 YOLOv8 模型共有 3 種類型和 5 種不同的尺寸。

Classification	Detection	Segmentation	Kind
yolov8n-cls.pt	yolov8n.pt	yolov8n-seg.pt	Nano
yolov8s-cls.pt	yolov8s.pt	yolov8s-seg.pt	Small
yolov8m-cls.pt	yolov8m.pt	yolov8m-seg.pt	Medium
yolov8l-cls.pt	yolov8l.pt	yolov8l-seg.pt	Large
yolov8x-cls.pt	yolov8x.pt	yolov8x-seg.pt	Huge

The bigger model you choose, the more quality results you'll get, but the slower it works.

There are three types of YOLOv8 models exist: for classification, for object detection and for instance segmentation. The classification models used only to detect a class of object on the image, so it can't be used for our task. The object detection models can detect bounding boxes of detected objects. These models can be used to get x1,y1,x2,y2 coordinates of each object, and you can use this coordinates to extract the object with background. Finally, segmentation models can be used to detect not only bounding boxes of the objects, but also exact shapes (bounding polygons) for them. Using bounding polygon, you can extract an object without background.

In the code above, I've loaded the middle-sized model for segmentation yolov8m-seg.pt, that can be used both to extract object with background and without it.

To detect specific object classes, that do not exist in pretrained models, you can create and train your own model, save it to the .pt file and load it. Read the first part of my YOLOv8 series to learn how to do this.

Run the model to detect objects

To detect objects on images, you can pass the list of image file names to the model object and receive the array of results for each image:

results = model(["road.jpg"])

This code assumes, that the sample image saved to the road.jpg file. If you send a single image in the list, the results array will contain a single element. You can send more and in this case, the result will contain more elements.

Parse detection results

Now, let's get detection results for the first image (road.jpg)

result = results[0]

The result is an object of the ultralytics.engine.results.Results class, which contains different information about detected objects on the image.

You can use a link above to learn more about all methods and properties, that this object contains, but here we need only few of them:

• result.boxes.xyxy - array of bounding boxes for all objects, detected on the image.

• result.masks.xy - array of bounding polygons for all objects, detected on the image.

For example, the result.boxes.xyxy[0] will contain [x1,y1,x2,y2] coordinates for the first object, detected on the image:

print(result.boxes.xyxy[0])

tensor([2251.1409, 1117.8158, 3216.7141, 1744.1128], device='cuda:0')

and the bounding polygon for the same object:

print(result.masks.xy[0])

[[       2500        1125]
 [     2493.8      1131.2]
 [     2481.2      1131.2]
 [       2475      1137.5]
 [     2468.8      1137.5]
 [     2462.5      1143.8]
 [     2456.2      1143.8]
 [     2418.8      1181.2]
 [     2418.8      1187.5]
 [     2381.2        1225]
 [     2381.2      1231.2]
 [       2350      1262.5]
 [       2350      1268.8]
 [     2337.5      1281.2]
 [     2337.5      1287.5]
 [       2325        1300]
 [       2325      1306.2]
 [     2306.2        1325]
 [     2306.2      1331.2]
 [       2300      1337.5]
 [       2300      1343.8]
 [     2287.5      1356.2]
 [     2287.5      1362.5]
 [     2281.2      1368.8]
 [     2281.2      1387.5]
 [       2275      1393.8]
 [       2275        1700]
 [     2281.2      1706.2]
 [     2281.2      1712.5]
 [     2287.5      1718.8]
 [     2356.2      1718.8]
 [     2368.8      1706.2]
 [     2368.8        1700]
 [     2381.2      1687.5]
 [     2381.2      1681.2]
 [     2393.8      1668.8]
 [     2393.8      1662.5]
 [     2412.5      1643.8]
 [     2456.2      1643.8]
 [     2462.5        1650]
 [     2468.8        1650]
 [     2481.2      1662.5]
 [     2562.5      1662.5]
 [     2568.8      1656.2]
 [       2575      1656.2]
 [     2581.2        1650]
 [     2712.5        1650]
 [     2718.8      1656.2]
 [     2737.5      1656.2]
 [     2743.8      1662.5]
 [     2768.8      1662.5]
 [       2775      1668.8]
 [     2831.2      1668.8]
 [     2837.5        1675]
 [     2868.8        1675]
 [       2875      1681.2]
 [     2887.5      1681.2]
 [       2900      1693.8]
 [     2906.2      1693.8]
 [     2912.5        1700]
 [     2918.8        1700]
 [     2931.2      1712.5]
 [     2931.2      1718.8]
 [     2937.5      1718.8]
 [       2950      1731.2]
 [     2956.2      1731.2]
 [     2962.5      1737.5]
 [     3018.8      1737.5]
 [     3018.8      1731.2]
 [     3037.5      1712.5]
 [     3037.5      1706.2]
 [     3043.8        1700]
 [     3043.8      1681.2]
 [       3050        1675]
 [       3050      1668.8]
 [     3056.2      1662.5]
 [     3062.5      1662.5]
 [     3068.8      1668.8]
 [     3081.2      1668.8]
 [       3100      1687.5]
 [     3106.2      1687.5]
 [     3112.5      1693.8]
 [       3175      1693.8]
 [     3181.2      1687.5]
 [     3187.5      1687.5]
 [     3193.8      1681.2]
 [     3193.8      1662.5]
 [       3200      1656.2]
 [       3200      1562.5]
 [     3193.8      1556.2]
 [     3193.8        1500]
 [     3187.5      1493.8]
 [     3187.5      1468.8]
 [     3181.2      1462.5]
 [     3181.2      1437.5]
 [       3175      1431.2]
 [       3175      1418.8]
 [     3168.8      1412.5]
 [     3168.8        1400]
 [     3143.8        1375]
 [     3143.8      1368.8]
 [       3125        1350]
 [       3125      1343.8]
 [     3112.5      1331.2]
 [     3112.5        1325]
 [       3100      1312.5]
 [       3100      1306.2]
 [     3087.5      1293.8]
 [     3087.5      1287.5]
 [       3075        1275]
 [       3075      1268.8]
 [     3068.8      1262.5]
 [     3068.8      1256.2]
 [       3050      1237.5]
 [       3050      1231.2]
 [     3006.2      1187.5]
 [     3006.2      1181.2]
 [       3000        1175]
 [       3000      1168.8]
 [     2993.8      1162.5]
 [     2993.8      1156.2]
 [     2987.5        1150]
 [     2987.5      1143.8]
 [       2975      1143.8]
 [     2968.8      1137.5]
 [       2950      1137.5]
 [     2943.8      1131.2]
 [     2868.8      1131.2]
 [     2862.5        1125]]

This is a list of [x,y] coordinates for all points in the polygon.

You can see below the bounding box and the bounding polygon for the first detected object:

Bounding box	Bounding polygon

As you may assume, to extract the object with background you can use the bounding box, but to extract the object without background, you will need to use the bounding polygon.

To extract all objects and save to separate files, you need to run the code for each detected object in a loop.

In the next sections, I will show how to achieve both.

Extract objects with background

First, I will show how to crop a single object, using coordinates of bounding box. Then, we will write a loop to extract all detected objects.

So, in the previous section, we extracted the bounding box for the first detected object as result.boxes.xyxy[0]. It contains an [x1,y1,x2,y2] array with coordinates. However, this is a PyTorch Tensor with values of Float32 type, but coordinates must be integers. Let's convert the tensor for appropriate coordinates:

x1,y1,x2,y2 = result.boxes.xyxy[0].cpu().numpy().astype(int);

Now, you need to load the image and crop it, using the coordninates above.

I will use a OpenCV library for this. Ensure that it's installed in your system or install it:

%pip install opencv-python

Then import it and load the image:

import cv2

img = cv2.imread("road.jpg")

The OpenCV image is a regular NumPy array. You can see it shape:

print(img.shape)

(604, 800, 3)

The first dimension is a number of rows (height of the image), the second dimensions is a number of columns (width of the image), and the third dimension is a number of color channels, which is 3 for standard RGB images.

Now, it's easy to crop the part of this array, using x1,y1,x2,y2 coordinates that we have:

img[y1:y2,x1:x2,:]

This way you get only rows from y1 to y2 and columns from x1 to x2, e.g. only the object, that is required. Let's save this cropped image to a new file:

cv2.imwrite("image1.png",img[y1:y2,x1:x2,:])

That's all. After running this code, you should see the new file image1.png. If you open it, you should see the cropped object with background:

Now, you can write a loop, to extract and save all detected objects:

for idx,box in enumerate(result.boxes.xyxy):
    x1,y1,x2,y2 = box.cpu().numpy().astype(int)
    cv2.imwrite(f"image{idx}.png", img[y1:y2,x1:x2,:])

After running this, you'll see the files image0.png, image1.png ... etc. with all detected objects on the image.

This is a full solution:

from ultralytics import YOLO
import cv2

model = YOLO("yolov8m-seg.pt")
results = model(["road.jpg"])

result = results[0]

img = cv2.imread("road.jpg")
for idx,box in enumerate(result.boxes.xyxy):
    x1,y1,x2,y2 = box.cpu().numpy().astype(int)
    cv2.imwrite(f"image{idx}.png", img[y1:y2,x1:x2,:])

Extract objects without background

First, to make an image transparent, we need to add a transparency channel to the input image. By default, JPG images do not have this channel, so, to add it using OpenCV you have to run this line of code:

img = cv2.cvtColor(img,cv2.COLOR_BGR2BGRA)

It's easy to cut the image, using the rectangular area, as you seen in the previous section, but I did not find any Python library, that can crop a part of image, using custom polygon.That is why, we will go other way. First, we will make transparent all pixels of the whole input image, that are not in a bounding polygon and then, we will cut the object from this transparent image, using the bounding box, as we did in the previous section.

To implement the first part (make the image transparent), we will need to create and apply a binary mask to the image. The binary mask is a black and white image, on which all pixels that are white treated as pixels, that belong to object and all pixels that are black treated as transparent pixels. For example, the binary mask for the image, that contains the first object will look like this:

OpenCV has a function, that allows to apply the binary mask to the image and this operation makes all pixels on the image transparent, except pixels, that are white on the binary mask.

But first, we need to create a binary mask for OpenCV: the black image with white bounding polygon.

As I said above, the OpenCV image is a NumPy array, so, to create a black binary image, you need to create the NumPy array of the same size as original image, filled with 0.

import numpy as np

mask = np.zeros_like(img,dtype=np.int32)

This code created array with the same size as original road.jpg image, filled with 0. The data type of items in this image must be integer. Now, you need to draw white bounding polygon on it, to make it look the same, as binary mask on the previous image.

The bounding polygon for the first object located in the result.masks.xy[0]. The type of items in this polygon is float32, but for images they must be int32. To convert the polygon to correct type, use the following code:

polygon = result.masks.xy[0].astype(np.int32)

Now, the fillPoly function of the OpenCV library can be used to draw white polygon on the black mask:

cv2.fillPoly(mask,[polygon],color=(255, 255, 255))

Finally, let's make everything except the object transparent on the image, using OpenCV binary AND operation:

img = cv2.bitwise_and(img, img, mask=mask[:,:,0].astype('uint8'))

It applies binary AND operation for each pixel on the img image using the mask. So, all pixels of the image that have 0 on the mask will be transparent.

As a result, of this operation, you will have the following image:

Finally, you can crop the object from it, using the bounding box coordinates, as in previous section:

x1,y1,x2,y2 = result.boxes.xyxy[0].cpu().numpy().astype(int)
cv2.imwrite("image1.png",img[y1:y2,x1:x2,:])

after running this, the image.png file will contain the object, without background:

Now, let's extract all detected objects from the image. To do this, we need to repeat all the code from this section for each detected object in a loop:

for idx,polygon in enumerate(result.masks.xy):
    polygon = polygon.astype(np.int32)
    img = cv2.imread("road.jpg")
    img = cv2.cvtColor(img,cv2.COLOR_BGR2BGRA)
    mask = np.zeros_like(img,dtype=np.int32)
    cv2.fillPoly(mask,[polygon],color=(255, 255, 255))
    x1,y1,x2,y2 = result.boxes.xyxy[idx].cpu().numpy().astype(int)
    img = cv2.bitwise_and(img, img, mask=mask[:,:,0].astype('uint8'))
    cv2.imwrite(f"image{idx}.png", img[y1:y2,x1:x2,:])

After running this code, you should see the image files image0.png, image1.png, image2.png and so on. Each image will have transparent background.

Here is a whole solution to extract all objects from the image with transparent background using YOLOv8.2 and OpenCV and save them to files:

from ultralytics import YOLO
import cv2
import numpy as np

model = YOLO("yolov8m-seg.pt")
results = model(["road.jpg"])

result = results[0]

for idx,polygon in enumerate(result.masks.xy):
    polygon = polygon.astype(np.int32)
    img = cv2.imread("road.jpg")
    img = cv2.cvtColor(img,cv2.COLOR_BGR2BGRA)
    mask = np.zeros_like(img,dtype=np.int32)
    cv2.fillPoly(mask,[polygon],color=(255, 255, 255))
    x1,y1,x2,y2 = result.boxes.xyxy[idx].cpu().numpy().astype(int)
    img = cv2.bitwise_and(img, img, mask=mask[:,:,0].astype('uint8'))
    cv2.imwrite(f"image{idx}.png", img[y1:y2,x1:x2,:])

Conclusion

In this article I showed a simple way to extract and save all detected objects from image using YOLOv8 and OpenCV in less than 20 lines of code. I did not dive to many details in this post. If you want to know more about computer vision and YOLOv8, welcome to read previous articles from my YOLOv8 article series. Links to them you can find either in the beginning or in the end of this article.

You can follow me on LinkedIn, Twitter, and Facebook to know first about new articles like this one and other software development news.

Have a fun coding and never stop learning!

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