Hello fellow learner! Today we will learn about detecting contours in an image. Contours are defined as refined boundaries of objects and can be really helpful in detecting objects.
Recommended read: How to detect edges in Python?
Detecting Contours using Python
So let’s get started with Detecting Contours for images using the OpenCV library in Python.
1. Importing Modules
First, we import the necessary modules which include OpenCV and matplotlib to plot the images on the screen.
import cv2
import matplotlib.pyplot as plt
2. Loading the image into the program
The next step includes loading the image from the file directory to our program using the imread function and then converting the image into RGB format.
We will plot the images in form of a subplot where the first image is the original loaded image. The code for the same is shown below.
loaded_img = cv2.imread("image.jpg")
loaded_img = cv2.cvtColor(loaded_img, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(20,20))
plt.subplot(2,2,1)
plt.title("Original Image")
plt.imshow(loaded_img)
plt.axis("off")
3. Converting image to GrayScale
For better detection of contours, we convert the image to a grayscale image by using cvtColor function. After converting the image to grayscale we plot it on the second subplot on the main plot.
gray_image = cv2.cvtColor(loaded_img, cv2.COLOR_RGB2GRAY)
plt.subplot(2,2,2)
plt.title("Grayscale Image")
plt.imshow(gray_image,cmap="gray")
plt.axis("off")
4. Getting Binary Image
Next we convert the image into a binary image as it makes the image processing much much easier as it removes the unnecessary items from the image and focuses on the important objects only.
The code for the same is shown below. We will be plotting the binary image on the third slot on the main plot.
_, binary_img = cv2.threshold(gray_image, 225, 255, cv2.THRESH_BINARY_INV)
plt.subplot(2,2,3)
plt.title("Binary Image")
plt.imshow(binary_img,cmap="gray")
plt.axis("off")
5. Detecting Contours
The final step is to detect contours using the findContours method of openCV library and then we draw the contours on the image.
We then plot all the images in the subplot, the code for the same is shown below.
contours, hierarchy = cv2.findContours(binary_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
final_image = cv2.drawContours(loaded_img, contours, -1, (0, 255, 0), 2)
plt.subplot(2,2,4)
plt.title("Contours detected Image")
plt.imshow(final_image,cmap="gray")
plt.axis("off")
The Output Plot
The final output of the whole procedure explained above is shown below. You can see that the results are very accurate.
The Final Code for Detecting Contours
import cv2
import matplotlib.pyplot as plt
loaded_img = cv2.imread("image1.png")
loaded_img = cv2.cvtColor(loaded_img, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(20,20))
plt.subplot(2,2,1)
plt.title("Original Image")
plt.imshow(loaded_img)
plt.axis("off")
gray_image = cv2.cvtColor(loaded_img, cv2.COLOR_RGB2GRAY)
plt.subplot(2,2,2)
plt.title("Grayscale Image")
plt.imshow(gray_image,cmap="gray")
plt.axis("off")
_, binary_img = cv2.threshold(gray_image, 225, 255, cv2.THRESH_BINARY_INV)
plt.subplot(2,2,3)
plt.title("Binary Image")
plt.imshow(binary_img,cmap="gray")
plt.axis("off")
contours, hierarchy = cv2.findContours(binary_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
final_image = cv2.drawContours(loaded_img, contours, -1, (0, 255, 0), 2)
plt.subplot(2,2,4)
plt.title("Contours detected Image")
plt.imshow(final_image,cmap="gray")
plt.axis("off")
plt.savefig('Contour_Detection_output_2.png', dpi = 1000,bbox_inches = 'tight')
plt.tight_layout()
plt.show()
I also tried the same code for a different image. The results are displayed below.
Conclusion
Congratulations! Now you are one step closer to detecting objects from images. We learned to detect the proper boundaries of the objects today!
Keep learning! Happy coding!
Thank you for reading!