Background Tuning Of Images With DeepLab V3 Using Pixellib
If today, I say that you don’t need any software to edit your pictures and can edit by yourself with pre-trained deep learning model in python, how awesome would that be? Let’s see how we can make it. But before that one needs to understand what foreground and background are. Source: Pinterest Required Deep Learning Tools Now we will try to understand the dependency and how can one download the pre-trained model. To download the pre-trained model: Open your browser and copy this URL there, press enter. https://github.com/ayoolaolafenwa/PixelLib/releases/download/1.1/deeplabv3_xception_tf_dim_ordering_tf_kernels.h5 Dependency: Pixellib Installation: pip install pixellib Code: #importing packages import pixellib from pixellib.tune_bg import alter_bg from matplotlib import pyplot as plt import numpy as np from PIL import Image from IPython.display import Image as img from pylab import rcParams rcParams['figure.figsize'] = 10, 10 #it increases the size of plot change_bg = alter_bg() #object creation #here alter_bg() is a class print(dir(change_bg)) #the functions it consists of Output: ['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'blur_bg', 'blur_camera', 'blur_frame', 'blur_video', 'change_bg_img', 'color_bg', 'color_camera', 'color_frame', 'color_video', 'gray_bg', 'gray_camera', 'gray_frame', 'gray_video', 'load_pascalvoc_model', 'model', 'segmentAsPascalvoc'] Loading Pre-Trained DeepLab V3 Here, we will load the pre-trained deep learning model that is DeepLab V3 for our task of background tuning. #loading pre trained model change_bg.load_pascalvoc_model("C:/Users/91884/Desktop/deeplabv3_xception_tf_dim_ordering_tf_kernels.h5") Loading Images Now, as we are ready with the pre-trained model for background tuning, we will load the main image and the image of the required background. Main image: file_name='C:/Users/91884/Pictures/demo.jpg' plt.imshow(Image.open(file_name)) OUTPUT Background image: bg_file='C:/Users/91884/Pictures/background.jpg' plt.imshow(Image.open(bg_file)) OUTPUT Blur Background First of all, we will blur the background of the main image. change_bg.blur_bg(file_name,moderate=True,output_image_name='blur1.jpg') plt.imshow(Image.open('blur1.jpg')) OUTPUT Grey Background In the next step, we will make the background grey. change_bg.gray_bg(file_name,output_image_name='gray.jpg') plt.imshow(Image.open('gray.jpg')) OUTPUT Changing the background to a Solid Color In this step, we will set the background of the main image to s solid colour. change_bg.color_bg(file_name, colors = (225, 225, 225), output_image_name = "colored_bg.jpg") plt.imshow(Image.open('colored_bg.jpg')) OUTPUT Changing the Background Finally, we will change the background of the main image. change_bg.change_bg_img(f_image_path = file_name,b_image_path = bg_file, output_image_name = "new_img.jpg") plt.imshow(Image.open("new_img.jpg")) OUTPUT Conclusion As we could see above, we were able to tune the background of the image very effectively. It required a very effort and could be achieved in a very few easy steps even when we were using deep learning. So using the pre-trained deep learning models yield effective results with less coding efforts. Hope you liked the article. Stay tuned for more. You can follow me at the handles mentioned. The complete code of the above implementation is available at the AIM’s GitHub repository. Please visit this link to find the notebook of this code.
Edit the picture background based on image segmentation with pre-trained deep learning model in python
How to edit or change the picture background based on image segmentation with pre-trained deep learning model, deeplab v3 in python
["Computer Vision", "Deep Learning", "image processing", "image segmentation", "Transfer Learning"]
["NumPy", "TPU", "image segmentation", "AI", "image processing", "Git", "Python", "Ray", "Aim", "deep learning", "Computer Vision", "Deep Learning", "Transfer Learning", "Matplotlib", "R"]
["AI", "deep learning", "Aim", "Ray", "NumPy", "Matplotlib", "TPU", "Python", "R", "Git"]
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