If you have a two-dimensional numpy array a, you can use a Gaussian filter on it directly without using Pillow to convert it to an image first. scipy has a function gaussian_filter that does the same. from scipy.ndimage.filters import gaussian_filter blurred = gaussian_filter(a, sigma=7)
The error message seems to be complaining about the shape, but it is really about the data type. Multiplying by 255 and then changing to uint8 fixed the problem for me: random_array = np.random.random_sample(content_array.shape) * 255 random_array = random_array.astype(np.uint8) random_image = Image.fromarray(random_array)
The fourth layer is the transparency value for image formats that support transparency, like PNG. If you remove the 4th value it’ll be a correct RGB image without transparency. EDIT: Example: >>> import PIL.Image >>> image = PIL.Image.open(‘../test.png’) >>> import numpy as np >>> image = np.array(image) >>> image.shape (381, 538, 4) >>> image[…,:3].shape (381, … Read more
A typical python tool chain would be: read your images with with PIL transform them into Numpy arrays use Scipy’s image filters (linear and rank, morphological) to implement your solution As far differentiating the shapes, I would obtain its silhouette by looking at the shape of the background. I would then detect the number of … Read more
In my case, on Windows, all I needed to do is to run: pip install pillow
Try using import PIL or from PIL import … instead. Pillow is a fork of PIL, the Python Imaging Library, which is no longer maintained. However, to maintain backwards compatibility, the old module name is used.
With the python CV2, you can also do this: import Image, cv2 cap = cv2.VideoCapture(0) # says we capture an image from a webcam _,cv2_im = cap.read() cv2_im = cv2.cvtColor(cv2_im,cv2.COLOR_BGR2RGB) pil_im = Image.fromarray(cv2_im) pil_im.show()
See this wikibook: https://en.wikibooks.org/wiki/Python_Imaging_Library/Editing_Pixels Modifying that code to fit your problem: pixels = img.load() # create the pixel map for i in range(img.size[0]): # for every pixel: for j in range(img.size[1]): if pixels[i,j] != (255, 0, 0): # change to black if not red pixels[i,j] = (0, 0 ,0)
I fancied some fun so I did some benchmarking on the various methods suggested above and a few ideas of my own. I collected together 1000 high resolution 12MP iPhone 6S images, each 4032×3024 pixels and use an 8-core iMac. Here are the techniques and results – each in its own section. Method 1 – … Read more
Install Pillow pip install Pillow and use as from PIL import Image image=Image.open(‘L_2d.png’) imageBox = image.getbbox() cropped = image.crop(imageBox) cropped.save(‘L_2d_cropped.png’) When you search for boundaries by mask=imageComponents[3], you search only by blue channel.