(In this case is only 3%, top class has 0.7 with scikit + Keras and 0.66 with OpenCV). Scikit image uses by default interpolation bi-linear, the same used by OpenCV, so it should be the same, I also tried every possible value for parameter mode, I can never obtain the same result between scikit and OpenCV.Īltough the values seems only slightly different, when passed to the network, the network produce a different result, which I noticed can be up to 12% in classification probalility. New_image = cv2.resize(image32,(80,80),interpolation=cv2.INTER_LINEAR) Image32 = cv2.imread(image).astype(np.float32)Ĭv2.cvtColor(image32,cv2.COLOR_BGR2RGB,image32) I think the problem is that Resize function of OpenCV (used internally by blobFromImage) produce a different result from scikit-image resize (I'm not saying is bugged, just want to obtain same results bewteen OpenCV and Sciki-Image, doing something in one or in another), for example for this image: My final application will use OpenCV in C++, so I need to match this snippets, as my network has been trained with data generated by scikit-image. Network is the same, input data is the same, but the results is slightly different and the reason is that resize function behaves differently between scikit-image and OpenCV (used internally by blobFromImage) and don't know how to adapt the OpenCV code to match scikit-image. The problem is that I cannot get the same data to pass to the network (DNN module in case of OpenCV). Model = ('mynet.prototxt', 'mynet.caffemodel') Image = resize(imread(img_path, as_grey=False), (80, 80), preserve_range=True, mode='constant') import cv2 import imutils image cv2.imread ('1.png') resized imutils.resize (image, width100) revert imutils.resize (resized, width250) cv2.imwrite ('resized.png', resized) cv2.imwrite ('original.png', image) cv2.imwrite ('revert.png', revert) cv2. Scikit-Image + Keras from keras.models import model_from_json With this article at OpenGenus, you must have the complete idea of how to resize an image in Python using OpenCV.I'm trying to reproduce the same output with these snippets: Image = cv2.imread("image.jpeg", mode='RGB') 3 Examples of cv2.resize () in Python OpenCV 3.1 Read Sample Image and Display 3.2 Example 1: Resize the Image to Half By Calculating Pixel Size 3.3 Example 2: Scaling Down the Image to Factor 0.5 (Half) 3.4 Example 3: Resize the Image to Double By Calculating Pixel Size 3. Import and read the image: import cv2 img cv2.imread ('pyimg. In the resize method, you can either specify the values of x and y axis or the number of rows and columns which tells the size of the image. One needs to try by experimentation.Ĭomplete Python code to resize an image: import cv2 To resize an image, you can use the resize () method of openCV. The best interpolation method will depend on the image. INTER_LANCZOS4: Lanczos interpolation over 8 x 8 pixel neighborhood.INTER_CUBIC: bicubic interpolation over 4 x 4 pixel neighborhood.INTER_AREA: resampling using pixel area relation.INTER_LINEAR: bilinear interpolation (default).For this we will use the image.shape attribute. Now, we simply apply array slicing to our NumPy array and produce our cropped image, So we have to find the dimensions of the image. INTER_NEAREST: nearest neighbor interpolation technique cv2.imshow ('image', img) cv2.waitKey (0) cv2.destroyAllWindows () Output : Step 2: Get the image Dimensions We can see the type of ‘ img ‘ as ‘ numpy.ndarray ‘.The different interpolation techniques used in OpenCV are: The value of the extra pixel depends on the technique used. If the original image is smaller, then a larger rescaled image has extra pixels which is not exactly the same as a nearby pixels. Interpolation is the way the extra pixels in the new image is calculated. Image = cv2.resize(image, dsize=(new_height, new_width), image = cv2.imread("image.jpeg", mode='RGB') image = np.random.rand(400, 400, 3)Īlternatively, you can load a real image using OpenCV. We can create an Numpy array with 3 dimensions that will work as an image. If you need to do some extra operations on the image, you can use Numpy. Only OpenCV library is used to resize the image. Code to resize Image in Python using OpenCV While resizing, the other two dimensions namely height and width are changed. If we resize an image, the number of channels remain the same.
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