![]() ![]() (In this case is only 3%, top class has 0.7 with scikit + Keras and 0.66 with OpenCV). (Btw, the parameters fx and fy denote the scaling factor in the function below.) img cv2.resize (img, None, fx0.5, fy0.5, interpolationcv2. ![]() If you’re interested in shrinking your image, INTERAREA is the way to go for you. 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. The images that are rescaled are either shrunk or enlarged. Try this: imgarray cv2.resize (imgarray, dsize (299, 299), interpolationcv2.INTERCUBIC) You can find the detailed explanation here: Explanation of cv2. Upper left pixel value is: 145.91113, 76.853516, 92.67676īut with scikit-image: img = resize(imread(img_path, as_grey=False), (80, 80), preserve_range=True, mode='constant') 2 Answers Sorted by: 5 I think the error comes from the dimension that requires only the width and height. The first is the image that you want to convert. New_image = cv2.resize(image32,(80,80),interpolation=cv2.INTER_LINEAR) In OpenCV, we use the cv2.resize() function to perform an image resize. Note that the initial dst type or size are not taken into. Image32 = cv2.imread(image).astype(np.float32)Ĭv2.cvtColor(image32,cv2.COLOR_BGR2RGB,image32) The function resize resizes the image src down to or up to the specified size. 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. Python3 def concatvh (list2d): return cv2.vconcat ( cv2. The problem is that I cannot get the same data to pass to the network (DNN module in case of OpenCV). cv2.imshow ('hconcatresize.jpg', imghresize) Output: hconcatresize.jpg Concatenate images of the same size vertically and horizontally: images can be combined using both cv2.hconcat () and cv2.vconcat () in tile form using a 2D list. Model = ('mynet.prototxt', 'mynet.caffemodel') Image = resize(imread(img_path, as_grey=False), (80, 80), preserve_range=True, mode='constant') ![]() Scikit-Image + Keras from keras.models import model_from_json I'm trying to reproduce the same output with these snippets: ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |