How is the convolution operation carried out when multiple channels are present at the input layer? (e.g. RGB) In such a case you have one 2D kernel per input channel (a.k.a plane). So you perform each convolution (2D Input, 2D kernel) separately and you sum the contributions which gives the final output feature map. Please … Read more
I wrote a basic HTML helper method to emit the correct <img> tag to take advantage of Google’s API. So, on your page (assuming ASPX view engine) use something like this: <%: Html.QRCodeImage(Request.Url.AbsolutePath) %> <%: Html.QRCodeImage(“Meagre human needs a phone to read QR codes. Ha ha ha.”) %> Or if you want to specify the … Read more
I wasted several hours until I discovered that reducing the batch size and resizing the width of my input image (image size) were necessary steps.
So i think you are on the right track w/r/t your step 1 (apply some algorithm to the image, which converts it into a set of features). This project is more challenging that most ML problems because here you will actually have to create your training data set from the raw data (the individual frames … Read more
You can certainly use the VideoWriter class, but you need to use the correct FourCC code that represents the the H264 standard. FourCC stands for Four Character Code, which is an identifier for a video codec, compression format, colour or pixel format used in media files. Specifically, when you create a VideoWriter object, you specify … Read more
You can just make a copy of the original image and set some channels to 0. import cv2 image = cv2.imread(‘download.jpg’) b = image.copy() # set green and red channels to 0 b[:, :, 1] = 0 b[:, :, 2] = 0 g = image.copy() # set blue and red channels to 0 g[:, :, … Read more
Template matching with matchTemplate is not good when your object is rotated or scaled in scene. You should try openCV function from Features2D Framework. For example SIFT or SURF descriptors, and FLANN matcher. Also, you will need findHomography method. Here is a good example of finding rotated object in scene. Update: In short, algorithm is … Read more
Sharpen square edges. Load the image, convert to grayscale, median blur to smooth, and sharpen to enhance edges. Obtain binary image and remove noise. We threshold to obtain a black/white binary image. Depending on the image, Otsu’s thresholding or adaptive thresholding would work. From here we create a rectangular kernel and perform morphological transformations to … Read more
I got the same problem when using the 1.0 released and I could make it work without having to roll back on a previous version. The problem is caused by change in the api. That discussion helped me to find the solution: Google group > Recent API Changes in TensorFlow You just have to update … Read more