image-recognition

问题 I am trying to solve a problem of compositing two images in Java. The program will take a part of the first image and past it on the second image. The goal is to make the boundary between the two images less visible. The boundary must be chosen in such a way that the difference between the two images at the boundary is small. My Tasks: To write a method to choose the boundary between the two images. The method will receive the overlapping parts of the input images. This must first be

问题 I am practicing using some swift 2 and have run in to some difficulties in using the CIDetector. I have an app which has a series of pictures in; three different rectangles and three different photos of people/groups. I have been just trying the CIDetector on these images to see what is recognised. The most success I have has is with the faces - however the face it's recognising are in very weird places on the image. Here is a rectangle image I tested, along with it's output: And here is a

问题 Folder structure for my S3 bucket is: Bucket ->training-set ->medium -> img1.jpeg -> img2.jpeg -> img3.PNG My training-set.lst file looks like this: 1 \t 1 \t medium/img1.jpeg 2 \t 1 \t medium/img2.jpeg 3 \t 1 \t medium/img3.PNG I created this using excel sheet. Error: Training failed with the following error: ClientError: Invalid lst file: training-set.lst "InputDataConfig": [ { "ChannelName": "train", "CompressionType": "None", "ContentType": "application/x-image", "DataSource": {

问题 Today I wrote a program for detecting circles using Hough Transform using OpenCV in C. The program inputs 3 images, each image contains a fixed small circle and a big circle with variable position. The program then recognizes both the circles and marks the centres of both the circles. Now what I want to do is that in the output image the (x,y) coordinates of the centre of the bigger circle should be displayed with respect to the centre of the fixed smaller circle . Here's the code for 'circle

问题 I made a direct comparison between TensorFlow vs Keras with the same parameters and the same dataset (MNIST). The strange thing is that Keras achieves 96% performance in 10 epochs, while TensorFlow achieves about 70% performance in 10 epochs. I have run this code many times in the same instance and this inconsistency always occurs. Even setting 50 epochs for TensorFlow, the final performance reaches 90%. Code: import keras from keras.datasets import mnist (x_train, y_train), (x_test, y_test)

问题 first time posting, hope I put the code in the right way. I'm trying to detect and count vehicles in a video, and so if you look at my code below I find the contours of the image after thresholding and dilating, and then I use drawContours and rectangle to draw a box around the detected contours. I tried to put a filter on the drawContours/rectangle if statement by saying if the area of the rectangle isn't bigger than 40,000, then don't draw it. Now if you look at the picture I attached, you

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 months ago . I want my application to understand the diagrams drawn on a paper of 3d objects like cubes, cylinders, pyramids by using image processing(openCV) and render corresponding 3d models in opengl. The following figure shows simple sketch and its corresponding 3d model. How can i recognize the objects drawn on paper

问题 i am working on an Android app that will recognize a GO board and create a SGF file of it. i need to detect the whole board in order to warp it and to be able to find the correct lines and stones like below. (source: eightytwo.axc.nl) right now i use an Opencv RGB Mat and do the following: separate the channels canny the separate channels Imgproc.Canny(channel, temp_canny, 30, 100); combine (bitwise OR) all channels. Core.bitwise_or(temp_canny, canny, canny); find the board contour Still i am

问题 I'm working on machine learning and I would like to use Google Cloud ml service. At this moment, I have trained my model with retrain.py code of Tensorflow (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py#L103) and I have exported the results to a cloudml (export and export.meta files). However when I try to make a prediction of new data with command (https://cloud.google.com/ml/reference/commandline/predict): gcloud beta ml predict it

问题 I am looking for an efficient way to detect the small boxes around the numbers (see images)? I already tried to use hough transformation with no success. Any ideas? I need some hints! I am using opencv... 回答1: I'd start by trying to detect the rectangular boundary of the overall sheet, then applying a perspective transform to make it truly rectangular. Crop that portion of the image out. If possible, then try to make the alternating white and grey sub-rectangles have an equal background