OpenCV

问题 I'm working on a commercial product where I need to estimate the 6DOF pose of a known 3D CAD (closed 2-manifold triangular mesh) in a single 2D image. In general, this a difficult problem but under our operational conditions, we can impose the following constraints simplifying the problem: The CAD object is known and we do NOT aim for generality like recognizing the class of all chairs. We could get the user to position the camera approximately to a specific pose (distance from the object,

问题 I am trying to extract numbers from in game screenshots. I'm trying to extract: 98 3430 5/10 from PIL import Image import pytesseract image="D:/img/New folder (2)/1.png" pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files/Tesseract-OCR/tesseract.exe' text = pytesseract.image_to_string(Image.open(image),lang='eng',config='--psm 5') print(text) output is gibberish ‘t hl) keteeeees ek pSlaerenen JU) pgrenmnreserenny Rates B d dali eas. 5 cle aM (Sores |, S| pgranmrerererecons a cee 3 pea 3

问题 I'm working on a commercial product where I need to estimate the 6DOF pose of a known 3D CAD (closed 2-manifold triangular mesh) in a single 2D image. In general, this a difficult problem but under our operational conditions, we can impose the following constraints simplifying the problem: The CAD object is known and we do NOT aim for generality like recognizing the class of all chairs. We could get the user to position the camera approximately to a specific pose (distance from the object,

问题 I am trying to use a dicom image and manipulate it using OpenCV in a Python environment. So far I have used the pydicom library to read the dicom(.dcm) image data and using the pixel array attribute to display the picture using OpenCV imshow method. But the output is just a blank window. Here is the snippet of code I am using at this moment. import numpy as np import cv2 import pydicom as dicom ds=dicom.dcmread('sample.dcm') cv2.imshow('sample image dicom',ds.pixel_array) cv2.waitkey() If i

问题 I am new to machine learning and currently working on a project. The project is about classifying images based on feature and color attributes. I have tried classifying images through feature extraction based on the example given in the OpenCV with Python by Example book (the Dense extractor SIFT descriptor technique to generate a codebook and train SVM to classify the extracted feature) but I haven't tried yet combining both feature and color attributes since the images sampled were gray

问题 I am new to machine learning and currently working on a project. The project is about classifying images based on feature and color attributes. I have tried classifying images through feature extraction based on the example given in the OpenCV with Python by Example book (the Dense extractor SIFT descriptor technique to generate a codebook and train SVM to classify the extracted feature) but I haven't tried yet combining both feature and color attributes since the images sampled were gray

问题 I'm working on a project where I have to use openCV in java to identify the state of a tic tac toe board. Please see the sample program execution below. input Output X,-,- -,O,- X,-,- I'm trying to solve this by finding contours in the image, but the problem is that the empty unmarked boxes are also being captured and I'm not being able to distinguish between the shapes using contour properties like polygon size and contour area. Below is the code that I have so far. package finalproject;

问题 I'm working on a project where I have to use openCV in java to identify the state of a tic tac toe board. Please see the sample program execution below. input Output X,-,- -,O,- X,-,- I'm trying to solve this by finding contours in the image, but the problem is that the empty unmarked boxes are also being captured and I'm not being able to distinguish between the shapes using contour properties like polygon size and contour area. Below is the code that I have so far. package finalproject;

问题 I want to use pytesseract to read digits from images. The images look as follows: The digits are dotted and in order to be able to use pytesseract, I need black connected digits on a white background . To do so, I thought about using erode and dilate as preprocessing techniques. As you can see, the images are similar, yet quite different in certain aspects. For example, the dots in the first image are darker than the background, while the dots in the second are whiter. That means, in the

问题 I want to use pytesseract to read digits from images. The images look as follows: The digits are dotted and in order to be able to use pytesseract, I need black connected digits on a white background . To do so, I thought about using erode and dilate as preprocessing techniques. As you can see, the images are similar, yet quite different in certain aspects. For example, the dots in the first image are darker than the background, while the dots in the second are whiter. That means, in the