Re: [Sikuli-driver] [Question #706950]: find the image of the 3d model that can be rotated or zoomed
Question #706950 on SikuliX changed: https://answers.launchpad.net/sikuli/+question/706950 RaiMan proposed the following answer: Not available as feature in SikuliX -- You received this question notification because your team Sikuli Drivers is an answer contact for SikuliX. ___ Mailing list: https://launchpad.net/~sikuli-driver Post to : sikuli-driver@lists.launchpad.net Unsubscribe : https://launchpad.net/~sikuli-driver More help : https://help.launchpad.net/ListHelp
Re: [Sikuli-driver] [Question #706950]: find the image of the 3d model that can be rotated or zoomed
Question #706950 on SikuliX changed: https://answers.launchpad.net/sikuli/+question/706950 Status: Answered => Invalid RaiMan changed the question status: Not possible -- You received this question notification because your team Sikuli Drivers is an answer contact for SikuliX. ___ Mailing list: https://launchpad.net/~sikuli-driver Post to : sikuli-driver@lists.launchpad.net Unsubscribe : https://launchpad.net/~sikuli-driver More help : https://help.launchpad.net/ListHelp
Re: [Sikuli-driver] [Question #706950]: find the image of the 3d model that can be rotated or zoomed
Question #706950 on SikuliX changed: https://answers.launchpad.net/sikuli/+question/706950 fontbots proposed the following answer: Detecting an image of a 3D model from various angles and zoom levels can be a challenging task, but there are a few approaches you can consider. Here are a couple of suggestions: Feature Detection and Matching: You can use feature detection algorithms like Scale-Invariant Feature Transform (SIFT) or Speeded-Up Robust Features (SURF) to extract distinctive features from both the 3D model and the image. Then, you can match these features to find correspondences between the model and the image. By analyzing the correspondences, you can estimate the rotation and scale of the 3D model in the image. 3D Model Rendering and Comparison: Instead of directly matching features, you can render the 3D model from different viewpoints using computer graphics techniques. Then, compare the rendered images with the target image using methods like normalized cross-correlation or structural similarity index (SSIM). By finding the best match or highest similarity score, you can estimate the rotation and scale of the 3D model in the image. Both approaches require some computational resources and may involve complex algorithms. OpenCV, a popular computer vision library, provides implementations of feature detection algorithms and image matching techniques that you can leverage for your task. It's worth noting that these suggestions provide a starting point, but depending on the specific requirements and constraints of your application, additional techniques or refinements may be necessary. Experimentation and fine-tuning will likely be required to achieve accurate and robust results.also visit https://fontbots.com/ -- You received this question notification because your team Sikuli Drivers is an answer contact for SikuliX. ___ Mailing list: https://launchpad.net/~sikuli-driver Post to : sikuli-driver@lists.launchpad.net Unsubscribe : https://launchpad.net/~sikuli-driver More help : https://help.launchpad.net/ListHelp
Re: [Sikuli-driver] [Question #706950]: find the image of the 3d model that can be rotated or zoomed
Question #706950 on SikuliX changed: https://answers.launchpad.net/sikuli/+question/706950 Status: Open => Answered george john proposed the following answer: detecting a 3D model from an image that can be rotated and zoomed presents some challenges, but there are approaches you can consider. Here's a general outline of a possible solution: Feature Extraction: Use feature extraction techniques, such as Scale- Invariant Feature Transform (SIFT) or Speeded-Up Robust Features (SURF), to identify distinctive points or regions in the image that can be used for matching. Feature Matching: Build a database of features extracted from multiple views of the 3D model. When a new image is presented, match the extracted features from the image with those in the database. This will help determine the orientation and scale of the 3D model in the image. Pose Estimation: Once you have feature matches, utilize techniques like Perspective-n-Point (PnP) or RANSAC (Random Sample Consensus) to estimate the pose (position and orientation) of the 3D model relative to the camera. This will allow you to understand the model's position and alignment in the image. Robustness Considerations: To handle variations in rotation, zoom, and lighting conditions, you may need to apply robust algorithms or combine multiple techniques. Additionally, you might consider using machine learning approaches, such as deep learning-based object detection, to enhance the accuracy and robustness of your system. Testing and Refinement: Test your system with various images containing the 3D model from different angles and zoom levels to evaluate its performance. Refine and iterate on your approach based on the results and feedback. Please note that implementing a robust solution for 3D model detection from arbitrary images can be complex and may require a combination of computer vision techniques, machine learning, and experimentation. It's recommended to study relevant literature, explore existing libraries or frameworks (such as OpenCV or TensorFlow), and consider consulting with experts in the field if possible. Keep in mind that the specific details of your problem, such as the characteristics of the 3D model and the nature of the images, will influence the optimal approach.also visit https://apkvenom.com/ " >venom -- You received this question notification because your team Sikuli Drivers is an answer contact for SikuliX. ___ Mailing list: https://launchpad.net/~sikuli-driver Post to : sikuli-driver@lists.launchpad.net Unsubscribe : https://launchpad.net/~sikuli-driver More help : https://help.launchpad.net/ListHelp
