Python « Enumap's Weblog. 20120606 แปลงระบบพิกัดด้วยไพธอน เราสามารถแปลงระบบพิกัดได้ง่ายๆ ด้วยภาษาไพธอน โดยใช้ไลบราลี่ pyproj (ต้องติดตั้งเพิ่ม) สามารถดาวน์โหลดได้จาก โค้ดตัวอย่างง่ายๆ ดังนี้ # Coordinate transformation with Python import pyproj def utm2latlon(E,N): “”” convert UTM zone 47N coordinate(E,N) to geographic coordinate (longitude, latitude) “”” source = pyproj.Proj(init=’epsg:32647′) target = pyproj.Proj(init=’epsg:4326′) lon,lat= pyproj.transform(source,target,E,N) return(lon,lat) def latlon2utm(lon,lat): “”” convert geographic coordinate (longitude, latitude) to UTM zone 47N coordinate (E, N) “”” source = pyproj.Proj(init=’epsg:4326′) target = pyproj.Proj(init=’epsg:32647′) E, N = pyproj.transform(source,target,lon,lat) return(E,N) ทำเป็นฟังก์ชั่นไว้ใช้งานเลย เวลาใช้ก็เรียก E,N = latlon2utm(99.50,13.50) print E,N.
Feature Descriptor. 【Structure】 of image retrieval on Android « Firefly's space. 1.
Use web service. – PHP 2. Save URLs of images in the database. 3. Retrieve URLs and download images directly from app in a separate thread. 4. 1) Server: 2) Client take a query image - Use magnetic sensors on the phone to get the orientation of the camera.Down-sample image to 1/16 of the original size mobile computing:cloud computing:display result: how to display the returned image to the users. Retrieving image from sd card. Blog - Toast Driven.
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Dijkstra « Time is an illusion. Lunchtime doubly so. I recently received the following question: Im trying to solve the following problem in Powershell: I know the name of Active Directory Site A and the name of Active Directory Site B, Site A doesn’t necessarily have a site link to Site B (could go A -> C -> B).
What is the total cost of the least cost path between these 2 sites? What is the total cost between any 2 sites? (effectively I’m trying to replace this : api with a Powershell version.)To do so, I think I need to implement some kind of Dijkstra engine to solve this… SIFT: Scale Invariant Feature Transform. Matching features across different images in a common problem in computer vision. When all images are similar in nature (same scale, orientation, etc) simple corner detectors can work. But when you have images of different scales and rotations, you need to use the Scale Invariant Feature Transform. Why care about SIFT SIFT isn’t just scale invariant. You can change the following, and still get good results: Scale (duh) Rotation Illumination Viewpoint Here’s an example. And we want to find these objects in this scene: Here’s the result: Now that’s some real robust image matching going on.
The algorithm SIFT is quite an involved algorithm. 10 Best Algorithm Websites. Search Engine Marketing (SEM), Paid Search Advertising (PPC) & Search Engine Optimization (SEO) - Search Engine Watch (#SEW) Feature descriptor comparison report « Computer Vision Talks. Sharing my research work of behavior of several types of feature descriptors.
This article is an update of old “Comparison of feature descriptors“ post. I’ve added a brand new ORB feature descriptor to the test suite, also SIFT descriptor included as well. And a new version of LAZY descriptor present in this test too. For this test i have written special test framework, which allows me to easily add the new kind of descriptors and test cases and generate report data in CSV-like format. Than i upload it in Google docs and create this awesome charts. Rotation test - this test shows how the feature descriptor depends on feature orientation.Scaling test - this test shows how the feature descriptor depends on feature size.Blur test - this test shows how the feature descriptor is robust against blur.Lighting test - this test shows how the feature descriptor is robust against lighting.Pattern detection test - this test performs detection of planar object (image) on the real video.