项目作者： aa1000
项目描述 ：
classic content-based image retrieval using python (feature vector distance)
高级语言： Python
项目地址: git://github.com/aa1000/ImageRetrievalClassic.git
Content-based Image Retrieval
classic content-based image retrieval using python (feature vector distance)
Each feature vector consists of an:
- RGB Color Histogram
- Indexed Color Histogram (using the indices of color classes in a global LUT)
- DCT2 (Discrete Cosine Transform) Low Frequency Range
- GLCM (Gray-Level Co-occurrence Matrix) Energy And Contrast
Dependencies:
- OpenCV (CV2)
- numpy
- scipy
- sklearn
- skimage
- glob2
- matplotlib
- tkinter
Steps:
- Create a root directory and put the ‘CreateDatabase.py’, ‘RetrieveSimilarImages.py’ and ‘ImageUtils.py’ scripts in it
- Collect the number of images you want to use as a database and arrange them into a new folder(s) in the root directory
- Run ‘CreateDatabase.py’ which will search for all folders in the root directory and create the database of feature vectors for all images present in them (might take a while depending on the size of the database and processing power)
- Run ‘RetrieveSimilarImages.py’ which will prompt you to choose an image file to use as the base for search then return the closest matches
