文件列表:

LICENSE (1090, 2015-07-23)
bin (0, 2015-07-23)
bin\test-object-detector (1056, 2015-07-23)
data (0, 2015-07-23)
data\config (0, 2015-07-23)
data\config\config.cfg (281, 2015-07-23)
data\images (0, 2015-07-23)
data\images\test-im-1-nms.png (26068, 2015-07-23)
data\images\test-im-1.png (23631, 2015-07-23)
data\images\test-im-2-nms.png (25332, 2015-07-23)
data\images\test-im-2.png (23078, 2015-07-23)
data\images\test-im-3-nms.png (13504, 2015-07-23)
data\images\test-im-3.png (12919, 2015-07-23)
data\images\test-im-4-nms.png (39354, 2015-07-23)
data\images\test-im-4.png (37420, 2015-07-23)
data\models (0, 2015-07-23)
data\models\svm.model (605, 2015-07-23)
data\models\svm.model_01.npy (96, 2015-07-23)
data\models\svm.model_02.npy (19520, 2015-07-23)
data\models\svm.model_03.npy (88, 2015-07-23)
object-detector (0, 2015-07-23)
object-detector\__init__.py (136, 2015-07-23)
object-detector\config.py (735, 2015-07-23)
object-detector\extract-features.py (2205, 2015-07-23)
object-detector\nms.py (3119, 2015-07-23)
object-detector\test-classifier.py (4480, 2015-07-23)
object-detector\train-classifier.py (1655, 2015-07-23)

# object-detector Object Detector using HOG as descriptor and Linear SVM as classifier. | [Video](https://www.youtube.com/watch?v=SPXocFBjr70) ## Run the code I have created a single python script that can be used to test the code. To test the code, run the lines below in your terminal. ```shell git clone https://github.com/bikz05/object-detector.git cd object-detector/bin test-object-detector ``` _The `test-object-detector` will download the [UIUC Image Database for Car Detection](https://cogcomp.cs.illinois.edu/Data/Car/) and train a classifier to detect cars in an image. The SVM model files will be stored in `data/models`, so that they can be resused later on._ ### Configuration File All the configurations are in the `data/config/config.cfg` configuration files. You can change it as per your need. Here is what the default configuration file looks like (which I have set for Car Detector)- ```bash [hog] min_wdw_sz: [100, 40] step_size: [10, 10] orientations: 9 pixels_per_cell: [8, 8] cells_per_block: [3, 3] visualize: False normalize: True [nms] threshold: .3 [paths] pos_feat_ph: ../data/features/pos neg_feat_ph: ../data/features/neg model_path: ../data/models/svm.model ``` ### About the modules * `extract-features.py` -- This module is used to extract HOG features of the training images. * `train-classifier.py` -- This module is used to train the classifier. * `nms.py` -- This module performs Non Maxima Suppression. * `test-classifier.py` -- This module is used to test the classifier using a test image. * `config.py` -- Imports the configuration variables from `config.cfg`. ## Some of the results #### Test Image 1 _Detections before NMS_  _Detections after NMS_  #### Test Image 2 _Detections before NMS_  _Detections after NMS_  #### Test Image 3 _Detections before NMS_  _Detections after NMS_  #### Test Image 4 _Detections before NMS_  _Detections after NMS_  ## TODO Here is list of tasks that I am planning to implement in the future - * Optimize code to use more `numpy` vectorized codes. * Faster NMS code. * Add bootstrapping (Hard Negative Mining) code. ## Useful tutorials 1. [Histogram of Oriented Gradients and Object Detection](http://www.pyimagesearch.com/2014/11/10/histogram-oriented-gradients-object-detection/) 2. [Image Pyramids with Python and OpenCV](http://www.pyimagesearch.com/2015/03/16/image-pyramids-with-python-and-opencv/) 3. [Sliding Windows for Object Detection with Python and OpenCV](http://www.pyimagesearch.com/2015/03/23/sliding-windows-for-object-detection-with-python-and-opencv/) 4. [Non-Maximum Suppression for Object Detection in Python](http://www.pyimagesearch.com/2014/11/17/non-maximum-suppression-object-detection-python/) 5. [(Faster) Non-Maximum Suppression in Python](http://www.pyimagesearch.com/2015/02/16/faster-non-maximum-suppression-python/) 6. [Texture Matching using Local Binary Patterns (LBP), OpenCV, scikit-learn and Python](http://hanzratech.in/2015/05/30/local-binary-patterns.html) 7. [Deteccin de objetos Course by Coursera](https://www.coursera.org/course/deteccionobjetos)

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