Building

Build samples of "dnn_objectect" module. Refer to OpenCV build tutorials for details. Enable BUILD_EXAMPLES=ON CMake option and build these targets (Linux):

example_dnn_objdetect_image_classification
example_dnn_objdetect_obj_detect

Download the weights file and model definition file from opencv_extra/dnn_objdetect

Object Detection

example_dnn_objdetect_obj_detect <model-definition-file> <model-weights-file> <test-image>

All the following examples were run on a laptop with Intel(R) Core(TM)2 i3-4005U CPU @ 1.70GHz (without GPU).

The model is incredibly fast taking just 0.172091 seconds on an average to predict multiple bounding boxes.

<bin_path>/example_dnn_objdetect_obj_detect  SqueezeDet_deploy.prototxt  SqueezeDet.caffemodel  tutorials/images/aeroplane.jpg
 
Total objects detected: 1 in 0.168792 seconds
------
Class: aeroplane
Probability: 0.845181
Co-ordinates: 41 116 415 254
------

Train_Dets

<bin_path>/example_dnn_objdetect_obj_detect  SqueezeDet_deploy.prototxt  SqueezeDet.caffemodel  tutorials/images/bus.jpg
 
Total objects detected: 1 in 0.201276 seconds
------
Class: bus
Probability: 0.701829
Co-ordinates: 0 32 415 244
------

Train_Dets

<bin_path>/example_dnn_objdetect_obj_detect  SqueezeDet_deploy.prototxt  SqueezeDet.caffemodel  tutorials/images/cat.jpg
 
Total objects detected: 1 in 0.190335 seconds
------
Class: cat
Probability: 0.703465
Co-ordinates: 34 0 381 282
------

Train_Dets

<bin_path>/example_dnn_objdetect_obj_detect  SqueezeDet_deploy.prototxt  SqueezeDet.caffemodel  tutorials/images/persons_mutli.jpg
 
Total objects detected: 2 in 0.169152 seconds
------
Class: person
Probability: 0.737349
Co-ordinates: 160 67 313 363
------
Class: person
Probability: 0.720328
Co-ordinates: 187 198 222 323
------

Train_Dets

Go ahead and run the model with other images !

Changing threshold

By default this model thresholds the detections at confidence of 0.53. While filtering there are number of bounding boxes which are predicted, you can manually control what gets thresholded by passing the value of optional arguement threshold like:

<bin_path>/example_dnn_objdetect_obj_detect <model-definition-file> <model-weights-file> <test-image> <threshold>

Changing the threshold to say 0.0, produces the following:

Train_Dets

That doesn't seem to be that helpful !

Image Classification

example_dnn_objdetect_image_classification <model-definition-file> <model-weights-file> <test-image>

The size of the model being 4.9MB, just takes a time of 0.136401 seconds to classify the image.

Running the model on examples produces the following results:

<bin_path>/example_dnn_objdetect_image_classification  SqueezeNet_deploy.prototxt  SqueezeNet.caffemodel  tutorials/images/aeroplane.jpg
Best class Index: 404
Time taken: 0.137722
Probability: 77.1757

Looking at synset_words.txt, the predicted class belongs to airliner

<bin_path>/example_dnn_objdetect_image_classification  SqueezeNet_deploy.prototxt  SqueezeNet.caffemodel  tutorials/images/cat.jpg
Best class Index: 285
Time taken: 0.136401
Probability: 40.7111

This belongs to the class: Egyptian cat

<bin_path>/example_dnn_objdetect_image_classification  SqueezeNet_deploy.prototxt  SqueezeNet.caffemodel  tutorials/images/space_shuttle.jpg
Best class Index: 812
Time taken: 0.137792
Probability: 15.8467

This belongs to the class: space shuttle