| OpenCV 4.3.0 Open Source Computer Vision

Object Detection using CNNs

# 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`