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

```bash 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.

```bash <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

```

```bash <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

```

```bash <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

```

```bash <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

```

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:

```bash <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:

That doesn't seem to be that helpful !

Image Classification

```bash 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:

```bash <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

```bash <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

```bash <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