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|
Object Detection using CNNs {#tutorial_dnn_objdetect}
===========================
# 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](https://raw.githubusercontent.com/opencv/opencv/3.4.0/samples/data/dnn/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`
|
