OpenCV object detection
Object detection is a computer vision technique that identifies the objects of interest within digital images or video frames.
Procedure
Here's the step by step procedure of how the object detection technique works:
Data collection and preparation: Gather a dataset of images or video frames containing the objects you want to detect. Split the dataset into training and test sets. For the training set, annotate the objects of interest by labeling them. For example, if you're building a car detection model, mark the cars in the training images like in the
.xmlfile below:
<annotation><folder>dataset</folder><filename>car1.jpg</filename><path>/path/to/car1.jpg</path><source><database>Unknown</database></source><size><width>640</width><height>480</height><depth>3</depth></size><segmented>0</segmented><object><name>car</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>100</xmin><ymin>200</ymin><xmax>400</xmax><ymax>350</ymax></bndbox></object></annotation>
The file above contains the annotation of only one car from an image car1.jpg. You have to annotate hundreds of vehicles to get better accuracy. There are several training methods other than annotating every image in .xml files.
Detection: You can choose any of the available detection algorithms such as haar cascades, you look only once (Yolo), or single shot multi-box detector (SSD), etc. The choice depends upon the factors like accuracy, speed, and computational power.
Preprocessing: Depending on your specific needs, perform preprocessing steps on your dataset. This may involve resizing, normalization, or converting images to grayscale, among other operations.
Apply detection algorithms: Utilize the chosen detection algorithm to analyze the annotated training images. Feed the annotations from the XML files into the algorithm to train it and generate the necessary model files or parameters.
Test and visualize the results: Draw bounding boxes or segmentation masks around the detected objects to visualize the results. If you do not get the desired results, iterate your detection process with tuned annotations, redefined preprocessing, or a different detection algorithm.
Example
Here's an example of a vehicle detection model trained over thousands of car images. We use the haar cascade detection algorithm, where the images are already annotated in the cars.xml file. We test the model with a sample video and see how accurate our results are:
<?xml version="1.0"?>
<!-- Add more negative to training. -->
<opencv_storage>
<cars3 type_id="opencv-haar-classifier">
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20 20</size>
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</opencv_storage>Sample code for vehicle detection
Line 1: Imports CV2 library.
Line 4: Feeds the annotated training dataset (stored in
cars.xml) to theCascadeClassifier()method of the haar detection algorithm.Line 6: Loads the test video (named
video.avi).Line 9: Starts the loop until every test video frame has been traversed.
Line 11: The
read()method reads the frame and stores it in theframesvariable. Theframesvariable gets updated in each iteration.retis a boolean variable that storesTrueif a frame has been read successfully. Otherwise, it storesFalse.Line 14: Greyscale every frame under preprocessing.
Line 17: Uses the
detectMultiScale()method to recognize cars of different sizes, wheregrayis the frame over which the car will be detected.scaleFactorspecifies the rate at which the image size is reduced during the process.minNeighborsspecifies the minimum number of neighbors a detected region needs to be considered a car.minSizesets the minimum size of the detected car.Lines 20–21: Draw rectangles over the image where it detects a car.
Line 24: Displays the results on the screen.
Lines 27–29: Waits for the user to press
qif they want to quit the window.Line 32: Closes all the opened windows.
Conclusion
Like vehicle detection, OpenCV provides the tools and libraries to detect various objects in image frames. We can use OpenCV's object detection algorithms to identify and localize those objects precisely by training our model with images of the objects we want to detect.
Note: Learn how to:
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