Object Detection with OpenCV and SSD MobileNet v3

Table of Contents

1. Description
2. Requirements
3. Instructions
4. Code Breakdown
5. License

Description

This Python script leverages OpenCV and a pre-trained SSD MobileNet v3 deep learning model for real-time object detection in video streams. It utilizes the COCO (Common Objects in Context) dataset for object class recognition.

Requirements

• Python 3.x (Download)
• OpenCV library: Install via pip install opencv-python
• NumPy library: Install via pip install numpy (usually included with OpenCV)
• coco.names file: Contains class labels for the COCO dataset (download from a reliable source)
• ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt file: Model configuration file (download from a reliable source)
• frozen_inference_graph.pb file: Model weights file (download from a reliable source)

Instructions

1. Download necessary files:

   • Obtain the coco.names, ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt, and frozen_inference_graph.pb files from a trusted source (ensure compatibility with your OpenCV version). Place them in the same directory as your Python script.

2. Run the script:

   • Open a terminal or command prompt, navigate to the directory containing your script and files.
   • Execute the script using:

     python object_detection.py

Code Breakdown

1. Imports:

   • cv2: Imports the OpenCV library for computer vision tasks.

2. Threshold and Video Capture:

   • thres: Sets the confidence threshold for object detection (adjust as needed).
   • cap: Initializes a video capture object (VideoCapture(1)) to access your webcam (or a different video source by providing its index or path).
   • cap.set(): Sets video capture properties:
     • 3: Width (adjust for desired resolution)
     • 4: Height (adjust for desired resolution)
     • 10: Brightness (adjust for lighting conditions)

3. Load Class Names:

   • classNames: Creates an empty list to store object class names.
   • classFile: Path to the coco.names file.
   • Loads class names from the file and splits them into a list.

4. Load Model Configuration and Weights:

   • configPath: Path to the ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt file.
   • weightsPath: Path to the frozen_inference_graph.pb file.
   • net: Creates a detection model object using cv2.dnn_DetectionModel().
   • Sets model input size, scale, mean, and color channel swapping parameters for compatibility with the model.

5. Main Loop:

   • while True: Continuously captures frames from the video stream.
   • success, img: Reads a frame and checks for success. Exits if unsuccessful.
   • classIds, confs, bbox: Performs object detection using the model on the current frame.
     • classIds: List of detected object class IDs.
     • confs: List of corresponding confidence scores (0-1).
     • bbox: List of bounding boxes (coordinates) for detected objects.
   • Prints detected object IDs and bounding boxes for debugging (optional).

6. Draw Bounding Boxes and Labels:

   • Checks if any objects were detected (len(classIds) != 0).
   • Iterates through detected objects using zip:
     • box: Current bounding box coordinates.
     • classId: Current object class ID (minus 1 for indexing).
     • confidence: Current object confidence score.
   • Draws a green rectangle around the detected object using cv2.rectangle().
   • Displays the corresponding class name (uppercase) and confidence score (rounded to two decimal places) using cv2.putText().

7. Display and Exit:

   • cv2.imshow(): Displays the processed frame with bounding boxes and labels in a window titled "Output".
   • cv2.waitKey(1): Waits for a key press for 1 millisecond.
   • Exits the loop and releases resources if the 'q' key is pressed.

8. Cleanup:

   • Releases the video capture object and closes all OpenCV windows.

     cap.release()
     cv2.destroyAllWindows()

License

This project is licensed under the MIT License. thannk