Detecting Objects in Videos using OpenCV

Object detection is a technique that is widely used in computer vision and image processing. The aim of this technique is to identify the presence and location of an object in an image or video. In this blog post, we will explore how to use OpenCV, a popular computer vision library, to detect objects in videos.

Object detection in videos can be a challenging task due to the large amount of data and noise present. OpenCV provides a range of pre-trained models and tools that can be used to facilitate this process.

We can start detecting objects in videos. For this example, we will detect shoes in a video file.

import cv2

# Load the video
video_path = "video.mp4"
cap = cv2.VideoCapture(video_path)

# Load the object detection model
model_path = "frozen_inference_graph.pb"
net = cv2.dnn.readNetFromTensorflow(model_path)

# Define the object classes
classes = ["background", "shoe"]

while True:
    # Read the current frame
    ret, frame = cap.read()

    # Stop the loop if the video is over
    if not ret:
        break

    # Detect objects in the current frame
    blob = cv2.dnn.blobFromImage(frame, size=(300, 300), swapRB=True)
    net.setInput(blob)
    detections = net.forward()

    # Loop over the detections and draw bounding boxes around the shoes
    for i in range(detections.shape[2]):
        confidence = detections[0, 0, i, 2]
        if confidence > 0.5:
            class_id = int(detections[0, 0, i, 1])
            if classes[class_id] == "shoe":
                box = detections[0, 0, i, 3:7] * np.array([frame.shape[1], frame.shape[0], frame.shape[1], frame.shape[0]])
                (startX, startY, endX, endY) = box.astype("int")
                cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 255, 0), 2)

    # Display the current frame
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

    # Stop the loop if the "q" key is pressed
    if key == ord("q"):
        break

# Release the video and close the window
cap.release()
cv2.destroyAllWindows()

In this example, we first load the video and the pre-trained object detection model. We then define the object classes we are looking for, in this case just “shoe”.

For each frame in the video, we detect objects using the object detection model and draw bounding boxes around the shoes using OpenCV. We then display the video with the bounding boxes drawn around the shoes.

To stop the video, we check if the “q” key has been pressed and release the video and close the window.

In conclusion, OpenCV is a powerful tool for detecting objects in videos. With a few lines of code, we can detect and track objects in real-time videos with high accuracy.