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  1. A great beginner-to-intermediate computer vision project is Real-Time Object Detection using Python and OpenCV. This project uses a pre-trained YOLO model to detect objects from a webcam feed in real time, making it practical for applications like surveillance, automation, and robotics.

    Steps to Build the Project

    1. Install Required Libraries Make sure you have Python installed, then install dependencies:

    pip install opencv-python numpy
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    2. Download YOLO Model Files Get the yolov3.weights and yolov3.cfg files from the official YOLO repository, along with the coco.names file containing class labels.

    3. Load Model and Classes in Python

    import cv2
    import numpy as np

    # Load YOLO
    net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
    with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

    layer_names = net.getLayerNames()
    output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
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    4. Capture Video and Detect Objects

    cap = cv2.VideoCapture(0)

    while True:
    _, frame = cap.read()
    height, width, _ = frame.shape

    blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)
    outs = net.forward(output_layers)

    for out in outs:
    for detection in out:
    scores = detection[5:]
    class_id = np.argmax(scores)
    confidence = scores[class_id]
    if confidence > 0.5:
    center_x, center_y, w, h = (detection[0:4] * [width, height, width, height]).astype('int')
    x, y = int(center_x - w / 2), int(center_y - h / 2)
    cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
    cv2.putText(frame, classes[class_id], (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2)

    cv2.imshow("Object Detection", frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
    break

    cap.release()
    cv2.destroyAllWindows()
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  1. Computer Vision in Python - GitHub

    This repository documents my work and experiments in computer vision using Python. It includes practical code for image processing, object detection, video analysis, and real-world applications.

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