Computer Vision (CV) enables machines to see, interpret, and understand images or videos like humans do.
1️⃣ What is Computer Vision?
It’s a field of AI that trains computers to extract meaningful info from visual inputs (images/videos).
2️⃣ Common Applications:
- Facial recognition (Face ID)
- Object detection (Self-driving cars)
- OCR (Reading text from images)
- Medical imaging (X-rays, MRIs)
- Surveillance & security
- Augmented Reality (AR)
3️⃣ Key CV Tasks:
- Image classification: What’s in the image?
- Object detection: Where is the object?
- Segmentation: What pixels belong to which object?
- Pose estimation: Detect body/face positions
- Image generation & enhancement
4️⃣ Popular Libraries & Tools:
- OpenCV
- TensorFlow & Keras
- PyTorch
- Mediapipe
- YOLO (You Only Look Once)
- Detectron2
5️⃣ Image Classification Example:
from tensorflow.keras.applications import MobileNetV2
model = MobileNetV2(weights="imagenet")
6️⃣ Object Detection:
Uses bounding boxes to detect and label objects.
YOLO, SSD, and Faster R-CNN are top models.
7️⃣ Convolutional Neural Networks (CNNs):
Core of most vision models. They detect patterns like edges, textures, shapes.
8️⃣ Image Preprocessing Steps:
- Resizing
- Normalization
- Grayscale conversion
- Data Augmentation (flip, rotate, crop)
9️⃣ Challenges in CV:
- Lighting variations
- Occlusions
- Low-resolution inputs
- Real-time performance
🔟 Real-World Use Cases:
- Face unlock
- Number plate recognition
- Virtual try-ons (glasses, clothes)
- Smart traffic systems
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