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Prueba de la biblioteca de visión por computadora OpenCV

Identifique los principales módulos de OpenCV y su función junto con los modelos aprendidos, evitando al mismo tiempo errores comunes de representación e implementación de imágenes.

Rutas de guía relacionadasBiblioteca Opencv
Pregunta 1 de 8

Which option names operations that directly alter image values or geometry?

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  1. Which option names operations that directly alter image values or geometry?
    • Estimate local feature descriptors for later matching
    • Decode video frames into image arrays
    • Filtering and geometric image transformations
    • Detect object locations without changing the input pixels
  2. In the standard OpenCV image-reading path, which channel order is commonly used for color images?
    • RGB in every API
    • BGR
    • CMYK
    • Grayscale only
  3. How does OpenCV commonly relate to PyTorch or TensorFlow in an ML pipeline?
    • It automatically replaces every dedicated framework's training loop
    • Its image I/O removes the need to define preprocessing
    • It requires every model to be trained inside its DNN module
    • It can preprocess data while another framework trains the model
  4. What must be checked when handing an OpenCV array to another model runtime?
    • Shape, datatype, range, layout and channel order
    • Checking only array shape is sufficient
    • Checking datatype and range but ignoring channel order is sufficient
    • Assuming every binding returns a contiguous RGB array
  5. What determines whether a particular codec or GPU path is available?
    • The package name alone means GPU support is enabled
    • A GPU device guarantees every module was compiled with acceleration
    • The package build and runtime environment
    • File format support is identical in every build
  6. What does camera calibration estimate or correct for?
    • The semantic classes present in the scene
    • Camera geometry such as intrinsics and lens distortion
    • The decision threshold for a recognition model
    • Which video codecs were compiled into the package
  7. Why should preprocessing match the model's training recipe?
    • Matching resolution makes every other preprocessing difference harmless
    • Normalization can change freely if array datatype stays the same
    • A saved model automatically repairs every input-representation mismatch
    • Different resize or normalization steps can shift predictions
  8. Which statement best limits what OpenCV alone provides?
    • A computer vision library, not a complete validated product
    • A complete training and deployment solution for every vision task
    • A guarantee that all preprocessing matches a downloaded model
    • An identical set of hardware backends in every package build