Open anhtuduong opened 9 months ago
References: https://docs.opencv.org/3.4/d4/dc6/tutorial_py_template_matching.html https://pyimagesearch.com/2015/01/26/multi-scale-template-matching-using-python-opencv/ https://docs.opencv.org/4.x/dc/dc3/tutorial_py_matcher.html https://stackoverflow.com/questions/4761940/opencv-template-matching-and-transparency https://answers.opencv.org/question/16535/alpha-dependent-template-matching/ https://stackoverflow.com/questions/4761940/opencv-template-matching-and-transparency https://github.com/opencv/opencv/issues/4837 https://stackoverflow.com/questions/12139381/alpha-dependent-template-matching https://stackoverflow.com/questions/65945434/template-matching-with-python-3-9-1-opencv-python-4-5-1-48-and-mask-transparen
Steps for Simple Image-Based Analysis:
1. Preprocessing:
2. Block Detection:
3. Matching Algorithm:
4. Hierarchical Assembly Interpretation:
5. Planning Robotic Actions:
6. Testing and Validation:
7. Iterative Refinement:
Example Approach:
For instance, if the outline represents a 3-level castle with a 5x1, 3x1, and 1x1 block:
Considerations:
While this method aims for precision without machine learning or 3D understanding, it requires robust image processing and shape-matching algorithms to accurately interpret the outline and infer the assembly sequence. Testing with various outlines and refining the algorithm iteratively will be crucial to achieving reliable results.