As I previously mentioned I wrote a python script to test how well our classifier detects narrow helixes. I wanted to test a small sample size, so I took 5 ear samples from a folder within our research gdrive to test against. The results were a bit discouraging, but I realized there's a few things I can do to better train the cascade.
detect.py (source code)
Results:
Trial 1 - 4 Narrow Helixes Detected
Trial 2 - 7 Narrow Helixes Detected
Trial 3- 4 Narrow Helixes Detected
Trial 4 - 5 Narrow Helixes Detected
Trial 5 - 5 Narrow Helixes Detected
The results from testing were very inaccurate. There should be only one narrow helix detected in a sample image. A few things that I believe will help are providing more samples to train with. With this classifier there are two positive samples per negative sample. In resources I found many cascades were trained with a high ratio of negative samples compared to positives. Also in my test script I specify minNeighbors to equal 5. Which means that my program will detect at least 5 objects before it declares that a narrow helix is found. I believe if I increase the minimum neighbors the detection will be more accurate.
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