image collection code

This commit is contained in:
Bharath Sudharsan 2022-07-23 04:25:28 +01:00
parent ec305108a6
commit 1d22dbfa3a
2 changed files with 58 additions and 4 deletions

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@ -16,7 +16,8 @@ Following can be observed from the video:
- **Memory** Consumes only 1 kB of RAM - difference between the RAM calculated by Arduino IDE before and after adding the TinyML-CAM image recognition system. - **Memory** Consumes only 1 kB of RAM - difference between the RAM calculated by Arduino IDE before and after adding the TinyML-CAM image recognition system.
### Code ### Code
- [[ino]-CameraWebServer.ino]()
- [[ipynb]-TinyML-CAM-full-code-with-markdown.ipynb](https://github.com/bharathsudharsan/TinyML-CAM/blob/main/%5Bipynb%5D-TinyML-CAM-full-code-with-markdown.ipynb) - [[ipynb]-TinyML-CAM-full-code-with-markdown.ipynb](https://github.com/bharathsudharsan/TinyML-CAM/blob/main/%5Bipynb%5D-TinyML-CAM-full-code-with-markdown.ipynb) -
- [[h]-HOG-plus-RandomForest-classifier.h](https://github.com/bharathsudharsan/TinyML-CAM/blob/main/%5Bh%5D-HOG-plus-RandomForest-classifier.h) - [[h]-HogClassifier.h](https://github.com/bharathsudharsan/TinyML-CAM/blob/main/%5Bh%5D-HogClassifier.h) - Contains the RandomForestClassifier trained using the collected image data.
- [[ino]-arduino-ESP32-code.ino](https://github.com/bharathsudharsan/TinyML-CAM/blob/main/%5Bino%5D-arduino-ESP32-code.ino) - upload to - [[h]-HogPipeline.h](https://github.com/bharathsudharsan/TinyML-CAM/blob/main/%5Bh%5D-HogPipeline.h) - Contains the HOG features extrator for image frames.
- [[ino]-arduino-ESP32-code.ino](https://github.com/bharathsudharsan/TinyML-CAM/blob/main/%5Bino%5D-arduino-ESP32-code.ino) - Upload to ESP32 along with the above two .h files. After upload, put your objects in front of the camera to see predicted labels.

53
[ino]-CameraWebServer.ino Normal file
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@ -0,0 +1,53 @@
#include "eloquent.h"
#include "eloquent/print.h"
#include "eloquent/tinyml/voting/quorum.h"
// replace 'm5wide' with your own model
// possible values are 'aithinker', 'eye', 'm5stack', 'm5wide', 'wrover'
#include "eloquent/vision/camera/m5wide.h"
#include "HogPipeline.h"
#include "HogClassifier.h"
Eloquent::TinyML::Voting::Quorum<7> quorum;
void setup() {
Serial.begin(115200);
delay(3000);
Serial.println("Begin");
camera.qqvga();
camera.grayscale();
while (!camera.begin())
Serial.println("Cannot init camera");
}
void loop() {
if (!camera.capture()) {
Serial.println(camera.getErrorMessage());
delay(1000);
return;
}
// apply HOG pipeline to camera frame
hog.transform(camera.buffer);
// get a stable prediction
// this is optional, but will improve the stability of predictions
uint8_t prediction = classifier.predict(hog.features);
int8_t stablePrediction = quorum.vote(prediction);
if (quorum.isStable()) {
eloquent::print::printf(
Serial,
"Stable prediction: %s \t(DSP: %d ms, Classifier: %d us)\n",
classifier.getLabelOf(stablePrediction),
hog.latencyInMillis(),
classifier.latencyInMicros()
);
}
camera.free();
}