image collection code

README.md

@@ -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.
 
 ### Code
-
-- [[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)
-- [[ino]-arduino-ESP32-code.ino](https://github.com/bharathsudharsan/TinyML-CAM/blob/main/%5Bino%5D-arduino-ESP32-code.ino) - upload to
+- [[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) -
+- [[h]-HogClassifier.h](https://github.com/bharathsudharsan/TinyML-CAM/blob/main/%5Bh%5D-HogClassifier.h) - Contains the RandomForestClassifier trained using the collected image data.
+- [[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.

[ino]-CameraWebServer.ino

@@ -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();
+}