How do I implement real-time inference on ESP32 and reduce latency for image recognition
Still based on my project image recognition system that can analyze images of tissue samples, identify malignancies, and predict possible symptoms and causes. How do i implement real-time inference and display predictions on the
LCD or send data to the cloud? Cause my Inference latency is too high for real-time application, what's the best way to implement this to ensure that Inference time does not remains above acceptable thresholds for real-time use.1 Reply
c++
#include "esp_camera.h"
#include "TensorFlowLite.h"
#include <WiFi.h>
#define LED_PIN 4
void capture_image() {
camera_fb_t *fb = esp_camera_fb_get();
if (!fb) {
Serial.println("Camera capture failed");
return;
}
Serial.println("Image captured successfully");
}
void preprocess_image() {
Serial.println("Preprocessing image...");
}
float run_model_inference() {
Serial.println("Loading model...");
float prediction = 0.85;
Serial.println("Running inference...");
return prediction;
}
void display_prediction(float prediction) {
Serial.print("Prediction: ");
Serial.println(prediction);
if (prediction > 0.5) {
digitalWrite(LED_PIN, HIGH);
Serial.println("Malignant tissue detected.");
} else {
digitalWrite(LED_PIN, LOW);
Serial.println("No malignant tissue detected.");
}
}
void setup() {
Serial.begin(115200);
camera_config_t config;
config.ledc_channel = LEDC_CHANNEL_0;
config.ledc_timer = LEDC_TIMER_0;
config.pin_d0 = Y2_GPIO_NUM;
config.pin_d1 = Y3_GPIO_NUM;
config.pin_d2 = Y4_GPIO_NUM;
config.pin_d3 = Y5_GPIO_NUM;
config.pin_d4 = Y6_GPIO_NUM;
config.pin_d5 = Y7_GPIO_NUM;
config.pin_d6 = Y8_GPIO_NUM;
config.pin_d7 = Y9_GPIO_NUM;
config.pin_xclk = XCLK_GPIO_NUM;
config.pin_pclk = PCLK_GPIO_NUM;
config.pin_vsync = VSYNC_GPIO_NUM;
config.pin_href = HREF_GPIO_NUM;
config.pin_sscb_sda = SIOD_GPIO_NUM;
config.pin_sscb_scl = SIOC_GPIO_NUM;
config.pin_pwdn = PWDN_GPIO_NUM;
config.pin_reset = RESET_GPIO_NUM;
config.xclk_freq_hz = 20000000;
config.pixel_format = PIXFORMAT_JPEG;
config.frame_size = FRAMESIZE_QVGA;
config.jpeg_quality = 10;
config.fb_count = 1;
if (esp_camera_init(&config) != ESP_OK) {
Serial.println("Camera init failed");
return;
}
pinMode(LED_PIN, OUTPUT);
}
void loop() {
run_inference();
delay(5000);
}
c++
#include "esp_camera.h"
#include "TensorFlowLite.h"
#include <WiFi.h>
#define LED_PIN 4
void capture_image() {
camera_fb_t *fb = esp_camera_fb_get();
if (!fb) {
Serial.println("Camera capture failed");
return;
}
Serial.println("Image captured successfully");
}
void preprocess_image() {
Serial.println("Preprocessing image...");
}
float run_model_inference() {
Serial.println("Loading model...");
float prediction = 0.85;
Serial.println("Running inference...");
return prediction;
}
void display_prediction(float prediction) {
Serial.print("Prediction: ");
Serial.println(prediction);
if (prediction > 0.5) {
digitalWrite(LED_PIN, HIGH);
Serial.println("Malignant tissue detected.");
} else {
digitalWrite(LED_PIN, LOW);
Serial.println("No malignant tissue detected.");
}
}
void setup() {
Serial.begin(115200);
camera_config_t config;
config.ledc_channel = LEDC_CHANNEL_0;
config.ledc_timer = LEDC_TIMER_0;
config.pin_d0 = Y2_GPIO_NUM;
config.pin_d1 = Y3_GPIO_NUM;
config.pin_d2 = Y4_GPIO_NUM;
config.pin_d3 = Y5_GPIO_NUM;
config.pin_d4 = Y6_GPIO_NUM;
config.pin_d5 = Y7_GPIO_NUM;
config.pin_d6 = Y8_GPIO_NUM;
config.pin_d7 = Y9_GPIO_NUM;
config.pin_xclk = XCLK_GPIO_NUM;
config.pin_pclk = PCLK_GPIO_NUM;
config.pin_vsync = VSYNC_GPIO_NUM;
config.pin_href = HREF_GPIO_NUM;
config.pin_sscb_sda = SIOD_GPIO_NUM;
config.pin_sscb_scl = SIOC_GPIO_NUM;
config.pin_pwdn = PWDN_GPIO_NUM;
config.pin_reset = RESET_GPIO_NUM;
config.xclk_freq_hz = 20000000;
config.pixel_format = PIXFORMAT_JPEG;
config.frame_size = FRAMESIZE_QVGA;
config.jpeg_quality = 10;
config.fb_count = 1;
if (esp_camera_init(&config) != ESP_OK) {
Serial.println("Camera init failed");
return;
}
pinMode(LED_PIN, OUTPUT);
}
void loop() {
run_inference();
delay(5000);
}