ESP32-CAM Object Detection Using OpenCV, Yolov3 In Python

Hi everyone! This tutorial will cover how to perform ESP32-CAM Object detection Using OpenCV In Python. We’ll be covering what the ESP32-CAM is and how to set it up, and then dive right into object detection. With OpenCV, you can process images and videos to detect objects, faces, or even the steering angle of a self-driving car.

We’ll use the Arduino IDE to program the ESP32-CAM and Python program for OpenCV to build a face detection and recognition project.

Here we explore how to use OpenCV in your projects using ESP32 Cam. OpenCV is a powerful library for image processing and computer vision that provides tools and functions for analyzing images and video streams.

Required Materials

ESP32 Camera module
FTDI Module
Micro-USB Cable
Jumper Wires

ESP32 CAM Module

The ESP32-CAM combines an ESP32-S module and an OV2640 2-megapixel camera. ESP32 Camera is designed for applications such as automation, security systems, and the Internet of Things (IoT) that require image or video processing.

The ESP32-CAM module features of ESP32 Cam module, which can capture images and video up to a resolution of 1600 x 1200 pixels.

The ESP32 cam is based on a 32-bit CPU, which also contains a combined Wi-Fi and Bluetooth/BLE. As well as being built in 520 KB SRAM, there is also external 4Mb PSRAM. This module contains GPIO Pins that have UART, SPI, I2C, PWM, ADC, DAC, and much more support.

Check ESP32 CAM Tutorial with Arduino IDE – This tutorial provides a step-by-step guide to getting started with the ESP32-CAM module using the Arduino IDE

ESP32 CAM Pinout

ESP32 Cam has 16 GPIO (general-purpose input/output) pins with the PWM, I2C, SPI, and UART functions.

Connection Between ESP32 Cam & USB-To-TTL

The ESP32-CAM module does not have a USB input, so for its initial configuration/programming, it is necessary to use a USB Converter for serial. ESP32-CAM module can be programmed using the FTDI USB to serial converter. there are many FTDI modules available based on different chips, like CP2102, CP2104, FT232RL, CH340G, and much more.

Here are the Circuit connections you will need to make between an FTDI module and an ESP32-CAM module for programming.

Make the connections according to the table and schematic below.

FTDI Module	ESP32-CAM Module
GND	GND
3.3V	3.3V
TX	RX (U0R)
RX	TX (U0T)
GND	GPIO0

Note – Make sure to check your connections before programming the ESP32-CAMERA module.

Arduino IDE configuration for ESP32-CAM

We already show how to program the ESP32 module with the Arduino IDE so check our previous post.

In the field Additional URLs for Board Manager enter the link https://raw.githubusercontent.com/espressif/arduino-esp32/gh-pages/package_esp32_index.json and then click on OK.

After a few seconds, the ESP32 boards will be available in your ide board manager.

Software and Libraries

Download the ESP32 Libraries from the ESP32-CAM GitHub repository. You can download the ZIP file by clicking on “Clone or download” & selecting “Download ZIP”.

Clone this repository under $Admin/Arduino/libraries directory.

Download the full code for – ESP32 Cam Object Detection

Arduino Code – ESP32-CAM Object Detection With OpenCV

Here’s an example code for object detection and identification using the ESP32-CAM module in Arduino IDE:

At Arduino IDE go to Tools > Board. Select ESP32 Wrover Module.

#include <WebServer.h>
#include <WiFi.h>
#include <esp32cam.h>

//THIS PROGRAM SENDS IMAGE IF IT IS PLACED IN WEB IP, BUT IF IT IS PLACED IN PYTHON IT SENDS VIDEO THROUGH THE ITERATIONS. . . (IF IT WORKS IN PYTHON)
const char* WIFI_SSID = "ESP Repeater";
const char* WIFI_PASS = "77777777";

WebServer server(80); //server on port 80

static auto loRes = esp32cam::Resolution::find(320, 240); //low resolution
static auto hiRes = esp32cam::Resolution::find(800, 600); //high resolution
//static auto hiRes = esp32cam::Resolution::find(640, 480); //high resolution (for fps rates) (IP CAM APP)

void
serveJpg() //capture image .jpg
{
  auto frame = esp32cam::capture();
  if (frame == nullptr) {
    Serial.println("Capture Fail");
    server.send(503, "", "");
    return;
  }
  Serial.printf("CAPTURE OK %dx%d %db\n", frame->getWidth(), frame->getHeight(),
                static_cast<int>(frame->size()));

  server.setContentLength(frame->size());
  server.send(200, "image/jpeg");
  WiFiClient client = server.client();
  frame->writeTo(client);  //and send to a client (in this case it will be python)
}

void
handleJpgLo()  //allows to send low resolution image
{
  if (!esp32cam::Camera.changeResolution(loRes)) {
    Serial.println("SET-LO-RES FAIL");
  }
  serveJpg();
}

void
handleJpgHi() //allows to send high resolution image
{
  if (!esp32cam::Camera.changeResolution(hiRes)) {
    Serial.println("SET-HI-RES FAIL");
  }
  serveJpg();
}

void setup()
{
  Serial.begin(115200);
  Serial.println();

  {
    using namespace esp32cam;
    Config cfg;
    cfg.setPins(pins::AiThinker);
    cfg.setResolution(hiRes);
    cfg.setBufferCount(2);
    cfg.setJpeg(80);

    bool ok = Camera.begin(cfg);
    Serial.println(ok ? "CAMARA OK" : "CAMARA FAIL");
  }

  WiFi.persistent(false);
  WiFi.mode(WIFI_STA);
  WiFi.begin(WIFI_SSID, WIFI_PASS); //connect to the WiFi network
  while (WiFi.status() != WL_CONNECTED) {
    delay(500);
  }

  Serial.print("http://");
  Serial.print(WiFi.localIP());
  Serial.println("/cam-lo.jpg");//to connect IP low res

  Serial.print("http://");
  Serial.print(WiFi.localIP());
  Serial.println("/cam-hi.jpg");//to connect high res IP
  server.on("/cam-lo.jpg",handleJpgLo);//send to the server
  server.on("/cam-hi.jpg", handleJpgHi);

  server.begin();
}

void loop()
{
  server.handleClient();
}

#include <WebServer.h>

#include <WiFi.h>

#include <esp32cam.h>

//THIS PROGRAM SENDS IMAGE IF IT IS PLACED IN WEB IP, BUT IF IT IS PLACED IN PYTHON IT SENDS VIDEO THROUGH THE ITERATIONS. . . (IF IT WORKS IN PYTHON)

const char* WIFI_SSID = "ESP Repeater";

const char* WIFI_PASS = "77777777";

WebServer server(80); //server on port 80

static auto loRes = esp32cam::Resolution::find(320, 240); //low resolution

static auto hiRes = esp32cam::Resolution::find(800, 600); //high resolution

//static auto hiRes = esp32cam::Resolution::find(640, 480); //high resolution (for fps rates) (IP CAM APP)

void

serveJpg() //capture image .jpg

{

auto frame = esp32cam::capture();

if (frame == nullptr) {

Serial.println("Capture Fail");

server.send(503, "", "");

return;

}

Serial.printf("CAPTURE OK %dx%d %db\n", frame->getWidth(), frame->getHeight(),

static_cast<int>(frame->size()));

server.setContentLength(frame->size());

server.send(200, "image/jpeg");

WiFiClient client = server.client();

frame->writeTo(client); //and send to a client (in this case it will be python)

}

void

handleJpgLo() //allows to send low resolution image

{

if (!esp32cam::Camera.changeResolution(loRes)) {

Serial.println("SET-LO-RES FAIL");

}

serveJpg();

}

void

handleJpgHi() //allows to send high resolution image

{

if (!esp32cam::Camera.changeResolution(hiRes)) {

Serial.println("SET-HI-RES FAIL");

}

serveJpg();

}

void setup()

{

Serial.begin(115200);

Serial.println();

{

using namespace esp32cam;

Config cfg;

cfg.setPins(pins::AiThinker);

cfg.setResolution(hiRes);

cfg.setBufferCount(2);

cfg.setJpeg(80);

bool ok = Camera.begin(cfg);

Serial.println(ok ? "CAMARA OK" : "CAMARA FAIL");

}

WiFi.persistent(false);

WiFi.mode(WIFI_STA);

WiFi.begin(WIFI_SSID, WIFI_PASS); //connect to the WiFi network

while (WiFi.status() != WL_CONNECTED) {

delay(500);

}

Serial.print("http://");

Serial.print(WiFi.localIP());

Serial.println("/cam-lo.jpg");//to connect IP low res

Serial.print("http://");

Serial.print(WiFi.localIP());

Serial.println("/cam-hi.jpg");//to connect high res IP

server.on("/cam-lo.jpg",handleJpgLo);//send to the server

server.on("/cam-hi.jpg", handleJpgHi);

server.begin();

}

void loop()

{

server.handleClient();

}

Set these two parameters according to your Wi-Fi settings.

const char* WIFI_SSID = "SSID"; 

const char* WIFI_PASS = "Pass";

const char* WIFI_SSID = "SSID";

const char* WIFI_PASS = "Pass";

Note – Remove the jumper connected between GPIO0 & GND in ESP32 Cam After uploading the code.

Open the Serial Monitor with the baud rate of 115200 Press the ESP32-CAM Reset button and wait for the IP to appear on the serial monitor. Wait for a few seconds and then hit reset again. As you can see I got my IP and it is shown in the image.

Copy this IP Address.

Python Code & Libriries

Here is an example code for object detection and identification using the ESP32-CAM Object Detection Using OpenCV In Python.

We need to install OpenCV. We are going to use the Python programming language to make this tutorial work, so let’s get started.

To install OpenCV

Install OpenCV and the important Python libraries on your laptop. You can use pip to install these libraries. Run the below commands in your terminal:

pip install opencv-python

1	pip install opencv-python

Once OpenCV installation is completed then you can run the following two lines of code to check whether cv2 has been installed or not

$ python
>>> import cv2
>>> cv2.__version__
'4.7.0'

$ python

>>> import cv2

>>> cv2.__version__

'4.7.0'

Install the necessary libraries

pip install numpy

A data set, which identifies the objects in the image using the object detection language. specifies the location of multiple objects in the image

classification
localization

1. Famous algorithm for object detection:

SSD-MobileNetv2, SSD-MobileNetv3

2. Famous dataset for object detection:

COCO

Here is the way to import those files

configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt' 
weightsPath = 'frozen_inference_graph.pb'

1 2	configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt' weightsPath = 'frozen_inference_graph.pb'

Please kindly download the file here. Thanks, configPath, weightPath

Be sure to download the algorithm. pre-trained object detection model frozen_inference_graph.pb and its configuration file ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt from the TensorFlow Object Detection.

Note – Put them in the same directory as your Python script.

Coco.names

The file ‘coco.names‘ This file contains the names of the 90+ objects that the YOLOv3 model is trained to detect.

To use this file in your object detection code, you can simply extract the contents of the file and store them in a list or directory, depending on your needs. Here’s an example.

Here is the list below

Person
Bicycle
Car
Motorcycle
Airplane
Bus
Train
Truck
Boat
Traffic Light
Fire Hydrant
Street Sign
Stop Sign
Parking Meter
Bench
Bird
Cat
Dog
Horse
Sheep
Cow
Elephant
Bear
Zebra
Giraffe
Hat
Backpack
Umbrella
Shoe
Eye Glasses
Handbag
Tie
Suitcase
Frisbee
Skis
Snowboard
Sports Ball
Kite
Baseball Bat
Baseball Glove
Skateboard
Surfboard
Tennis Racket
Bottle
Plate
Wine Glass
Cup
Fork
Knife
Spoon
Bowl
Banana
Apple
Sandwich
Orange
Broccoli
Carrot
Hot Dog
Pizza
Donut
Cake
Chair
Couch
Potted Plant
Bed
Mirror
Dining Table
Window
Desk
Toilet
Door
Tv
Laptop
Mouse
Remote
Keyboard
Cell Phone
Microwave
Oven
Toaster
Sink
Refrigerator
Blender
Book
Clock
Vase
Scissors
Teddy Bear
Hair Drier
Toothbrush
Hair Brush

Set classFile in code like below.

classNames = []
classFile = 'coco.names'

1 2	classNames = [] classFile = 'coco.names'

Python code – ESP32-CAM Object detection Using OpenCV

Now open a Thonny IDE or Python code editor, such as Idle. Copy and paste the below code.

We are using the urllib.request library to retrieve the frames from the URL of the ESP32-CAM module video stream.

import cv2 #opencv
import urllib.request #to open and read URL
import numpy as np

#OBJECT CLASSIFICATION PROGRAM FOR VIDEO IN IP ADDRESS

url = 'http://192.168.1.6/cam-hi.jpg'
#url = 'http://192.168.1.6/'
winName = 'ESP32 CAMERA'
cv2.namedWindow(winName,cv2.WINDOW_AUTOSIZE)
#scale_percent = 80 # percent of original size    #for image processing

classNames = []
classFile = 'coco.names'
with open(classFile,'rt') as f:
    classNames = f.read().rstrip('\n').split('\n')

configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
weightsPath = 'frozen_inference_graph.pb'

net = cv2.dnn_DetectionModel(weightsPath,configPath)
net.setInputSize(320,320)
#net.setInputSize(480,480)
net.setInputScale(1.0/127.5)
net.setInputMean((127.5, 127.5, 127.5))
net.setInputSwapRB(True)

while(1):
    imgResponse = urllib.request.urlopen (url) # here open the URL
    imgNp = np.array(bytearray(imgResponse.read()),dtype=np.uint8)
    img = cv2.imdecode (imgNp,-1) #decodificamos

    img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE) # vertical
    #img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #black and white

    

    classIds, confs, bbox = net.detect(img,confThreshold=0.5)
    print(classIds,bbox)

    if len(classIds) != 0:
        for classId, confidence,box in zip(classIds.flatten(),confs.flatten(),bbox):
            cv2.rectangle(img,box,color=(0,255,0),thickness = 3) #mostramos en rectangulo lo que se encuentra
            cv2.putText(img, classNames[classId-1], (box[0]+10,box[1]+30), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0),2)


    cv2.imshow(winName,img) #  show the picture

    #wait for ESC to be pressed to end the program
    tecla = cv2.waitKey(5) & 0xFF
    if tecla == 27:
        break
cv2.destroyAllWindows()

import cv2 #opencv

import urllib.request #to open and read URL

import numpy as np

#OBJECT CLASSIFICATION PROGRAM FOR VIDEO IN IP ADDRESS

url = 'http://192.168.1.6/cam-hi.jpg'

#url = 'http://192.168.1.6/'

winName = 'ESP32 CAMERA'

cv2.namedWindow(winName,cv2.WINDOW_AUTOSIZE)

#scale_percent = 80 # percent of original size #for image processing

classNames = []

classFile = 'coco.names'

with open(classFile,'rt') as f:

classNames = f.read().rstrip('\n').split('\n')

configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'

weightsPath = 'frozen_inference_graph.pb'

net = cv2.dnn_DetectionModel(weightsPath,configPath)

net.setInputSize(320,320)

#net.setInputSize(480,480)

net.setInputScale(1.0/127.5)

net.setInputMean((127.5, 127.5, 127.5))

net.setInputSwapRB(True)

while(1):

imgResponse = urllib.request.urlopen (url) # here open the URL

imgNp = np.array(bytearray(imgResponse.read()),dtype=np.uint8)

img = cv2.imdecode (imgNp,-1) #decodificamos

img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE) # vertical

#img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #black and white

classIds, confs, bbox = net.detect(img,confThreshold=0.5)

print(classIds,bbox)

if len(classIds) != 0:

for classId, confidence,box in zip(classIds.flatten(),confs.flatten(),bbox):

cv2.rectangle(img,box,color=(0,255,0),thickness = 3) #mostramos en rectangulo lo que se encuentra

cv2.putText(img, classNames[classId-1], (box[0]+10,box[1]+30), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0),2)

cv2.imshow(winName,img) # show the picture

#wait for ESC to be pressed to end the program

tecla = cv2.waitKey(5) & 0xFF

if tecla == 27:

break

cv2.destroyAllWindows()

Note – Make sure to replace <ESP32-CAM-IP-ADDRESS> it with the IP address of your ESP32-CAM module.

url = 'http://192.168.1.6/cam-hi.jpg'

1	url = 'http://192.168.1.6/cam-hi.jpg'

Working- Object detection and identification using ESP32-CAM

Using the provided code can be a useful reference if you want to build another project that requires object detection. You can simply just copy the code and customize it as per your need.

Object detection and identification using ESP32-CAM and OpenCV is a powerful combination for creating computer vision applications.

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ESP32-CAM Object Detection Using OpenCV, Yolov3 In Python

Required Materials

ESP32 CAM Module

ESP32 CAM Pinout

Connection Between ESP32 Cam & USB-To-TTL

Arduino IDE configuration for ESP32-CAM

Software and Libraries

Arduino Code – ESP32-CAM Object Detection With OpenCV

Python Code & Libriries

To install OpenCV

Install the necessary libraries

Coco.names

Python code – ESP32-CAM Object detection Using OpenCV

Working- Object detection and identification using ESP32-CAM

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