Just to confirm, you want a C++ program for Raspberry Pi using OpenCV to:

1. Capture frames from the camera.
2. Compare them against three reference images (C2T2S1.png, C3T3S2.png, C2T2S2.png).
3. Identify if any of these images appear in the scene using feature detection methods.
4. Display "Count Shapes" on the LCD for 5 seconds when an image is detected.
5. Depending on the detected image:
   • If C2T2S1.png is found, display "C: 2 T:2 S:1" for 5 seconds.
   • If C3T3S2.png is found, display "C: 3 T:3 S:2" for 5 seconds.
   • If C2T2S2.png is found, display "C: 2 T:2 S:2" for 5 seconds.
6. Repeat the detection process in a loop.
7. Use the same LCD1602 wiring setup as in your example code.

Does this match your requirements? Let me know if there are any additional functionalities you'd like to include!

Great! I will write a C++ program that uses OpenCV to capture frames from the camera, detect any of the three specified images (C2T2S1.png, C3T3S2.png, C2T2S2.png), and display the appropriate message on an LCD1602 screen.

The program will:

1. Capture live frames from the camera.
2. Compare them with the reference images using feature detection techniques.
3. Display "Count Shapes" for 5 seconds when any of the images are detected.
4. Show the corresponding shape count (C: X T: X S: X) for 5 seconds based on which image is recognized.
5. Loop the detection process continuously.

I will now write and provide the complete C++ code for you

Raspberry Pi C++ Program: OpenCV Image Detection with LCD1602

To meet the requirements, we'll create a C++ program that uses OpenCV for feature-based image detection and the WiringPi LCD library for displaying messages on a 16×2 LCD (LCD1602). The program continuously captures camera frames, checks for any of the three reference images in the frame using ORB feature matching, and updates the LCD with the appropriate messages. Below are the main steps and a complete code example.

1. Initialize Camera and LCD Display

First, set up the Raspberry Pi camera and the LCD. We use OpenCV's VideoCapture to access the camera, and WiringPi to interface with the LCD1602. The LCD is initialized with the same pin wiring as in the example code (using WiringPi pin numbering for RS, E, D4–D7). In 4-bit mode, only those four data pins (D4–D7) are used along with RS (register select) and E (enable). We call wiringPiSetup() and then lcdInit() with parameters for 2 rows, 16 columns, 4-bit mode, and the pin numbers:

• LCD RS (Register Select) – e.g. WiringPi pin 25
• LCD E (Enable) – e.g. WiringPi pin 24
• LCD D4–D7 (Data lines) – e.g. WiringPi pins 23, 22, 21, 14

This matches typical LCD1602 wiring on Pi (you may adjust pin numbers if your wiring differs). After initialization, we'll have an lcd handle to send text to the display.

2. Load Reference Images and Compute Features

Load the three reference images from the specified file paths using cv::imread. Convert them to grayscale (if not already) for feature detection. We use OpenCV's ORB (Oriented FAST and Rotated BRIEF) feature detector because it’s fast and free from patent restrictions (unlike SIFT/SURF). Create an ORB detector with cv::ORB::create() and use detectAndCompute() on each reference image to obtain keypoints and descriptors. Doing this once at startup is important for efficiency, so we don't recompute features for the reference images every frame.

cpp
cv::Ptr<cv::ORB> orb = cv::ORB::create();
std::vector<cv::KeyPoint> kp1, kp2, kp3;
cv::Mat desc1, desc2, desc3;
orb->detectAndCompute(refImage1, cv::noArray(), kp1, desc1);
orb->detectAndCompute(refImage2, cv::noArray(), kp2, desc2);
orb->detectAndCompute(refImage3, cv::noArray(), kp3, desc3);

Here, refImage1 is the cv::Mat for C2T2S1.png, etc. The ORB detector finds keypoints and encodes them into descriptors (feature vectors) for each reference image.

3. Capture Frames and Detect Features in Real-Time

Start an infinite loop to continuously capture frames from the camera. For each frame:

• Grab a frame from VideoCapture.
• Convert the frame to grayscale (cv::cvtColor) since feature detection works best on single-channel images.
• Use ORB’s detectAndCompute() on the frame to get keypoints and descriptors of the scene.

We reuse the same ORB instance for every frame to avoid reinitialization overhead. The descriptor of the frame will be matched against the descriptors of our reference images.

4. Match Features to Recognize Reference Images

For each reference image, use a feature matcher to compare the frame’s descriptors with the reference's descriptors. A common choice for binary descriptors like ORB is cv::BFMatcher with Hamming distance. We perform K-Nearest Neighbors matching (k=2) and apply Lowe's ratio test to find good matches. The ratio test filters out false matches by ensuring the best match is significantly closer than the second-best match (e.g. distance ratio < 0.7).

After obtaining "good" matches, we check if there are enough matches to reliably identify the object. Typically, we require a minimum number of matches (e.g., 8 or 10) to consider the object detected. If the count is below this threshold, the detection for that image is discarded as unreliable.

If the threshold is met, we further verify by computing a homography using cv::findHomography with RANSAC. This finds a 3×3 perspective transformation between the reference image and the scene. RANSAC will discard outlier correspondences, giving us a robust check: if a valid homography is found (meaning the reference image’s keypoints can map onto the frame), then the reference image is truly present in the frame. This two-step process (ratio test + homography) greatly reduces false positives in detection.

5. Update LCD1602 with Messages

When a reference image is recognized in the frame, we update the LCD display accordingly:

• Phase 1: Display “Count Shapes” for 5 seconds. This message prompts the user that the shape count will follow. We can use lcdClear() to clear any previous text, then position the cursor at the start (lcdPosition(lcd, 0, 0)) and use lcdPuts(lcd, "Count Shapes") to print the message. We then delay 5 seconds before changing the text. (In WiringPi, delay(5000) will pause 5 seconds, or we can use sleep(5) as in the example code.)

• Phase 2: After 5 seconds, clear the display and show the specific count for that image for another 5 seconds. For example, if C2T2S1.png was detected, we display “C: 2 T:2 S:1”. The program should format the string exactly as specified (with spaces after the colons). Use lcdPuts again to write the count string. Keep it displayed for 5 seconds.

We use the same LCD functions as the provided example (like lcdPuts to print strings). A brief pause (delay) is inserted to hold each message on screen for the specified duration.

To avoid flicker or constantly rewriting the same message, the code can track the last detected image. Only update the LCD when a new detection occurs or when the detected image changes. If no known image is detected in a frame, the LCD can remain unchanged or be cleared after some timeout (this is optional and was not explicitly specified).

6. Continuous Loop and Resource Management

These steps run continuously in a loop to constantly monitor the camera feed and update the LCD in real-time. Proper resource management and efficiency considerations include:

• Reusing Objects: Initialize the ORB detector and matcher once, and reuse them every frame. Precompute reference descriptors once at start. This avoids repetitive costly operations and ensures the loop runs efficiently.
• Memory Management: OpenCV’s smart pointers and cv::Mat manage memory internally. After processing each frame, old cv::Mat data will be replaced or freed as new frames come in. We should also release the camera (cap.release()) if the loop ends.
• Performance: ORB is fast, but if needed, one can downscale frames or limit ORB to a maximum number of features to maintain speed. The chosen MIN_MATCH_COUNT (e.g., 8 or 10) balances sensitivity and false positives – it can be tuned based on the scene complexity.

Finally, ensure the program is compiled with the proper libraries: link against OpenCV (-lopencv_core -lopencv_highgui -lopencv_features2d ...) and WiringPi (-lwiringPi -lwiringPiDev). Now, let's put everything together in code.

Complete C++ Code Example

cpp
#include <opencv2/opencv.hpp>
#include <wiringPi.h>
#include <lcd.h>
#include <iostream>

// Use WiringPi pin numbers for LCD pins (adjust if needed)
#define LCD_RS  25  // Register Select pin
#define LCD_E   24  // Enable pin
#define LCD_D4  23  // Data bit 4
#define LCD_D5  22  // Data bit 5
#define LCD_D6  21  // Data bit 6
#define LCD_D7  14  // Data bit 7

int main() {
    // Initialize WiringPi and LCD
    if (wiringPiSetup() < 0) {
        std::cerr << "WiringPi setup failed\n";
        return 1;
    }
    int lcd = lcdInit(2, 16, 4, LCD_RS, LCD_E, LCD_D4, LCD_D5, LCD_D6, LCD_D7, 0, 0, 0, 0);
    if (lcd == -1) {
        std::cerr << "LCD initialization failed\n";
        return 1;
    }

    // Load reference images in grayscale
    cv::Mat ref1 = cv::imread("/home/pi/Desktop/C2T2S1.png", cv::IMREAD_GRAYSCALE);
    cv::Mat ref2 = cv::imread("/home/pi/Desktop/C3T3S2.png", cv::IMREAD_GRAYSCALE);
    cv::Mat ref3 = cv::imread("/home/pi/Desktop/C2T2S2.png", cv::IMREAD_GRAYSCALE);
    if (ref1.empty() || ref2.empty() || ref3.empty()) {
        std::cerr << "Error: Could not load all reference images.\n";
        return 1;
    }

    // Create ORB feature detector and compute reference features
    cv::Ptr<cv::ORB> orb = cv::ORB::create();
    std::vector<cv::KeyPoint> kp1, kp2, kp3;
    cv::Mat desc1, desc2, desc3;
    orb->detectAndCompute(ref1, cv::noArray(), kp1, desc1);
    orb->detectAndCompute(ref2, cv::noArray(), kp2, desc2);
    orb->detectAndCompute(ref3, cv::noArray(), kp3, desc3);

    // Create a BFMatcher for Hamming distance (suitable for ORB)
    cv::BFMatcher matcher(cv::NORM_HAMMING);

    // Open camera stream
    cv::VideoCapture cap(0);
    if (!cap.isOpened()) {
        std::cerr << "Error: Could not open the camera.\n";
        return 1;
    }

    const int MIN_MATCH_COUNT = 8;  // Minimum matches to confirm detection
    std::string lastDetected = "";  // Remember last detected image to avoid repeat messages

    while (true) {
        // Capture a frame
        cv::Mat frame;
        if (!cap.read(frame)) {
            std::cerr << "Error: Camera frame not read properly.\n";
            break;  // Break loop on camera error or end
        }

        // Convert to grayscale for feature detection
        cv::Mat gray;
        cv::cvtColor(frame, gray, cv::COLOR_BGR2GRAY);

        // Detect ORB features in the frame
        std::vector<cv::KeyPoint> frameKeypoints;
        cv::Mat frameDesc;
        orb->detectAndCompute(gray, cv::noArray(), frameKeypoints, frameDesc);
        if (frameDesc.empty()) {
            // No features detected in this frame; continue to next frame
            continue;
        }

        // Match frame descriptors with each reference image's descriptors using KNN matching
        std::vector<std::vector<cv::DMatch>> knnMatches1, knnMatches2, knnMatches3;
        matcher.knnMatch(desc1, frameDesc, knnMatches1, 2);
        matcher.knnMatch(desc2, frameDesc, knnMatches2, 2);
        matcher.knnMatch(desc3, frameDesc, knnMatches3, 2);

        // Apply Lowe's ratio test to filter good matches
        std::vector<cv::DMatch> goodMatches1, goodMatches2, goodMatches3;
        for (const auto& m : knnMatches1) {
            if (m.size() == 2 && m[0].distance < 0.7f * m[1].distance) {
                goodMatches1.push_back(m[0]);
            }
        }
        for (const auto& m : knnMatches2) {
            if (m.size() == 2 && m[0].distance < 0.7f * m[1].distance) {
                goodMatches2.push_back(m[0]);
            }
        }
        for (const auto& m : knnMatches3) {
            if (m.size() == 2 && m[0].distance < 0.7f * m[1].distance) {
                goodMatches3.push_back(m[0]);
            }
        }

        // Determine which (if any) reference image is detected
        std::string detectedImage = "";
        // Check each reference against the threshold and verify by homography
        if (goodMatches1.size() >= MIN_MATCH_COUNT) {
            std::vector<cv::Point2f> refPoints, scenePoints;
            for (const auto& match : goodMatches1) {
                refPoints.push_back(kp1[match.queryIdx].pt);
                scenePoints.push_back(frameKeypoints[match.trainIdx].pt);
            }
            cv::Mat H = cv::findHomography(refPoints, scenePoints, cv::RANSAC);
            if (!H.empty()) {
                detectedImage = "C2T2S1";
            }
        }
        if (detectedImage.empty() && goodMatches2.size() >= MIN_MATCH_COUNT) {
            std::vector<cv::Point2f> refPoints, scenePoints;
            for (const auto& match : goodMatches2) {
                refPoints.push_back(kp2[match.queryIdx].pt);
                scenePoints.push_back(frameKeypoints[match.trainIdx].pt);
            }
            cv::Mat H = cv::findHomography(refPoints, scenePoints, cv::RANSAC);
            if (!H.empty()) {
                detectedImage = "C3T3S2";
            }
        }
        if (detectedImage.empty() && goodMatches3.size() >= MIN_MATCH_COUNT) {
            std::vector<cv::Point2f> refPoints, scenePoints;
            for (const auto& match : goodMatches3) {
                refPoints.push_back(kp3[match.queryIdx].pt);
                scenePoints.push_back(frameKeypoints[match.trainIdx].pt);
            }
            cv::Mat H = cv::findHomography(refPoints, scenePoints, cv::RANSAC);
            if (!H.empty()) {
                detectedImage = "C2T2S2";
            }
        }

        // Update LCD if a new image is detected
        if (!detectedImage.empty() && detectedImage != lastDetected) {
            lastDetected = detectedImage;
            // Phase 1: Display "Count Shapes"
            lcdClear(lcd);
            lcdPosition(lcd, 0, 0);               // top-left corner of LCD
            lcdPuts(lcd, "Count Shapes");
            delay(5000);                          // wait 5 seconds (5000 ms)
            // Phase 2: Display the specific counts
            lcdClear(lcd);
            lcdPosition(lcd, 0, 0);
            if (detectedImage == "C2T2S1") {
                lcdPuts(lcd, "C: 2 T:2 S:1");
            } else if (detectedImage == "C3T3S2") {
                lcdPuts(lcd, "C: 3 T:3 S:2");
            } else if (detectedImage == "C2T2S2") {
                lcdPuts(lcd, "C: 2 T:2 S:2");
            }
            delay(5000);  // keep the count displayed for 5 seconds
            // After this delay, the loop continues and may update the display again if needed
        }

        // (Optional) If no image is detected, you could clear or show an idle message here.
    }

    // Cleanup: release camera and clear LCD
    cap.release();
    lcdClear(lcd);
    return 0;
}

Explanation: In this program, we initialize the LCD with WiringPi (using the same pin setup as the reference example) and load the three reference images. We use ORB feature detection to find keypoints and descriptors in both the reference images and each camera frame. Using a brute-force matcher with Hamming distance, we compare the frame’s features to each reference’s features and apply Lowe’s ratio test to filter matches. If enough matches are found (at least 8 in this code), we compute a homography to verify the presence of the target image in the scene. Once an image is recognized, the LCD is updated in two steps: first displaying "Count Shapes", then the corresponding counts (C = circles, T = triangles, S = squares) for that image, each for 5 seconds. The loop then continues to process new frames, ensuring the display always reflects the latest detection. This approach uses robust feature matching instead of simple shape counting, making the image detection more reliable in varied conditions.