2021-Galilee-L3-Sensor-Glasses
Lunette intelligente permettant de venir en aide aux personnes aveugles ou aux personnes présentant une forte déficience visuelle à l'aide d'une détection d'obstacle et d'une reconnaissance faciale.
Slides & Videos
Members
Name | Contribution |
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SELVARAJ SANJEEV | Pour commencer, je tiens à préciser que j'ai effectué l'ensemble du projet en présence de mon camarade Sami COSTA. En effet, ayant la chance d'habiter à coté, chaque semaine nous nous sommes réunis pour travailler le projet ensemble environ 3 heures par semaines en dehors des séances de tps. Par conséquent notre contribution est vraiment équivalente sur tous les points du projet. Tout d'abord, dans les 2 premières semaines(semaines 1 et 2), j'ai participé au choix de sujet ainsi qu'à l'état de l'art et au choix de la problématique. Grâce à l'etat de l'art j'ai pu choisir les composants dont nous avions besoin mais avant de les commander, nous voulions être sûr d'être capable de coder nos idées. Nous avons donc avant l'achat des composants, codé la fonction détection d'obstacle dans la semaine qui a suivie (semaine 3). Pour cette fonction, l'ensemble du matériel était déjà présent dans les boites fournies en séance de tp. C'est à dire, la carte arduino, le passive buzzer et le capteur ultrason. Notre fonction a tout de suite fonctionnée sans problème. Pour le code nous avons utilisé le Pdf "kit de démarrage arduino" fournis en séance de TP. Dans la semaine 4, j'ai participé au codage de la reconnaissance faciale. Pour cela, n'ayant aucunes connaissances en intelligence artificielle, nous avons cherché des tutoriels arduino sur google et des tutoriels sur youtube. Nous nous sommes inspirés d'un document et d'une vidéo, voici les liens. https://randomnerdtutorials.com/installing-the-esp32-board-in-arduino-ide-windows-instructions/ https://youtu.be/q-KIpFIbRMk Dans la semaine 5, j'ai participé aux commandes du matériel, au branchement, à l'assemblage et à la présentation finale. Le point où nous avons rencontré le plus de problèmes était dans la reconnaissance faciale. Au début nous n'arrivions même pas à installer notre ESP32-CAM sur arduino. Puis ensuite nous avons eu de gros probèmes : "scketch, croquis trop gros" que nous avons pu régler grace a ce forum : http://www.abcelectronique.com/forum/showthread.php?t=102137. Pour résumé : - Choix du projet et de la problématique - Codage - Assemblage - Commande Matériaux - Branchement - Montage Vidéo - Rapport - Soutenance - Conclusion |
COSTA SAMI | Pour commencer, je tiens à préciser que j'ai effectué l'ensemble du projet en présence de mon camarade Sanjeev Selvaraj. Ma contribution est vraiment égale, car nous nous sommes réuni chaque semaine pour travailler ensemble, par conséquent nous avons exactement le même fichier "contribution", rien ne diffère. En effet, ayant la chance d'habiter à coté, chaque semaine nous nous sommes réunis pour travailler le projet ensemble environ 3 heures par semaines en dehors des séances de tps. Par conséquent notre contribution est vraiment équivalente sur tous les points du projet. Tout d'abord, dans les 2 premières semaines(semaines 1 et 2), j'ai participé au choix de sujet ainsi qu'à l'état de l'art et au choix de la problématique. Grâce à l'etat de l'art j'ai pu choisir les composants dont nous avions besoin mais avant de les commander, nous voulions être sûr d'être capable de coder nos idées. Nous avons donc avant l'achat des composants, codé la fonction détection d'obstacle dans la semaine qui a suivie (semaine 3). Pour cette fonction, l'ensemble du matériel était déjà présent dans les boites fournies en séance de tp. C'est à dire, la carte arduino, le passive buzzer et le capteur ultrason. Notre fonction a tout de suite fonctionnée sans problème. Pour le code nous avons utilisé le Pdf "kit de démarrage arduino" fournis en séance de TP. Dans la semaine 4, j'ai participé au codage de la reconnaissance faciale. Pour cela, n'ayant aucunes connaissances en intelligence artificielle, nous avons cherché des tutoriels arduino sur google et des tutoriels sur youtube. Nous nous sommes inspirés d'un document et d'une vidéo, voici les liens. https://randomnerdtutorials.com/installing-the-esp32-board-in-arduino-ide-windows-instructions/ https://youtu.be/q-KIpFIbRMk Dans la semaine 5, j'ai participé aux commandes du matériel, au branchement, à l'assemblage et à la présentation finale. Le point où nous avons rencontré le plus de problèmes était dans la reconnaissance faciale. Au début nous n'arrivions même pas à installer notre ESP32-CAM sur arduino. Puis ensuite nous avons eu de gros probèmes : "scketch, croquis trop gros" que nous avons pu régler grace a ce forum : http://www.abcelectronique.com/forum/showthread.php?t=102137. En résumé : - Choix du projet et de la problématique - Codage - Assemblage - Commande Matériaux - Branchement - Montage Vidéo - Rapport - Soutenance - Conclusion |
State of the Art
Business Aspect
Pour la réalisation de notre projet, nous avons fait des recherches sur internet et nous avons remarqué que le marché des lunettes pour aveugles n’était pas présent en France que depuis les années 2017 et 2018. Par conséquent, il est très difficile de trouver des chiffres concernant les ventes. La plupart des articles que nous avons lu nous ont amenés sur des entreprises étrangères, la plupart du temps chinoises. Pour le peu de lunettes existantes sur le marché que nous avons trouvé, la totalité fonctionnait avec l’intelligence artificielle. Les fonctions qui revenaient souvent sont la détection d’obstacles et la reconnaissance d’objets. Les prix varient énormément. Nous avons vu des lunettes qui coutent 300 € alors que certaines coutent 5000 €. Nous avons remarqué qu’il y’a encore très peu de concurrence sur ce marché et donc le peu de concurrence existant cache leur technologie et les matériaux utilisés. Il est donc très difficile de s’inspirer des produits déjà existants sur le marché. Les lunettes pour aveugles sont encore trop peu connues du grand public. Nous n’avons encore jamais rencontré des publicités ou des annonces marketing visant à vendre ou faire connaître ce genre de produit, ce qui prouve que c’est un néo-marché en pleine croissance. Les lunettes intelligentes AngelEye ont été créées par le groupe NextVPU à Shangai en Chine. Ce sont des lunettes qui conseillent les personnes aveugles et malvoyantes à percevoir le monde, elles sont basées sur la technologie de vision par ordinateur et l’intelligence artificielle la plus avancée. 1ère fonction : Détection d’obstacles : Les lunettes détecteront tout obstacle que vous pourriez heurter puis communiqueront à l’utilisateur par audio. 2ème fonction : Reconnaissance avancée : Les lunettes reconnaissent 20 catégories d’objets, y compris le passage pour piétons, les escaliers, la porte, la sortie, le billet de caisse, la couleur, le texte, les personnes, etc. et communiquent à l’utilisateur par la voix. Tout est fait localement et aucune connexion sans fil n’est nécessaire. 3ème fonction : Rapport de localisation : avec un simple bouton-poussoir, il rapporte les informations de localisation de l’utilisateur, par exemple le nom de la rue, le nom de la prochaine croix, la distance à la prochaine croix, la direction de la direction, les ROI à proximité, etc. Prix de lancement : 399$ Avantage : Prix très abordable par rapport à la concurrence. Inconvénient : Reconnaissance d’obstacle limitée
Légère et compacte, cette machine à lire portable est un outil formidable pour les malvoyants et aveugles. Lecture de texte, reconnaissance des visages, détection des couleurs et des billets de banque, lecture de l’heure. OrCam MyEye est un appareil à activation vocale révolutionnaire utilisable sur presque toutes les lunettes. Il allume instantanément le texte d’un livre, d’un smartphone ou de toute autre surface. Il reconnaît les visages et vous aide à faire vos cours, à travailler et à vivre de manière indépendante! OrCam MyEye transmet oralement les informations visuelles en temps réel et sans connexion. Prix: 4750 euros Avantage: Reconnaissance faciale et reconnaissance objet très avancé Inconvénient: Pas de détection d’obstacle et prix trop élevé
Technical Aspect
Dimensions et poids :
Device: 22.5 gr/0.79 oz
Device+Box: 553 gr/19.5 oz 76 x 21 x 14.9 mm/3 x 0.83 x 0.59 in
Alimentation : 3.7VDC
Batterie : 320mAH nominal
Camera : 13Megapixels
Langues : Disponible dans plus de 20 langues, dont l’anglais, l’espagnol, l’allemand, le français, le portugais, le russe, l’arabe, le mandarin, le japonais, etc.
La boîte contient :
Project Description
Problem Definition
Cette forte hausse est notamment dû à la forte augmentation de l'utilisation des nouvelles technologies tels que les pc et les smartphones. En effet, les lumières bleues présentent dans les écrans accélèrent de manière importante la déficience visuelle et peuvent avoir dans certains cas extrême des répercussions irréversible. Pour exemple, En Chine, un homme est devenu temporairement aveugle d'un œil après avoir passé la nuit à jouer sur son téléphone.
Que pouvons-nous apporter pour améliorer le quotidien des personnes aveugles ou présentant une forte déficience visuelle ?
Il était important pour nous de choisir un projet répondant un réel problème et apportant une solution à ce problème. Nous avons choisi le domaine de la déficience visuelle car pour les aveugles, aujourd’hui, peu de solutions concrètes ou complètes sont apportées.
Challenges & Motivation
Le but de ces lunettes seraient de détecter et d'avertir la personne malvoyante lorsqu’un objet se met au travers de son chemin en émettant un bruit sonore.
En outre, nous voulons intégrer un capteur ultrason directement dans les lunettes qui permettra de détecter l’objet qui se situe devant la personne à une certaine distance et avertira la personne en émettant un bruit sonore grâce à un buzzer arduino.
Une deuxième fonction permettrait d'identifier précisément les personnes à l’aide d’une reconnaissance faciale. Pour cela, une caméra sera directement intégrée aux lunettes. Elle identifiera les personnes enregistrés dans le serveur web et considérera les autres comme des inconnus.
Pour la détection des obstacles, nous avons pensé à un capteur ultrasonic, le HC-SR04. Relativement facile à programmer, et précis. Quand celui-ci détecte un obstacle, le buzzer arduino émettra un bruit sonore pour avertir la personne.
Pour la reconnaissance faciale, nous voulons intégrer la caméra ESP32-CAM directement aux lunettes qui reconnaitra les personnes enregistrés dans le serveur web.
Real and Complete Usecases
Premier cas d’utilisation: Détection d’obstacle à partir de 1,5m La personne rencontre un obstacle. S’il se rapproche à une distance inférieure ou égale à 1m50 l’alerte sonore se déclenche. Si la personne se rapproche à une distance entre 0,75m et 1m l’alerte sonore s’accélère ainsi que le ton de l’alerte sonore augmente. Si la personne s’approche encore de l’obstacle dans une distance comprise entre 0,5m et 0,75m l’alerte sonore s’accélère encore et devient plus forte. Enfin, si le sujet dépasse la barre symbolique dans 0,5m, nous considérons que le danger est imminent et que l’alerte sonore devient une alerte continue. 2ème cas d’exploitation: Reconnaissance faciale Si nous démarrons la caméra et qu’on la connecte au réseau wifi de l’ordinateur, puis qu’on se connecte au serveur web qu’on a créé, et qu’on commence le flux de la caméra, alors nous pouvons activer la reconnaissance faciale. Ainsi nous pouvons inscrire des visages qui seront enregistrés par la caméra, tous les autres seront prévus comme des intrus. Il n’y a pas vraiment de cas d’utilisation pour cette fonction, comme pour la détection d’obstacle. Nous aurions voulu installer un bouton sur les lunettes qui une fois actionnée, lancerais le flux de la caméra et la reconnaissance de faciale, mais par manque de temps nous n’avons pu mettre cela en œuvre, malgré tous les efforts fournis.
Technical Description
Pour la détection d’obstacle:
Le capteur ultrasonic est un capteur à ultrason low cost. Ce capteur fonctionne avec une tension d’alimentation de 5 volts, dispose d’un angle de mesure de 15° environ et permet de faire des mesures de distance entre 2 centimètres et 4 mètres avec une précision de 3mm.
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On envoie une impulsion HIGH de 10µs sur la broche TRIGGER du capteur.
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Le capteur envoie alors une série de 8 impulsions ultrasoniques à 40KHz.
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Les ultrasons se propagent dans l’air jusqu’à toucher un obstacle et retourne dans l’autre sens vers le capteur.
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Le capteur détecte l’écho et clôture la prise de mesure.
Le signal sur la broche ECHO du capteur reste à HIGHT durant les étapes 3 et 4, ce qui permet de mesurer la durée de l’aller-retour des ultrasons et donc de déterminer la distance.
Dans notre programme, nous programmons le buzzer de sorte à ce que quand la distance calculée par le capteur ultrason dont la méthode de fonctionnement a été stipulé plus haut est inférieur ou égale à 1M50, celui-ci émette un son. Ainsi, dans un premier temps le capteur ultrason calcule la distance et envoie l’information à l’Arduino et une fois que l’information captée par l’Arduino est égale ou inférieur a une distance de 1m50, il envoie l’information au buzzer d’émettre un signal tant que celui-ci est compris dans la distance que nous avons codé.
Pour la reconnaissance faciale :
La carte ESP32-Cam intègre un processeur ESP32 et une caméra OV2640 (2M pixels). Elle consiste à transmettre en WIFI et en direct un flux vidéo, des images.
En plus, elle est équipée d’un lecteur de cartes microSD qui permet de stocker des images et des vidéos.
Programmation de la carte esp32 cam : Il s’agit de charger le serveur vidéo web dans la carte. Pour téléverser tout le programme dans la carte esp32 cam, nous avons utiliser le module arduino uno. On réalise le câblage en branchant 5V vers 5V, VOT vers TX0, VOR vers RX0 et GND vers GND de la carte arduino. Lors du téléversement, il faut relier GPI0 au GND et il faudra penser à l’enlever pour utiliser la carte esp32 cam. Après le branchement, on lance l’IDE Arduino pour pouvoir récupérer le code et réaliser le téléversement. Il faudra aller dans Outils->Gestionnaire de cartes->Sélectionner ESP32 WroverModule. Puis, dans fichier->exemples->ESP32->Caméra, il faut ouvrir le fichier CameraWebServeur.ino qui contient l’exemple de code qui permet la création du serveur web de la carte ESP32 cam. Dans le code, il faudra ajouter le SSID et le mot de passe du wifi locale qui est également utilisé sur le PC. Une fois le code téléversé, il faudra penser à retirer le câble du flash mode et appuyer sur le bouton Reset qui se trouve sur la carte ESP32 cam. Une fois le téléchargement terminé, il faut ouvrir le moniteur série pour récupérer l’adresse IP du serveur web de la caméra ESP32 et il faut saisir l’adresse IP dans un navigateur web pour avoir accès au serveur web de la carte ESP32 cam où on peut faire des réglages de résolution, effets spéciaux, prix de photo etc. On peut même activer la détection et la reconnaissance du visage pour pouvoir enregistrer les visages pour qu’elle reconnaisse les visages enregistrés.
Hardware
Software
Arduino Code
const int TrigPin = 2; const int EchoPin = 3; float cm; #define buzzer 5 void setup(){ Serial.begin(9600); pinMode(TrigPin, OUTPUT); pinMode(EchoPin, INPUT); } void loop(){ digitalWrite(TrigPin, LOW); delayMicroseconds(2); digitalWrite(TrigPin, HIGH); delayMicroseconds(10); digitalWrite(TrigPin, LOW); cm = pulseIn(EchoPin, HIGH) / 58.0; //The echo time is converted into cm cm = (int(cm * 100.0)) / 100.0; //Keep two decimal places Serial.print("Distance\t=\t"); Serial.print(cm); Serial.print("cm"); Serial.println(); delay(500); if (100 <= cm && cm <= 150){ tone(buzzer, 400, 100); } else if (75 <= cm && cm < 100){ tone(buzzer, 400, 200); } else if (50 <= cm && cm < 75){ tone(buzzer, 400, 300); } else if (0<= cm && cm < 50){ tone(buzzer, 400, 800); } }
#include "esp_camera.h" #include// // WARNING!!! Make sure that you have either selected ESP32 Wrover Module, // or another board which has PSRAM enabled // // Select camera model // n n fcf#define CAMERA_MODEL_WROVER_KIT //#define CAMERA_MODEL_ESP_EYE //#define CAMERA_MODEL_M5STACK_PSRAM //#define CAMERA_MODEL_M5STACK_WIDE #define CAMERA_MODEL_AI_THINKER #include "camera_pins.h" const char* ssid = "iPhone de San"; const char* password = "xperia_Z3"; void startCameraServer(); void setup() { Serial.begin(115200); Serial.setDebugOutput(true); Serial.println(); 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; //init with high specs to pre-allocate larger buffers if(psramFound()){ config.frame_size = FRAMESIZE_UXGA; config.jpeg_quality = 10; config.fb_count = 2; } else { config.frame_size = FRAMESIZE_SVGA; config.jpeg_quality = 12; config.fb_count = 1; } #if defined(CAMERA_MODEL_ESP_EYE) pinMode(13, INPUT_PULLUP); pinMode(14, INPUT_PULLUP); #endif // camera init esp_err_t err = esp_camera_init(&config); if (err != ESP_OK) { Serial.printf("Camera init failed with error 0x%x", err); return; } sensor_t * s = esp_camera_sensor_get(); //initial sensors are flipped vertically and colors are a bit saturated if (s->id.PID == OV3660_PID) { s->set_vflip(s, 1);//flip it back s->set_brightness(s, 1);//up the blightness just a bit s->set_saturation(s, -2);//lower the saturation } //drop down frame size for higher initial frame rate s->set_framesize(s, FRAMESIZE_QVGA); #if defined(CAMERA_MODEL_M5STACK_WIDE) s->set_vflip(s, 1); s->set_hmirror(s, 1); #endif WiFi.begin(ssid, password); while (WiFi.status() != WL_CONNECTED) { delay(500); Serial.print("."); } Serial.println(""); Serial.println("WiFi connected"); startCameraServer(); Serial.print("Camera Ready! Use 'http://"); Serial.print(WiFi.localIP()); Serial.println("' to connect"); } void loop() { // put your main code here, to run repeatedly: delay(10000); }
#include "esp_http_server.h" #include "esp_timer.h" #include "esp_camera.h" #include "img_converters.h" #include "camera_index.h" #include "Arduino.h" #include "fb_gfx.h" #include "fd_forward.h" #include "fr_forward.h" #define ENROLL_CONFIRM_TIMES 5 #define FACE_ID_SAVE_NUMBER 7 #define FACE_COLOR_WHITE 0x00FFFFFF #define FACE_COLOR_BLACK 0x00000000 #define FACE_COLOR_RED 0x000000FF #define FACE_COLOR_GREEN 0x0000FF00 #define FACE_COLOR_BLUE 0x00FF0000 #define FACE_COLOR_YELLOW (FACE_COLOR_RED | FACE_COLOR_GREEN) #define FACE_COLOR_CYAN (FACE_COLOR_BLUE | FACE_COLOR_GREEN) #define FACE_COLOR_PURPLE (FACE_COLOR_BLUE | FACE_COLOR_RED) typedef struct { size_t size; //number of values used for filtering size_t index; //current value index size_t count; //value count int sum; int * values; //array to be filled with values } ra_filter_t; typedef struct { httpd_req_t *req; size_t len; } jpg_chunking_t; #define PART_BOUNDARY "123456789000000000000987654321" static const char* _STREAM_CONTENT_TYPE = "multipart/x-mixed-replace;boundary=" PART_BOUNDARY; static const char* _STREAM_BOUNDARY = "\r\n--" PART_BOUNDARY "\r\n"; static const char* _STREAM_PART = "Content-Type: image/jpeg\r\nContent-Length: %u\r\n\r\n"; static ra_filter_t ra_filter; httpd_handle_t stream_httpd = NULL; httpd_handle_t camera_httpd = NULL; static mtmn_config_t mtmn_config = {0}; static int8_t detection_enabled = 0; static int8_t recognition_enabled = 0; static int8_t is_enrolling = 0; static face_id_list id_list = {0}; static ra_filter_t * ra_filter_init(ra_filter_t * filter, size_t sample_size){ memset(filter, 0, sizeof(ra_filter_t)); filter->values = (int *)malloc(sample_size * sizeof(int)); if(!filter->values){ return NULL; } memset(filter->values, 0, sample_size * sizeof(int)); filter->size = sample_size; return filter; } static int ra_filter_run(ra_filter_t * filter, int value){ if(!filter->values){ return value; } filter->sum -= filter->values[filter->index]; filter->values[filter->index] = value; filter->sum += filter->values[filter->index]; filter->index++; filter->index = filter->index % filter->size; if (filter->count < filter->size) { filter->count++; } return filter->sum / filter->count; } static void rgb_print(dl_matrix3du_t *image_matrix, uint32_t color, const char * str){ fb_data_t fb; fb.width = image_matrix->w; fb.height = image_matrix->h; fb.data = image_matrix->item; fb.bytes_per_pixel = 3; fb.format = FB_BGR888; fb_gfx_print(&fb, (fb.width - (strlen(str) * 14)) / 2, 10, color, str); } static int rgb_printf(dl_matrix3du_t *image_matrix, uint32_t color, const char *format, ...){ char loc_buf[64]; char * temp = loc_buf; int len; va_list arg; va_list copy; va_start(arg, format); va_copy(copy, arg); len = vsnprintf(loc_buf, sizeof(loc_buf), format, arg); va_end(copy); if(len >= sizeof(loc_buf)){ temp = (char*)malloc(len+1); if(temp == NULL) { return 0; } } vsnprintf(temp, len+1, format, arg); va_end(arg); rgb_print(image_matrix, color, temp); if(len > 64){ free(temp); } return len; } static void draw_face_boxes(dl_matrix3du_t *image_matrix, box_array_t *boxes, int face_id){ int x, y, w, h, i; uint32_t color = FACE_COLOR_YELLOW; if(face_id < 0){ color = FACE_COLOR_RED; } else if(face_id > 0){ color = FACE_COLOR_GREEN; } fb_data_t fb; fb.width = image_matrix->w; fb.height = image_matrix->h; fb.data = image_matrix->item; fb.bytes_per_pixel = 3; fb.format = FB_BGR888; for (i = 0; i < boxes->len; i++){ // rectangle box x = (int)boxes->box[i].box_p[0]; y = (int)boxes->box[i].box_p[1]; w = (int)boxes->box[i].box_p[2] - x + 1; h = (int)boxes->box[i].box_p[3] - y + 1; fb_gfx_drawFastHLine(&fb, x, y, w, color); fb_gfx_drawFastHLine(&fb, x, y+h-1, w, color); fb_gfx_drawFastVLine(&fb, x, y, h, color); fb_gfx_drawFastVLine(&fb, x+w-1, y, h, color); #if 0 // landmark int x0, y0, j; for (j = 0; j < 10; j+=2) { x0 = (int)boxes->landmark[i].landmark_p[j]; y0 = (int)boxes->landmark[i].landmark_p[j+1]; fb_gfx_fillRect(&fb, x0, y0, 3, 3, color); } #endif } } static int run_face_recognition(dl_matrix3du_t *image_matrix, box_array_t *net_boxes){ dl_matrix3du_t *aligned_face = NULL; int matched_id = 0; aligned_face = dl_matrix3du_alloc(1, FACE_WIDTH, FACE_HEIGHT, 3); if(!aligned_face){ Serial.println("Could not allocate face recognition buffer"); return matched_id; } if (align_face(net_boxes, image_matrix, aligned_face) == ESP_OK){ if (is_enrolling == 1){ int8_t left_sample_face = enroll_face(&id_list, aligned_face); if(left_sample_face == (ENROLL_CONFIRM_TIMES - 1)){ Serial.printf("Enrolling Face ID: %d\n", id_list.tail); } Serial.printf("Enrolling Face ID: %d sample %d\n", id_list.tail, ENROLL_CONFIRM_TIMES - left_sample_face); rgb_printf(image_matrix, FACE_COLOR_CYAN, "ID[%u] Sample[%u]", id_list.tail, ENROLL_CONFIRM_TIMES - left_sample_face); if (left_sample_face == 0){ is_enrolling = 0; Serial.printf("Enrolled Face ID: %d\n", id_list.tail); } } else { matched_id = recognize_face(&id_list, aligned_face); if (matched_id >= 0) { Serial.printf("Match Face ID: %u\n", matched_id); rgb_printf(image_matrix, FACE_COLOR_GREEN, "Hello Subject %u", matched_id); } else { Serial.println("No Match Found"); rgb_print(image_matrix, FACE_COLOR_RED, "Intruder Alert!"); matched_id = -1; } } } else { Serial.println("Face Not Aligned"); //rgb_print(image_matrix, FACE_COLOR_YELLOW, "Human Detected"); } dl_matrix3du_free(aligned_face); return matched_id; } static size_t jpg_encode_stream(void * arg, size_t index, const void* data, size_t len){ jpg_chunking_t *j = (jpg_chunking_t *)arg; if(!index){ j->len = 0; } if(httpd_resp_send_chunk(j->req, (const char *)data, len) != ESP_OK){ return 0; } j->len += len; return len; } static esp_err_t capture_handler(httpd_req_t *req){ camera_fb_t * fb = NULL; esp_err_t res = ESP_OK; int64_t fr_start = esp_timer_get_time(); fb = esp_camera_fb_get(); if (!fb) { Serial.println("Camera capture failed"); httpd_resp_send_500(req); return ESP_FAIL; } httpd_resp_set_type(req, "image/jpeg"); httpd_resp_set_hdr(req, "Content-Disposition", "inline; filename=capture.jpg"); httpd_resp_set_hdr(req, "Access-Control-Allow-Origin", "*"); size_t out_len, out_width, out_height; uint8_t * out_buf; bool s; bool detected = false; int face_id = 0; if(!detection_enabled || fb->width > 400){ size_t fb_len = 0; if(fb->format == PIXFORMAT_JPEG){ fb_len = fb->len; res = httpd_resp_send(req, (const char *)fb->buf, fb->len); } else { jpg_chunking_t jchunk = {req, 0}; res = frame2jpg_cb(fb, 80, jpg_encode_stream, &jchunk)?ESP_OK:ESP_FAIL; httpd_resp_send_chunk(req, NULL, 0); fb_len = jchunk.len; } esp_camera_fb_return(fb); int64_t fr_end = esp_timer_get_time(); Serial.printf("JPG: %uB %ums\n", (uint32_t)(fb_len), (uint32_t)((fr_end - fr_start)/1000)); return res; } dl_matrix3du_t *image_matrix = dl_matrix3du_alloc(1, fb->width, fb->height, 3); if (!image_matrix) { esp_camera_fb_return(fb); Serial.println("dl_matrix3du_alloc failed"); httpd_resp_send_500(req); return ESP_FAIL; } out_buf = image_matrix->item; out_len = fb->width * fb->height * 3; out_width = fb->width; out_height = fb->height; s = fmt2rgb888(fb->buf, fb->len, fb->format, out_buf); esp_camera_fb_return(fb); if(!s){ dl_matrix3du_free(image_matrix); Serial.println("to rgb888 failed"); httpd_resp_send_500(req); return ESP_FAIL; } box_array_t *net_boxes = face_detect(image_matrix, &mtmn_config); if (net_boxes){ detected = true; if(recognition_enabled){ face_id = run_face_recognition(image_matrix, net_boxes); } draw_face_boxes(image_matrix, net_boxes, face_id); free(net_boxes->score); free(net_boxes->box); free(net_boxes->landmark); free(net_boxes); } jpg_chunking_t jchunk = {req, 0}; s = fmt2jpg_cb(out_buf, out_len, out_width, out_height, PIXFORMAT_RGB888, 90, jpg_encode_stream, &jchunk); dl_matrix3du_free(image_matrix); if(!s){ Serial.println("JPEG compression failed"); return ESP_FAIL; } int64_t fr_end = esp_timer_get_time(); Serial.printf("FACE: %uB %ums %s%d\n", (uint32_t)(jchunk.len), (uint32_t)((fr_end - fr_start)/1000), detected?"DETECTED ":"", face_id); return res; } static esp_err_t stream_handler(httpd_req_t *req){ camera_fb_t * fb = NULL; esp_err_t res = ESP_OK; size_t _jpg_buf_len = 0; uint8_t * _jpg_buf = NULL; char * part_buf[64]; dl_matrix3du_t *image_matrix = NULL; bool detected = false; int face_id = 0; int64_t fr_start = 0; int64_t fr_ready = 0; int64_t fr_face = 0; int64_t fr_recognize = 0; int64_t fr_encode = 0; static int64_t last_frame = 0; if(!last_frame) { last_frame = esp_timer_get_time(); } res = httpd_resp_set_type(req, _STREAM_CONTENT_TYPE); if(res != ESP_OK){ return res; } httpd_resp_set_hdr(req, "Access-Control-Allow-Origin", "*"); while(true){ detected = false; face_id = 0; fb = esp_camera_fb_get(); if (!fb) { Serial.println("Camera capture failed"); res = ESP_FAIL; } else { fr_start = esp_timer_get_time(); fr_ready = fr_start; fr_face = fr_start; fr_encode = fr_start; fr_recognize = fr_start; if(!detection_enabled || fb->width > 400){ if(fb->format != PIXFORMAT_JPEG){ bool jpeg_converted = frame2jpg(fb, 80, &_jpg_buf, &_jpg_buf_len); esp_camera_fb_return(fb); fb = NULL; if(!jpeg_converted){ Serial.println("JPEG compression failed"); res = ESP_FAIL; } } else { _jpg_buf_len = fb->len; _jpg_buf = fb->buf; } } else { image_matrix = dl_matrix3du_alloc(1, fb->width, fb->height, 3); if (!image_matrix) { Serial.println("dl_matrix3du_alloc failed"); res = ESP_FAIL; } else { if(!fmt2rgb888(fb->buf, fb->len, fb->format, image_matrix->item)){ Serial.println("fmt2rgb888 failed"); res = ESP_FAIL; } else { fr_ready = esp_timer_get_time(); box_array_t *net_boxes = NULL; if(detection_enabled){ net_boxes = face_detect(image_matrix, &mtmn_config); } fr_face = esp_timer_get_time(); fr_recognize = fr_face; if (net_boxes || fb->format != PIXFORMAT_JPEG){ if(net_boxes){ detected = true; if(recognition_enabled){ face_id = run_face_recognition(image_matrix, net_boxes); } fr_recognize = esp_timer_get_time(); draw_face_boxes(image_matrix, net_boxes, face_id); free(net_boxes->score); free(net_boxes->box); free(net_boxes->landmark); free(net_boxes); } if(!fmt2jpg(image_matrix->item, fb->width*fb->height*3, fb->width, fb->height, PIXFORMAT_RGB888, 90, &_jpg_buf, &_jpg_buf_len)){ Serial.println("fmt2jpg failed"); res = ESP_FAIL; } esp_camera_fb_return(fb); fb = NULL; } else { _jpg_buf = fb->buf; _jpg_buf_len = fb->len; } fr_encode = esp_timer_get_time(); } dl_matrix3du_free(image_matrix); } } } if(res == ESP_OK){ size_t hlen = snprintf((char *)part_buf, 64, _STREAM_PART, _jpg_buf_len); res = httpd_resp_send_chunk(req, (const char *)part_buf, hlen); } if(res == ESP_OK){ res = httpd_resp_send_chunk(req, (const char *)_jpg_buf, _jpg_buf_len); } if(res == ESP_OK){ res = httpd_resp_send_chunk(req, _STREAM_BOUNDARY, strlen(_STREAM_BOUNDARY)); } if(fb){ esp_camera_fb_return(fb); fb = NULL; _jpg_buf = NULL; } else if(_jpg_buf){ free(_jpg_buf); _jpg_buf = NULL; } if(res != ESP_OK){ break; } int64_t fr_end = esp_timer_get_time(); int64_t ready_time = (fr_ready - fr_start)/1000; int64_t face_time = (fr_face - fr_ready)/1000; int64_t recognize_time = (fr_recognize - fr_face)/1000; int64_t encode_time = (fr_encode - fr_recognize)/1000; int64_t process_time = (fr_encode - fr_start)/1000; int64_t frame_time = fr_end - last_frame; last_frame = fr_end; frame_time /= 1000; uint32_t avg_frame_time = ra_filter_run(&ra_filter, frame_time); Serial.printf("MJPG: %uB %ums (%.1ffps), AVG: %ums (%.1ffps), %u+%u+%u+%u=%u %s%d\n", (uint32_t)(_jpg_buf_len), (uint32_t)frame_time, 1000.0 / (uint32_t)frame_time, avg_frame_time, 1000.0 / avg_frame_time, (uint32_t)ready_time, (uint32_t)face_time, (uint32_t)recognize_time, (uint32_t)encode_time, (uint32_t)process_time, (detected)?"DETECTED ":"", face_id ); } last_frame = 0; return res; } static esp_err_t cmd_handler(httpd_req_t *req){ char* buf; size_t buf_len; char variable[32] = {0,}; char value[32] = {0,}; buf_len = httpd_req_get_url_query_len(req) + 1; if (buf_len > 1) { buf = (char*)malloc(buf_len); if(!buf){ httpd_resp_send_500(req); return ESP_FAIL; } if (httpd_req_get_url_query_str(req, buf, buf_len) == ESP_OK) { if (httpd_query_key_value(buf, "var", variable, sizeof(variable)) == ESP_OK && httpd_query_key_value(buf, "val", value, sizeof(value)) == ESP_OK) { } else { free(buf); httpd_resp_send_404(req); return ESP_FAIL; } } else { free(buf); httpd_resp_send_404(req); return ESP_FAIL; } free(buf); } else { httpd_resp_send_404(req); return ESP_FAIL; } int val = atoi(value); sensor_t * s = esp_camera_sensor_get(); int res = 0; if(!strcmp(variable, "framesize")) { if(s->pixformat == PIXFORMAT_JPEG) res = s->set_framesize(s, (framesize_t)val); } else if(!strcmp(variable, "quality")) res = s->set_quality(s, val); else if(!strcmp(variable, "contrast")) res = s->set_contrast(s, val); else if(!strcmp(variable, "brightness")) res = s->set_brightness(s, val); else if(!strcmp(variable, "saturation")) res = s->set_saturation(s, val); else if(!strcmp(variable, "gainceiling")) res = s->set_gainceiling(s, (gainceiling_t)val); else if(!strcmp(variable, "colorbar")) res = s->set_colorbar(s, val); else if(!strcmp(variable, "awb")) res = s->set_whitebal(s, val); else if(!strcmp(variable, "agc")) res = s->set_gain_ctrl(s, val); else if(!strcmp(variable, "aec")) res = s->set_exposure_ctrl(s, val); else if(!strcmp(variable, "hmirror")) res = s->set_hmirror(s, val); else if(!strcmp(variable, "vflip")) res = s->set_vflip(s, val); else if(!strcmp(variable, "awb_gain")) res = s->set_awb_gain(s, val); else if(!strcmp(variable, "agc_gain")) res = s->set_agc_gain(s, val); else if(!strcmp(variable, "aec_value")) res = s->set_aec_value(s, val); else if(!strcmp(variable, "aec2")) res = s->set_aec2(s, val); else if(!strcmp(variable, "dcw")) res = s->set_dcw(s, val); else if(!strcmp(variable, "bpc")) res = s->set_bpc(s, val); else if(!strcmp(variable, "wpc")) res = s->set_wpc(s, val); else if(!strcmp(variable, "raw_gma")) res = s->set_raw_gma(s, val); else if(!strcmp(variable, "lenc")) res = s->set_lenc(s, val); else if(!strcmp(variable, "special_effect")) res = s->set_special_effect(s, val); else if(!strcmp(variable, "wb_mode")) res = s->set_wb_mode(s, val); else if(!strcmp(variable, "ae_level")) res = s->set_ae_level(s, val); else if(!strcmp(variable, "face_detect")) { detection_enabled = val; if(!detection_enabled) { recognition_enabled = 0; } } else if(!strcmp(variable, "face_enroll")) is_enrolling = val; else if(!strcmp(variable, "face_recognize")) { recognition_enabled = val; if(recognition_enabled){ detection_enabled = val; } } else { res = -1; } if(res){ return httpd_resp_send_500(req); } httpd_resp_set_hdr(req, "Access-Control-Allow-Origin", "*"); return httpd_resp_send(req, NULL, 0); } static esp_err_t status_handler(httpd_req_t *req){ static char json_response[1024]; sensor_t * s = esp_camera_sensor_get(); char * p = json_response; *p++ = '{'; p+=sprintf(p, "\"framesize\":%u,", s->status.framesize); p+=sprintf(p, "\"quality\":%u,", s->status.quality); p+=sprintf(p, "\"brightness\":%d,", s->status.brightness); p+=sprintf(p, "\"contrast\":%d,", s->status.contrast); p+=sprintf(p, "\"saturation\":%d,", s->status.saturation); p+=sprintf(p, "\"sharpness\":%d,", s->status.sharpness); p+=sprintf(p, "\"special_effect\":%u,", s->status.special_effect); p+=sprintf(p, "\"wb_mode\":%u,", s->status.wb_mode); p+=sprintf(p, "\"awb\":%u,", s->status.awb); p+=sprintf(p, "\"awb_gain\":%u,", s->status.awb_gain); p+=sprintf(p, "\"aec\":%u,", s->status.aec); p+=sprintf(p, "\"aec2\":%u,", s->status.aec2); p+=sprintf(p, "\"ae_level\":%d,", s->status.ae_level); p+=sprintf(p, "\"aec_value\":%u,", s->status.aec_value); p+=sprintf(p, "\"agc\":%u,", s->status.agc); p+=sprintf(p, "\"agc_gain\":%u,", s->status.agc_gain); p+=sprintf(p, "\"gainceiling\":%u,", s->status.gainceiling); p+=sprintf(p, "\"bpc\":%u,", s->status.bpc); p+=sprintf(p, "\"wpc\":%u,", s->status.wpc); p+=sprintf(p, "\"raw_gma\":%u,", s->status.raw_gma); p+=sprintf(p, "\"lenc\":%u,", s->status.lenc); p+=sprintf(p, "\"vflip\":%u,", s->status.vflip); p+=sprintf(p, "\"hmirror\":%u,", s->status.hmirror); p+=sprintf(p, "\"dcw\":%u,", s->status.dcw); p+=sprintf(p, "\"colorbar\":%u,", s->status.colorbar); p+=sprintf(p, "\"face_detect\":%u,", detection_enabled); p+=sprintf(p, "\"face_enroll\":%u,", is_enrolling); p+=sprintf(p, "\"face_recognize\":%u", recognition_enabled); *p++ = '}'; *p++ = 0; httpd_resp_set_type(req, "application/json"); httpd_resp_set_hdr(req, "Access-Control-Allow-Origin", "*"); return httpd_resp_send(req, json_response, strlen(json_response)); } static esp_err_t index_handler(httpd_req_t *req){ httpd_resp_set_type(req, "text/html"); httpd_resp_set_hdr(req, "Content-Encoding", "gzip"); sensor_t * s = esp_camera_sensor_get(); if (s->id.PID == OV3660_PID) { return httpd_resp_send(req, (const char *)index_ov3660_html_gz, index_ov3660_html_gz_len); } return httpd_resp_send(req, (const char *)index_ov2640_html_gz, index_ov2640_html_gz_len); } void startCameraServer(){ httpd_config_t config = HTTPD_DEFAULT_CONFIG(); httpd_uri_t index_uri = { .uri = "/", .method = HTTP_GET, .handler = index_handler, .user_ctx = NULL }; httpd_uri_t status_uri = { .uri = "/status", .method = HTTP_GET, .handler = status_handler, .user_ctx = NULL }; httpd_uri_t cmd_uri = { .uri = "/control", .method = HTTP_GET, .handler = cmd_handler, .user_ctx = NULL }; httpd_uri_t capture_uri = { .uri = "/capture", .method = HTTP_GET, .handler = capture_handler, .user_ctx = NULL }; httpd_uri_t stream_uri = { .uri = "/stream", .method = HTTP_GET, .handler = stream_handler, .user_ctx = NULL }; ra_filter_init(&ra_filter, 20); mtmn_config.type = FAST; mtmn_config.min_face = 80; mtmn_config.pyramid = 0.707; mtmn_config.pyramid_times = 4; mtmn_config.p_threshold.score = 0.6; mtmn_config.p_threshold.nms = 0.7; mtmn_config.p_threshold.candidate_number = 20; mtmn_config.r_threshold.score = 0.7; mtmn_config.r_threshold.nms = 0.7; mtmn_config.r_threshold.candidate_number = 10; mtmn_config.o_threshold.score = 0.7; mtmn_config.o_threshold.nms = 0.7; mtmn_config.o_threshold.candidate_number = 1; face_id_init(&id_list, FACE_ID_SAVE_NUMBER, ENROLL_CONFIRM_TIMES); Serial.printf("Starting web server on port: '%d'\n", config.server_port); if (httpd_start(&camera_httpd, &config) == ESP_OK) { httpd_register_uri_handler(camera_httpd, &index_uri); httpd_register_uri_handler(camera_httpd, &cmd_uri); httpd_register_uri_handler(camera_httpd, &status_uri); httpd_register_uri_handler(camera_httpd, &capture_uri); } config.server_port += 1; config.ctrl_port += 1; Serial.printf("Starting stream server on port: '%d'\n", config.server_port); if (httpd_start(&stream_httpd, &config) == ESP_OK) { httpd_register_uri_handler(stream_httpd, &stream_uri); } }
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#if defined(CAMERA_MODEL_WROVER_KIT) #define PWDN_GPIO_NUM -1 #define RESET_GPIO_NUM -1 #define XCLK_GPIO_NUM 21 #define SIOD_GPIO_NUM 26 #define SIOC_GPIO_NUM 27 #define Y9_GPIO_NUM 35 #define Y8_GPIO_NUM 34 #define Y7_GPIO_NUM 39 #define Y6_GPIO_NUM 36 #define Y5_GPIO_NUM 19 #define Y4_GPIO_NUM 18 #define Y3_GPIO_NUM 5 #define Y2_GPIO_NUM 4 #define VSYNC_GPIO_NUM 25 #define HREF_GPIO_NUM 23 #define PCLK_GPIO_NUM 22 #elif defined(CAMERA_MODEL_ESP_EYE) #define PWDN_GPIO_NUM -1 #define RESET_GPIO_NUM -1 #define XCLK_GPIO_NUM 4 #define SIOD_GPIO_NUM 18 #define SIOC_GPIO_NUM 23 #define Y9_GPIO_NUM 36 #define Y8_GPIO_NUM 37 #define Y7_GPIO_NUM 38 #define Y6_GPIO_NUM 39 #define Y5_GPIO_NUM 35 #define Y4_GPIO_NUM 14 #define Y3_GPIO_NUM 13 #define Y2_GPIO_NUM 34 #define VSYNC_GPIO_NUM 5 #define HREF_GPIO_NUM 27 #define PCLK_GPIO_NUM 25 #elif defined(CAMERA_MODEL_M5STACK_PSRAM) #define PWDN_GPIO_NUM -1 #define RESET_GPIO_NUM 15 #define XCLK_GPIO_NUM 27 #define SIOD_GPIO_NUM 25 #define SIOC_GPIO_NUM 23 #define Y9_GPIO_NUM 19 #define Y8_GPIO_NUM 36 #define Y7_GPIO_NUM 18 #define Y6_GPIO_NUM 39 #define Y5_GPIO_NUM 5 #define Y4_GPIO_NUM 34 #define Y3_GPIO_NUM 35 #define Y2_GPIO_NUM 32 #define VSYNC_GPIO_NUM 22 #define HREF_GPIO_NUM 26 #define PCLK_GPIO_NUM 21 #elif defined(CAMERA_MODEL_M5STACK_WIDE) #define PWDN_GPIO_NUM -1 #define RESET_GPIO_NUM 15 #define XCLK_GPIO_NUM 27 #define SIOD_GPIO_NUM 22 #define SIOC_GPIO_NUM 23 #define Y9_GPIO_NUM 19 #define Y8_GPIO_NUM 36 #define Y7_GPIO_NUM 18 #define Y6_GPIO_NUM 39 #define Y5_GPIO_NUM 5 #define Y4_GPIO_NUM 34 #define Y3_GPIO_NUM 35 #define Y2_GPIO_NUM 32 #define VSYNC_GPIO_NUM 25 #define HREF_GPIO_NUM 26 #define PCLK_GPIO_NUM 21 #elif defined(CAMERA_MODEL_AI_THINKER) #define PWDN_GPIO_NUM 32 #define RESET_GPIO_NUM -1 #define XCLK_GPIO_NUM 0 #define SIOD_GPIO_NUM 26 #define SIOC_GPIO_NUM 27 #define Y9_GPIO_NUM 35 #define Y8_GPIO_NUM 34 #define Y7_GPIO_NUM 39 #define Y6_GPIO_NUM 36 #define Y5_GPIO_NUM 21 #define Y4_GPIO_NUM 19 #define Y3_GPIO_NUM 18 #define Y2_GPIO_NUM 5 #define VSYNC_GPIO_NUM 25 #define HREF_GPIO_NUM 23 #define PCLK_GPIO_NUM 22 #else #error "Camera model not selected" #endif
External Services
0