import cv2
import os 
import numpy as np
from picamera2 import Picamera2
from flask import Flask, render_template, Response

app = Flask(__name__)

#Parameters
id = 0
font = cv2.FONT_HERSHEY_COMPLEX
height=1
boxColor=(0,0,255)      #BGR- GREEN
nameColor=(255,255,255) #BGR- WHITE
confColor=(255,255,0)   #BGR- TEAL

face_detector=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
# names related to id
names = ['None', 'Saumit', 'Bikram', 'Junaid']

# Create an instance of the PiCamera2 object
cam = Picamera2()
## Initialize and start realtime video capture
# Set the resolution of the camera preview
cam.preview_configuration.main.size = (640, 360)
cam.preview_configuration.main.format = "RGB888"
cam.preview_configuration.controls.FrameRate=30
cam.preview_configuration.align()
cam.configure("preview")
cam.start()

def generate_frames():
    while True:
        # Capture a frame from the camera
        frame=cam.capture_array()

        #Convert fram from BGR to grayscale
        frameGray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        #Create a DS faces- array with 4 elements- x,y coordinates top-left corner), width and height
        faces = face_detector.detectMultiScale(
                frameGray,      # The grayscale frame to detect
                scaleFactor=1.1,# how much the image size is reduced at each image scale-10% reduction
                minNeighbors=5, # how many neighbors each candidate rectangle should have to retain it
                minSize=(150, 150)# Minimum possible object size. Objects smaller than this size are ignored.
                )
        for(x,y,w,h) in faces:
            namepos=(x+5,y-5) #shift right and up/outside the bounding box from top
            confpos=(x+5,y+h-5) #shift right and up/intside the bounding box from bottom
            #create a bounding box across the detected face
            cv2.rectangle(frame, (x,y), (x+w,y+h), boxColor, 3) #5 parameters - frame, topleftcoords,bottomrightcooords,boxcolor,thickness

            #recognizer.predict() method takes the ROI as input and
            #returns the predicted label (id) and confidence score for the given face region.
            id, confidence = recognizer.predict(frameGray[y:y+h,x:x+w])
        
            # If confidence is less than 100, it is considered a perfect match
            if confidence < 100:
                id = names[id]
                confidence = f"{100 - confidence:.0f}%"
            else:
                id = "unknown"
                confidence = f"{100 - confidence:.0f}%"

        #Display name and confidence of person who's face is recognized
        cv2.putText(frame, str(id), namepos, font, height, nameColor, 2)
        cv2.putText(frame, str(confidence), confpos, font, height, confColor, 1)

    # Display output Flask web application:
    # Convert the frame to JPEG format
    ret, buffer = cv2.imencode('.jpg', frame)
    frame = buffer.tobytes()

    # Yield the frame in the HTTP response
    yield (b'--frame\r\n'
           b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/video_feed')
def video_feed():
    return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')

if __name__ == '__main__':
    app.run(debug=True)