webui: Cleaning up dump+ Livestream IMPLEMENTATION

1 files changed, 48 insertions, 40 deletions

diff --git a/face-detection/03_face_recogition.py b/face-detection/03_face_recogition.py
index 185d3c2..51741a5 100644
--- a/face-detection/03_face_recogition.py
+++ b/face-detection/03_face_recogition.py
@@ -2,6 +2,9 @@ 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
@@ -28,53 +31,58 @@ cam.preview_configuration.align()
 cam.configure("preview")
 cam.start()
 
-while True:
-    # Capture a frame from the camera
-    frame=cam.capture_array()
+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
+        #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])
+            #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}%"
+            # 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 realtime capture output to the user
-    cv2.imshow('Raspi Face Recognizer',frame)
+    # 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')
 
-    # Wait for 30 milliseconds for a key event (extract sigfigs) and exit if 'ESC' or 'q' is pressed
-    key = cv2.waitKey(100) & 0xff
-    #Checking keycode
-    if key == 27:  # ESCAPE key
-        break
-    elif key == 113:  # q key
-        break
+@app.route('/video_feed')
+def video_feed():
+    return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
 
-# Release the camera and close all windows
-print("\n [INFO] Exiting Program and cleaning up stuff")
-cam.stop()
-cv2.destroyAllWindows()
+if __name__ == '__main__':
+    app.run(debug=True)