facedetection: reversing changes

1 files changed, 40 insertions, 48 deletions

diff --git a/face-detection/03_face_recogition.py b/face-detection/03_face_recogition.py
index 51741a5..185d3c2 100644
--- a/face-detection/03_face_recogition.py
+++ b/face-detection/03_face_recogition.py
@@ -2,9 +2,6 @@ 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
@@ -31,58 +28,53 @@ cam.preview_configuration.align()
 cam.configure("preview")
 cam.start()
 
-def generate_frames():
-    while True:
-        # Capture a frame from the camera
-        frame=cam.capture_array()
+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 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')
+    # Display realtime capture output to the user
+    cv2.imshow('Raspi Face Recognizer',frame)
 
-@app.route('/video_feed')
-def video_feed():
-    return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
+    # 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
 
-if __name__ == '__main__':
-    app.run(debug=True)
+# Release the camera and close all windows
+print("\n [INFO] Exiting Program and cleaning up stuff")
+cam.stop()
+cv2.destroyAllWindows()