Change in the Program

Numerization2.m

@@ -0,0 +1,34 @@
+clear;
+close all;
+clc;
+pkg load io;
+
+% sample frequency (Hz)
+fps = 30;  
+                              
+%Load the data inside Octave from our first code                     
+data = load("dataRGB");
+greenchannel = data(:,2);
+n = data(:,2);
+
+%Numerization of the values
+greenchannel_avg = mean(greenchannel);
+greenchannel_std = std(greenchannel);
+greenchannel_normalized = (greenchannel - greenchannel_avg)/greenchannel_std;
+
+%Fast Fourier Transform
+y = fft(greenchannel_normalized);
+
+%number of samples
+n = length(greenchannel_normalized); 
+        
+% frequency range
+
+fr = (0:n-1)*(fps/n);     
+power = abs(y).^2/n;
+
+figure(2)
+plot(fr, power,'linewidth',3);    
+xlabel('Frequency')
+ylabel('Power')
+xlim([0.75 4])

ProgrammingForFaceDetection.py

@@ -1,26 +0,0 @@
-# Programmin for Face detection
-# Haar Cascade
-import cv2
-
-# Charger le classificateur Haar Cascade
-face_cascade = cv2.CascadeClassifier("Haar_Cascade.xml")
-
-# Charger l'image dans OpenCV
-# Convertir l'image en niveaux de gris
-img = cv2.imread("Image.jpg")
-gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-# Just to check if there is a photo
-if gray_img.shape[0] == 0 or gray_img.shape[1] == 0:
-    print("Error: input image is empty")
-
-# Détection des visages dans l'image
-faces = face_cascade.detectMultiScale(
-    gray_img, scaleFactor=1.1, minNeighbors=5)
-
-# Dessiner un rectangle autour de chaque visage détecté
-for x, y, w, h in faces:
-    img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 3)
-
-# Afficher l'image
-cv2.imshow("Faces", img)
-cv2.waitKey(0)

TotalCode.py

@@ -0,0 +1,84 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load video using OpenCV
+video = cv2.VideoCapture("Sophia.mp4")
+
+# Get video information
+fps = video.get(cv2.CAP_PROP_FPS)
+num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+
+# Load face detection model
+face_detector = cv2.CascadeClassifier("Haar_Cascade.xml")
+
+# Lists to store information for each frame
+frame_matrices = []
+average_rgb = []
+
+# Loop through frames
+for i in range(num_frames):
+    ret, frame = video.read()
+
+    # Check if frame was successfully retrieved
+    if not ret:
+        break
+
+    # Skip frames to get 15 frames per second
+    if i % int(fps/15) != 0:
+        continue
+
+    # Convert to grayscale
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces
+    face = face_detector.detectMultiScale(
+        gray_frame, scaleFactor=1.1, minNeighbors=5)
+    # print(face)
+
+    # Draw a rectangle on each detected face and if face is detected store information
+    if len(face) > 0:
+        x, y, w, h = face[0]
+        face = frame[y:y+h, x:x+w]
+        frame = cv2.rectangle(frame, (int(x*1.15), int(y*1)),
+                              (x + int(w*0.7), y + int(h*0.2)), (0, 255, 0), 3)
+        # Show image to check if the rectangle is well positioned on the forehead
+        # cv2.imshow("Faces", frame)
+        # cv2.waitKey(0)
+
+        # Split into RGB channels
+        b, g, r = cv2.split(face)
+
+        # Calculate average on each channel
+        avg_b = np.mean(b) / 255
+        avg_g = np.mean(g) / 255
+        avg_r = np.mean(r) / 255
+
+        # Add to list
+        average_rgb.append([avg_b, avg_g, avg_r])
+        # frame_matrices.append(face)
+        print(average_rgb)
+        # print(frame_matrices)
+
+# Convert to numpy array
+average_rgb = np.array(average_rgb)
+print(average_rgb)
+np.savetxt("dataRGB", average_rgb)
+# Get number of frames
+num_frames = average_rgb.shape[0]
+
+# Create an array of frame numbers
+frame_numbers = np.arange(num_frames)
+
+# Plot the line graph
+plt.plot(frame_numbers, average_rgb[:, 0], 'b', label='Blue')
+plt.plot(frame_numbers, average_rgb[:, 1], 'g', label='Green')
+plt.plot(frame_numbers, average_rgb[:, 2], 'r', label='Red')
+
+# Add labels and legend
+plt.xlabel('Frame Number')
+plt.ylabel('Normalized Average RGB Value')
+plt.legend(loc='upper right')
+
+# Show the plot
+plt.show()