Total Program

Calculation.m

@@ -0,0 +1,10 @@
+img = imread('Image.jpg');
+pixels = double(img);
+raw_data = reshape(pixels, [], 1);
+% We have to add an iteration for the RGB chanels
+% To calculate the mean value 
+mean_value = mean(A);
+% To calculate the standard deviation
+standard_deviation = std(A);
+% x'(t)=(x(t)-mean)/standard deviation
+norm_data = (raw_data - mean_value)/standard_deviation;

CalculationTest.m

@@ -0,0 +1,8 @@
+raw_data = [];
+% We have to add an iteration for the RGB chanels
+% To calculate the mean value 
+mean_value = mean(raw_data)
+% To calculate the standard deviation
+standard_deviation = std(raw_data)
+% x'(t)=(x(t)-mean)/standard deviation
+norm_data = (raw_data - mean_value)/standard_deviation

Calculation_2.m

@@ -0,0 +1,8 @@
+raw_data = [1,2,3,4,5];
+% We have to add an iteration for the RGB chanels
+% To calculate the mean value 
+mean_value = mean(raw_data)
+% To calculate the standard deviation
+standard_deviation = std(raw_data)
+% x'(t)=(x(t)-mean)/standard deviation
+norm_data = (raw_data - mean_value)/standard_deviation

CompletedCode.py

@@ -0,0 +1,74 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load video using OpenCV
+video = cv2.VideoCapture("PPG_Programming.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
+    faces = face_detector.detectMultiScale(gray_frame, 1.3, 5)
+
+    # If face is detected, store information
+    if len(faces) > 0:
+        x, y, w, h = faces[0]
+        face = frame[y:y+h, x:x+w]
+
+        # 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)
+
+# Convert to numpy array
+average_rgb = np.array(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()

ProgrammingForFaceDetection.py

@@ -4,7 +4,7 @@ import cv2
 import numpy as np
 
 # Charger le classificateur Haar Cascade
-face_cascade = cv2.CascadeClassifier("Haar_Cascade.xml")
+face_cascade = cv2.CascadeClassifier("Haar_Cascade_Eye")
 
 # Charger l'image dans OpenCV
 # Convertir l'image en niveaux de gris
@@ -15,23 +15,14 @@ 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(
+Eye = 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)
+for x, y, w, h in Eye:
+    img = cv2.rectangle(img, (x, y),
+                        (x + w, y + h), (0, 255, 0), 3)
 
 # Afficher l'image
 cv2.imshow("Faces", img)
 cv2.waitKey(0)
-
-# Split the image into its RGB channels
-b, g, r = cv2.split(img)
-
-# Display the individual channels
-cv2.imshow('Red channel', r)
-cv2.imshow('Green channel', g)
-cv2.imshow('Blue channel', b)
-cv2.waitKey(0)
-cv2.destroyAllWindows()

RGB_Chanels.py

@@ -6,10 +6,14 @@ img = cv2.imread('Image.jpg')
 
 # Split the image into its RGB channels
 b, g, r = cv2.split(img)
-
+# e = np.zeros((100, 100), dtype=np.uint8)
+# red_merged = cv2.merge((e, e, r))
 # Display the individual channels
 cv2.imshow('Red channel', r)
+# cv2.imshow('Red channel 2', red_merged)
 cv2.imshow('Green channel', g)
 cv2.imshow('Blue channel', b)
 cv2.waitKey(0)
 cv2.destroyAllWindows()
+
+# Note that the individual channels are displayed in grayscale because they represent the intensity values of a single color (red, green, or blue) in the image.