Face Detection

CompletedCode.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()

ProgrammingForFaceDetection.py

@@ -1,26 +1,44 @@
 # Programmin for Face detection
 # Haar Cascade
 import cv2
+import numpy as np
 
 # Charger le classificateur Haar Cascade
 face_cascade = cv2.CascadeClassifier("Haar_Cascade.xml")
 
+average_rgb = []
+
 # Charger l'image dans OpenCV
 # Convertir l'image en niveaux de gris
-img = cv2.imread("Image.jpg")
+img = cv2.imread("PhotoTest.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)
-
+face = face_cascade.detectMultiScale(gray_img, scaleFactor=1.1, minNeighbors=5)
+print(face)
 # 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 face:
+    img = cv2.rectangle(img, (int(x*1.2), int(y*1.15)),
+                        (x + int(w*0.7), y + int(h*0.2)), (0, 255, 0), 3)
 
 # Afficher l'image
 cv2.imshow("Faces", img)
 cv2.waitKey(0)
+
+b, g, r = cv2.split(img)
+cv2.imshow("b", b)
+cv2.imshow("g", g)
+cv2.imshow("r", r)
+cv2.waitKey(0)
+# 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)

RGB_Chanels.py

@@ -0,0 +1,19 @@
+import cv2
+import numpy as np
+
+# Load the image
+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.

dataRGB

@@ -0,0 +1,635 @@
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