Commit

CompletedCode.py

@@ -40,8 +40,8 @@ for i in range(num_frames):
     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), y),
-                              (x + int(w*0.7), y + h), (0, 255, 0), 3)
+        frame = cv2.rectangle(frame, (int(x*1.15), int(y*1.15)),
+                              (x + int(w*0.7), y + int(h*0.2)), (0, 255, 0), 3)
         # Afficher l'image
         # cv2.imshow("Faces", frame)
         # cv2.waitKey(0)

ProgrammingForFaceDetection.py

@@ -6,6 +6,8 @@ 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("PhotoTest.jpg")
@@ -25,3 +27,18 @@ for x, y, w, h in face:
 # 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)