From 741b3138f7d398301090f18275e7c0c01c3d9303 Mon Sep 17 00:00:00 2001
From: Alexandre <alexandre.verot@ecam.fr>
Date: Wed, 8 Feb 2023 22:21:04 +0100
Subject: [PATCH] Modification of the ROI (rectangle)

---
 CompletedCode.py               | 19 ++++++++++++++-----
 ProgrammingForFaceDetection.py | 13 ++++++-------
 2 files changed, 20 insertions(+), 12 deletions(-)

diff --git a/CompletedCode.py b/CompletedCode.py
index 6231fad..2f92c1c 100644
--- a/CompletedCode.py
+++ b/CompletedCode.py
@@ -32,12 +32,19 @@ for i in range(num_frames):
     gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
     # Detect faces
-    faces = face_detector.detectMultiScale(gray_frame, 1.3, 5)
+    face = face_detector.detectMultiScale(
+        gray_frame, scaleFactor=1.1, minNeighbors=5)
+    # print(face)
 
-    # If face is detected, store information
-    if len(faces) > 0:
-        x, y, w, h = faces[0]
+    # Dessiner un rectangle autour de chaque visage détecté and if fac 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), y),
+                              (x + int(w*0.7), y + h), (0, 255, 0), 3)
+        # Afficher l'image
+        # cv2.imshow("Faces", frame)
+        # cv2.waitKey(0)
 
         # Split into RGB channels
         b, g, r = cv2.split(face)
@@ -49,7 +56,9 @@ for i in range(num_frames):
 
         # Add to list
         average_rgb.append([avg_b, avg_g, avg_r])
-        frame_matrices.append(face)
+        # frame_matrices.append(face)
+        print(average_rgb)
+        # print(frame_matrices)
 
 # Convert to numpy array
 average_rgb = np.array(average_rgb)
diff --git a/ProgrammingForFaceDetection.py b/ProgrammingForFaceDetection.py
index f364f67..4ab9883 100644
--- a/ProgrammingForFaceDetection.py
+++ b/ProgrammingForFaceDetection.py
@@ -4,7 +4,7 @@ import cv2
 import numpy as np
 
 # Charger le classificateur Haar Cascade
-face_cascade = cv2.CascadeClassifier("Haar_Cascade_Eye")
+face_cascade = cv2.CascadeClassifier("Haar_Cascade.xml")
 
 # Charger l'image dans OpenCV
 # Convertir l'image en niveaux de gris
@@ -15,13 +15,12 @@ if gray_img.shape[0] == 0 or gray_img.shape[1] == 0:
     print("Error: input image is empty")
 
 # Détection des visages dans l'image
-Eye = 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 Eye:
-    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*1.15), int(y*1.1)),
+                        (x + int(w*0.8), y + int(h*0.5)), (0, 255, 0), 3)
 
 # Afficher l'image
 cv2.imshow("Faces", img)