alexandre.verot
/
SignalProjectFaceDetection
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Modification of the ROI (rectangle)

This commit is contained in:
Alexandre VEROT 2023-02-08 22:21:04 +01:00
parent 36b01581a6
commit 741b3138f7
2 changed files with 20 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
CompletedCode.py
View File

@ -32,12 +32,19 @@ for i in range(num_frames):
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Detect faces # 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 # Dessiner un rectangle autour de chaque visage détecté and if fac is detected store information
if len(faces) > 0: if len(face) > 0:
x, y, w, h = faces[0] x, y, w, h = face[0]
face = frame[y:y+h, x:x+w] 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 # Split into RGB channels
b, g, r = cv2.split(face) b, g, r = cv2.split(face)
@ -49,7 +56,9 @@ for i in range(num_frames):
# Add to list # Add to list
average_rgb.append([avg_b, avg_g, avg_r]) 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 # Convert to numpy array
average_rgb = np.array(average_rgb) average_rgb = np.array(average_rgb)

13
ProgrammingForFaceDetection.py
View File

@ -4,7 +4,7 @@ import cv2
import numpy as np import numpy as np
# Charger le classificateur Haar Cascade # Charger le classificateur Haar Cascade
face_cascade = cv2.CascadeClassifier("Haar_Cascade_Eye") face_cascade = cv2.CascadeClassifier("Haar_Cascade.xml")
# Charger l'image dans OpenCV # Charger l'image dans OpenCV
# Convertir l'image en niveaux de gris # 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") print("Error: input image is empty")
# Détection des visages dans l'image # Détection des visages dans l'image
Eye = face_cascade.detectMultiScale( face = face_cascade.detectMultiScale(gray_img, scaleFactor=1.1, minNeighbors=5)
gray_img, scaleFactor=1.1, minNeighbors=5) print(face)
# Dessiner un rectangle autour de chaque visage détecté # Dessiner un rectangle autour de chaque visage détecté
for x, y, w, h in Eye: for x, y, w, h in face:
img = cv2.rectangle(img, (x, y), img = cv2.rectangle(img, (int(x*1.15), int(y*1.1)),
(x + w, y + h), (0, 255, 0), 3) (x + int(w*0.8), y + int(h*0.5)), (0, 255, 0), 3)
# Afficher l'image # Afficher l'image
cv2.imshow("Faces", img) cv2.imshow("Faces", img)