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-rw-r--r--face-detection/01_face_capture_dataset.py14
1 files changed, 8 insertions, 6 deletions
diff --git a/face-detection/01_face_capture_dataset.py b/face-detection/01_face_capture_dataset.py
index a56a274..c72a14a 100644
--- a/face-detection/01_face_capture_dataset.py
+++ b/face-detection/01_face_capture_dataset.py
@@ -6,10 +6,11 @@ from picamera2 import Picamera2
count = 0
pos=(30,60) #top-left
font=cv2.FONT_HERSHEY_COMPLEX
-height=1.5
-color=(0,0,255) #BGR- RED
-weight=3
-face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+height=1.5 #font_scale
+textcolor=(0,0,255) #BGR- RED
+boxcolor=(255,0,255) #BGR- BLUE
+weight=3 #font-thickness
+face_detector=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# For each person, enter one numeric face id
face_id = input('\n----Enter User-id and press <return>----')
@@ -29,7 +30,7 @@ while True:
# Capture a frame from the camera
frame=cam.capture_array()
#Display count of images taken
- cv2.putText(frame,'Count:'+str(int(count)),pos,font,height,color,weight)
+ cv2.putText(frame,'Count:'+str(int(count)),pos,font,height,textcolor,weight)
#Convert fram from BGR to grayscale
frameGray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
@@ -42,13 +43,14 @@ while True:
)
for (x,y,w,h) in faces:
#create a bounding box across the detected face
- cv2.rectangle(frame, (x,y), (x+w,y+h), (255,0,0), 3) #tuple
+ cv2.rectangle(frame, (x,y), (x+w,y+h), boxcolor, 3) #5 parameters - frame, topleftcoords,bottomrightcooords,boxcolor,thickness
count += 1 # increment count
# if dataset folder doesnt exist create:
if not os.path.exists("dataset"):
os.makedirs("dataset")
# Save the captured bounded-grayscaleimage into the datasets folder
cv2.imwrite("dataset/User." + str(face_id) + '.' + str(count) + ".jpg", frameGray[y:y+h,x:x+w]) #req os
+
# Display the original frame to the user
cv2.imshow('FaceCapture', frame)
# Wait for 30 milliseconds for a key event (extract sigfigs) and exit if 'ESC' or 'q' is pressed