diff options
| -rw-r--r-- | face-detection/01_face_capture_dataset.py | 44 |
1 files changed, 24 insertions, 20 deletions
diff --git a/face-detection/01_face_capture_dataset.py b/face-detection/01_face_capture_dataset.py index c72a14a..edf271e 100644 --- a/face-detection/01_face_capture_dataset.py +++ b/face-detection/01_face_capture_dataset.py @@ -2,19 +2,19 @@ import cv2 import os from picamera2 import Picamera2 -#Parameters -count = 0 -pos=(30,60) #top-left -font=cv2.FONT_HERSHEY_COMPLEX -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') +# Constants +COUNT_LIMIT = 30 +POS=(30,60) #top-left +FONT=cv2.FONT_HERSHEY_COMPLEX #font type for text overlay +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>----') -print("\n [INFO] Initializing face capture. Look the camera and wait!") +print("\n [INFO] Initializing face capture. Look at the camera and wait!") # Create an instance of the PiCamera2 object cam = Picamera2() @@ -26,42 +26,46 @@ cam.preview_configuration.align() cam.configure("preview") cam.start() +count=0 + 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,textcolor,weight) + # Display count of images taken + cv2.putText(frame,'Count:'+str(int(count)),POS,FONT,HEIGHT,TEXTCOLOR,WEIGHT) - #Convert fram from BGR to grayscale + # Convert frame from BGR to grayscale frameGray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) - #Create a DS faces- array with 4 elements- x,y coordinates top-left corner), width and height - faces = face_detector.detectMultiScale( + # Create a DS faces- array with 4 elements- x,y coordinates (top-left corner), width and height + faces = FACE_DETECTOR.detectMultiScale( # detectMultiScale has 4 parameters frameGray, # The grayscale frame to detect scaleFactor=1.1,# how much the image size is reduced at each image scale-10% reduction minNeighbors=5, # how many neighbors each candidate rectangle should have to retain it minSize=(30, 30)# Minimum possible object size. Objects smaller than this size are ignored. ) for (x,y,w,h) in faces: - #create a bounding box across the detected face - cv2.rectangle(frame, (x,y), (x+w,y+h), boxcolor, 3) #5 parameters - frame, topleftcoords,bottomrightcooords,boxcolor,thickness + # Create a bounding box across the detected face + 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 + cv2.imwrite("dataset/User." + str(face_id) + '.' + str(count) + ".jpg", frameGray[y:y+h,x:x+w]) # 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 key = cv2.waitKey(100) & 0xff - #Checking keycode + # Checking keycode if key == 27: # ESCAPE key break elif key == 113: # q key break - elif count >= 30: # Take 30 face sample and stop video capture + elif count >= COUNT_LIMIT: # Take COUNT_LIMIT face sample and stop video capture break + # Release the camera and close all windows print("\n [INFO] Exiting Program and cleaning up stuff") cam.stop() |