def preprocess_image(src):
face_rects = FaceDetector.detect_faces(src)
# The original code expected only single face.
# If face was found then we crop only the first region and use it below
# If there is no face then try to process image raw camera image
if face_rects:
(x1, y1, x2, y2) = face_rects[0]
src = src[y1:y2, x1:x2]
# scale the image
preprocessed_image = cv2.resize(
src,
(VideoFaceMatcher.NETWORK_WIDTH, VideoFaceMatcher.NETWORK_HEIGHT))
# convert to RGB
preprocessed_image = cv2.cvtColor(preprocessed_image,
cv2.COLOR_BGR2RGB)
# whiten
preprocessed_image = VideoFaceMatcher.whiten_image(preprocessed_image)
# return the preprocessed image
return preprocessed_image, face_rects
def face_detection_image():
try:
# Initializes the FaceDetector with its default values
detector = FaceDetector()
#Run the face detection
detection_result = detector.detect_faces(request.data)
#Reads the numpy array to an image and corrects the color scheme
img = Image.fromarray(detection_result.astype('uint8'))
b, g, r = img.split()
img = Image.merge("RGB", (r, g, b))
#Prepare the image to send back to the client
file_object = io.BytesIO()
img.save(file_object, 'PNG')
file_object.seek(0)
return send_file(file_object,
attachment_filename='face_detection.png',
mimetype='image/jpg')
except Exception as error:
raise error
import cv2
#import tensorflow as tf
from FaceDetector import FaceDetector
import matplotlib.pyplot as plt
cap = cv2.VideoCapture('happiness.mov')
detector = FaceDetector('haarcascade_frontalface.xml')
while True:
ret, frame = cap.read()
if frame is None:
print('None')
else:
faces = detector.detect_faces(frame, show_image=True)
print(faces[:][:])
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = frame[y:y + h, x:x + w]
cv2.imshow('frame', frame)
cv2.waitKey(41)
