def get_face_alignment(image_bytes):
im = transform_image(image_bytes=image_bytes)
# Detect Landmark
points = fbc.getLandmarks(faceDetector, landmarkDetector, im)
points = np.array(points)
print('Landmarks detected')
# Convert image to floating point in the range 0 to 1
im = np.float32(im) / 255.0
# Specify the size of aligned face image. Compute the normalized image by using the similarity transform
# Dimension of output Image
h = im.shape[0] # 600
w = im.shape[1] # 600
# Normalize the image to output coordinates
imNorm, points = fbc.normalizeImagesAndLandmarks((h, w), im, points)
imNorm = np.uint8(imNorm * 255)
print("Aligned image is made.....")
# This is aligned image
return imNorm
def perform_face_alignment(image_bytes):
h=w=600
points = fbc.getLandmarks(faceDetector, landmarkDetector, image_bytes)
points = np.array(points)
image_bytes = np.float32(image_bytes)/255.0
imNorm, points = fbc.normalizeImagesAndLandmarks((h,w), image_bytes, points)
imNorm = np.uint8(imNorm * 255)
return imNorm#[:,:,::-1]
def align_face(bytes_im):
im = transform_image(image_bytes=bytes_im)
points = fbc.getLandmarks(faceDetector, landmarkDetector5, im)
points = np.array(points)
im = np.float32(im) / 255.0
# Dimensions of the output image
h = im.shape[0]
w = im.shape[1]
imNorm, points = fbc.normalizeImagesAndLandmarks((h, w), im, points)
imNorm = np.uint8(imNorm * 255)
return imNorm
def get_aligned_image(im):
try:
face_detector = dlib.get_frontal_face_detector()
points = fbc.getLandmarks(face_detector, landmark_detector, im)
points = np.array(points)
im = np.float32(im) / 255.0
print("Inside the get_aligned_face function.")
h = 600
w = 600
imNorm, points = fbc.normalizeImagesAndLandmarks((h, w), im, points)
imNorm = np.uint8(imNorm * 255)
return imNorm
return
except Exception as e:
print(repr(e))
raise (e)
def classify_image (event, context):
try:
content_type_header = event['headers']['content-type']
body = base64.b64decode(event["body"])
picture = decoder.MultipartDecoder(body, content_type_header).parts[0]
im_arr = np.frombuffer(picture.content, dtype=np.uint8)
im = cv2.imdecode(im_arr, flags=cv2.IMREAD_COLOR)
points = fbc.getLandmarks(faceDetector, landmarkDetector, im)
points = np.array(points)
im = np.float32(im)/255.0
h = 600
w = 600
imNorm, points = fbc.normalizeImagesAndLandmarks((h, w), im, points)
imNorm = np.uint8(imNorm*255)
filename = picture.headers[b'Content-Disposition'].decode().split(';')[1].split('=')[1]
if len(filename) < 4:
filename = picture.headers[b'Content-Disposition'].decode().split(';')[2].split('=')[1]
print ('all done')
return {
"statusCode": 200,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({'file': filename.replace('"', ''), 'aligned_image': str (base64.b64encode (cv2.imencode ('.jpg', imNorm)[1])) })
}
except Exception as e:
print(repr(e))
return {
"statusCode": 500,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({"error": repr(e)})
}
def get_aligned_image(image_bytes):
print('Alignment process started...')
(faceDetector, landmarkDetector) = face_landmark_detector()
im = transform_image(image_bytes)
# Detect landmarks
points = fbc.getLandmarks(faceDetector, landmarkDetector, im)
points = np.array(points)
landmarksDetected = len(points)
print(f'Landmarks detected: {landmarksDetected}')
if landmarksDetected == 0:
return None
im = np.float32(im)/255.0
h = im.shape[0] # 600
w = im.shape[1] # 600
imNorm, points = fbc.normalizeImagesAndLandmarks((h, w), im, points)
imNorm = np.uint8(imNorm * 255)
return imNorm # aligned image
# 归一化图片和特征点
imagesNorm = []
pointsNorm = []
# 平均值
pointsAvg = np.zeros((numLandmarks,2), dtype=np.float32)
# 将图片规整到输出坐标系中并求解平均值
for i, img in enumerate(images):
points = allPoints[i]
points = np.array(points)
# print(points)
img, points = fbc.normalizeImagesAndLandmarks((h,w),img,points)
cv2.imshow("{}".format(i), img)
pointsAvg = pointsAvg + (points/(1.0*numImages))
# 添加边界点
points = np.concatenate((points, boundaryPts), axis=0)
pointsNorm.append(points)
imagesNorm.append(img)
pointsAvg = np.concatenate((pointsAvg, boundaryPts), axis=0)
# 计算delaunay三角形
rect = (0, 0, w, h)
def align_face(event,context):
try:
print(event)
print(context)
content_type_header = event['headers']['content-type']
print(event['body'])
body = base64.b64decode(event["body"])
print(body)
print("BODY LOADED...")
content_type = event.get('headers', {"content-type" : ''}).get('content-type')
# body = bytes(body,'utf-8')
multipart_data = decoder.MultipartDecoder(body, content_type).parts[0]
img_io = io.BytesIO(multipart_data.content)
img = Image.open(img_io)
img = cv2.cvtColor(np.array(img),cv2.COLOR_BGR2RGB)
PREDICTOR_PATH = "shape_predictor_5_face_landmarks.dat"
faceDetector = dlib.get_frontal_face_detector()
landmarkDetector = dlib.shape_predictor(PREDICTOR_PATH)
points = fbc.getLandmarks(faceDetector, landmarkDetector, img)
if len(points) == 0:
return {
"statusCode": 202,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({'error': 'No Face detected in the image'})
}
points = np.array(points)
img = np.float32(img)/255.0
h = 600
w = 600
#Normalize image to output co-ord
imNorm, points = fbc.normalizeImagesAndLandmarks((h,w), img, points)
imNorm = np.uint8(imNorm*255)
#encoded_img = base64.b64encode(imNorm).decode("utf-8")
serialized_img = base64.b64encode(cv2.imencode('.jpg', imNorm)[1]).decode()
return {
"statusCode": 200,
"headers": {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin': '*',
"Access-Control-Allow-Credentials": True
},
"body": json.dumps({'aligned': serialized_img})
}
except Exception as e:
print(repr(e))
return {
"statuscode" : 500,
"headers" : {
'Content-Type': 'application/json',
'Access-Control-Allow-Origin' : '*',
'Access-Control-Allow-Credentials' : True
},
"body": json.dumps({"error": repr(e)})
}
img2 = cv2.imread("../data/images/girls/hexiaoping.jpg")
# 获取关键特征点
points1 = fbc.getLandmarks(faceDetector, landmarkDetector, img1)
points2 = fbc.getLandmarks(faceDetector, landmarkDetector, img2)
points1 = np.array(points1)
points2 = np.array(points2)
img1 = np.float32(img1) / 255.0
img2 = np.float32(img2) / 255.0
h = 480
w = 480
imgNorm1, points1 = fbc.normalizeImagesAndLandmarks((h, w), img1, points1)
imgNorm2, points2 = fbc.normalizeImagesAndLandmarks((h, w), img2, points2)
pointsAvg = (points1 + points2) / 2.0
# 边界点
boundaryPoints = fbc.getEightBoundaryPoints(h, w)
points1 = np.concatenate((points1, boundaryPoints), axis=0)
points2 = np.concatenate((points2, boundaryPoints), axis=0)
pointsAvg = np.concatenate((pointsAvg, boundaryPoints), axis=0)
# 就算细分三角形
rect = (0, 0, w, h)
dt = fbc.calculateDelaunayTriangles(rect, pointsAvg)
winName = "Face Morphing"