mirror of
https://github.com/serengil/deepface.git
synced 2025-06-07 20:15:21 +00:00
new backends for detectors
This commit is contained in:
Şefik Serangil
parent
adb8c85354
commit
b9abfb9c00
@ -22,8 +22,7 @@ from deepface.basemodels.DlibResNet import DlibResNet
|
||||
from deepface.extendedmodels import Age, Gender, Race, Emotion
|
||||
from deepface.commons import functions, realtime, distance as dst
|
||||
|
||||
def verify(img1_path, img2_path=''
|
||||
, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True):
|
||||
def verify(img1_path, img2_path = '', model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv'):
|
||||
|
||||
tic = time.time()
|
||||
|
||||
@ -105,8 +104,8 @@ def verify(img1_path, img2_path=''
|
||||
input_shape = input_shape[1:3]
|
||||
|
||||
|
||||
img1 = functions.preprocess_face(img1_path, input_shape, enforce_detection = enforce_detection)
|
||||
img2 = functions.preprocess_face(img2_path, input_shape, enforce_detection = enforce_detection)
|
||||
img1 = functions.preprocess_face(img = img1_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)
|
||||
img2 = functions.preprocess_face(img = img2_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)
|
||||
|
||||
img1_representation = custom_model.predict(img1)[0,:]
|
||||
img2_representation = custom_model.predict(img2)[0,:]
|
||||
@ -274,8 +273,8 @@ def verify(img1_path, img2_path=''
|
||||
#----------------------
|
||||
#crop and align faces
|
||||
|
||||
img1 = functions.preprocess_face(img1_path, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
|
||||
img2 = functions.preprocess_face(img2_path, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
|
||||
img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
|
||||
img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
|
||||
|
||||
#----------------------
|
||||
#find embeddings
|
||||
@ -348,7 +347,7 @@ def verify(img1_path, img2_path=''
|
||||
#return resp_objects
|
||||
|
||||
|
||||
def analyze(img_path, actions = [], models = {}, enforce_detection = True):
|
||||
def analyze(img_path, actions = [], models = {}, enforce_detection = True, detector_backend = 'opencv'):
|
||||
|
||||
if type(img_path) == list:
|
||||
img_paths = img_path.copy()
|
||||
@ -422,7 +421,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
|
||||
|
||||
if action == 'emotion':
|
||||
emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
|
||||
img = functions.preprocess_face(img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection)
|
||||
img = functions.preprocess_face(img = img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection, detector_backend = detector_backend)
|
||||
|
||||
emotion_predictions = emotion_model.predict(img)[0,:]
|
||||
|
||||
@ -454,7 +453,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
|
||||
|
||||
elif action == 'gender':
|
||||
if img_224 is None:
|
||||
img_224 = functions.preprocess_face(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
|
||||
img_224 = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend) #just emotion model expects grayscale images
|
||||
#print("gender prediction")
|
||||
|
||||
gender_prediction = gender_model.predict(img_224)[0,:]
|
||||
@ -468,7 +467,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
|
||||
|
||||
elif action == 'race':
|
||||
if img_224 is None:
|
||||
img_224 = functions.preprocess_face(img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection) #just emotion model expects grayscale images
|
||||
img_224 = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend) #just emotion model expects grayscale images
|
||||
race_predictions = race_model.predict(img_224)[0,:]
|
||||
race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
|
||||
|
||||
@ -515,12 +514,11 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True):
|
||||
#return resp_objects
|
||||
|
||||
|
||||
def detectFace(img_path):
|
||||
img = functions.preprocess_face(img_path)[0] #preprocess_face returns (1, 224, 224, 3)
|
||||
def detectFace(img_path, detector_backend = 'opencv'):
|
||||
img = functions.preprocess_face(img = img_path, detector_backend = detector_backend)[0] #preprocess_face returns (1, 224, 224, 3)
|
||||
return img[:, :, ::-1] #bgr to rgb
|
||||
|
||||
def find(img_path, db_path
|
||||
, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True):
|
||||
def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv'):
|
||||
|
||||
model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
|
||||
metric_names = ['cosine', 'euclidean', 'euclidean_l2']
|
||||
@ -660,7 +658,7 @@ def find(img_path, db_path
|
||||
|
||||
input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
|
||||
|
||||
img = functions.preprocess_face(employee, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
|
||||
img = functions.preprocess_face(img = employee, target_size = (input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
|
||||
representation = model.predict(img)[0,:]
|
||||
|
||||
instance = []
|
||||
@ -686,7 +684,7 @@ def find(img_path, db_path
|
||||
|
||||
input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
|
||||
|
||||
img = functions.preprocess_face(employee, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
|
||||
img = functions.preprocess_face(img = employee, target_size = (input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
|
||||
representation = ensemble_model.predict(img)[0,:]
|
||||
instance.append(representation)
|
||||
|
||||
@ -731,7 +729,7 @@ def find(img_path, db_path
|
||||
else:
|
||||
input_shape = input_shape[1:3]
|
||||
|
||||
img = functions.preprocess_face(img_path, input_shape, enforce_detection = enforce_detection)
|
||||
img = functions.preprocess_face(img = img_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)
|
||||
target_representation = ensemble_model.predict(img)[0,:]
|
||||
|
||||
for k in metric_names:
|
||||
@ -823,7 +821,7 @@ def find(img_path, db_path
|
||||
|
||||
input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
|
||||
|
||||
img = functions.preprocess_face(img_path, (input_shape_y, input_shape_x), enforce_detection = enforce_detection)
|
||||
img = functions.preprocess_face(img = img_path, target_size = (input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
|
||||
target_representation = model.predict(img)[0,:]
|
||||
|
||||
distances = []
|
||||
@ -872,7 +870,8 @@ def allocateMemory():
|
||||
print("Analyzing your system...")
|
||||
functions.allocateMemory()
|
||||
|
||||
#---------------------------
|
||||
#main
|
||||
|
||||
functions.initializeFolder()
|
||||
|
||||
#---------------------------
|
||||
|
||||
|
||||
@ -14,7 +14,3 @@ def findEuclideanDistance(source_representation, test_representation):
|
||||
|
||||
def l2_normalize(x):
|
||||
return x / np.sqrt(np.sum(np.multiply(x, x)))
|
||||
|
||||
"""def l2_normalize(x, axis=-1, epsilon=1e-10):
|
||||
output = x / np.sqrt(np.maximum(np.sum(np.square(x), axis=axis, keepdims=True), epsilon))
|
||||
return output"""
|
||||
@ -16,6 +16,7 @@ import multiprocessing
|
||||
import subprocess
|
||||
import tensorflow as tf
|
||||
import keras
|
||||
import bz2
|
||||
|
||||
def loadBase64Img(uri):
|
||||
encoded_data = uri.split(',')[1]
|
||||
@ -29,18 +30,6 @@ def distance(a, b):
|
||||
|
||||
return math.sqrt(((x2 - x1) * (x2 - x1)) + ((y2 - y1) * (y2 - y1)))
|
||||
|
||||
def findFileHash(file):
|
||||
BLOCK_SIZE = 65536 # The size of each read from the file
|
||||
|
||||
file_hash = hashlib.sha256() # Create the hash object, can use something other than `.sha256()` if you wish
|
||||
with open(file, 'rb') as f: # Open the file to read it's bytes
|
||||
fb = f.read(BLOCK_SIZE) # Read from the file. Take in the amount declared above
|
||||
while len(fb) > 0: # While there is still data being read from the file
|
||||
file_hash.update(fb) # Update the hash
|
||||
fb = f.read(BLOCK_SIZE) # Read the next block from the file
|
||||
|
||||
return file_hash.hexdigest()
|
||||
|
||||
def initializeFolder():
|
||||
|
||||
home = str(Path.home())
|
||||
@ -53,46 +42,6 @@ def initializeFolder():
|
||||
os.mkdir(home+"/.deepface/weights")
|
||||
print("Directory ",home,"/.deepface/weights created")
|
||||
|
||||
#----------------------------------
|
||||
"""
|
||||
#avoid interrupted file download
|
||||
|
||||
weight_hashes = [
|
||||
['age_model_weights.h5', '0aeff75734bfe794113756d2bfd0ac823d51e9422c8961125b570871d3c2b114']
|
||||
, ['facenet_weights.h5', '90659cc97bfda5999120f95d8e122f4d262cca11715a21e59ba024bcce816d5c']
|
||||
, ['facial_expression_model_weights.h5', 'e8e8851d3fa05c001b1c27fd8841dfe08d7f82bb786a53ad8776725b7a1e824c']
|
||||
, ['gender_model_weights.h5', '45513ce5678549112d25ab85b1926fb65986507d49c674a3d04b2ba70dba2eb5']
|
||||
, ['openface_weights.h5', '5b41897ec6dd762cee20575eee54ed4d719a78cb982b2080a87dc14887d88a7a']
|
||||
, ['race_model_single_batch.h5', 'eb22b28b1f6dfce65b64040af4e86003a5edccb169a1a338470dde270b6f5e54']
|
||||
, ['vgg_face_weights.h5', '759266b9614d0fd5d65b97bf716818b746cc77ab5944c7bffc937c6ba9455d8c']
|
||||
]
|
||||
|
||||
for i in weight_hashes:
|
||||
|
||||
weight_file = home+"/.deepface/weights/"+i[0]
|
||||
expected_hash = i[1]
|
||||
|
||||
#check file exits
|
||||
if os.path.isfile(weight_file) == True:
|
||||
current_hash = findFileHash(weight_file)
|
||||
if current_hash != expected_hash:
|
||||
print("hash violated for ", i[0],". It's going to be removed.")
|
||||
os.remove(weight_file)
|
||||
"""
|
||||
#----------------------------------
|
||||
|
||||
"""
|
||||
TODO: C4.5 tree finds the following split points for cosine, euclidean, euclidean_l2 respectively.
|
||||
Check these thresholds in unit tests.
|
||||
vgg-face: 0.3147, 0.4764, 0.7933
|
||||
facenet: 0.4062, 11.2632, 0.9014
|
||||
openface: 0.1118, 0.4729, 0.4729
|
||||
deepface: 0.1349, 42.2178, 0.5194
|
||||
"""
|
||||
|
||||
"""
|
||||
TODO: create an ensemble method
|
||||
"""
|
||||
def findThreshold(model_name, distance_metric):
|
||||
|
||||
threshold = 0.40
|
||||
@ -154,20 +103,10 @@ def get_opencv_path():
|
||||
path = folders[0]
|
||||
for folder in folders[1:]:
|
||||
path = path + "/" + folder
|
||||
|
||||
face_detector_path = path+"/data/haarcascade_frontalface_default.xml"
|
||||
eye_detector_path = path+"/data/haarcascade_eye.xml"
|
||||
|
||||
if os.path.isfile(face_detector_path) != True:
|
||||
raise ValueError("Confirm that opencv is installed on your environment! Expected path ",face_detector_path," violated.")
|
||||
|
||||
return path+"/data/"
|
||||
|
||||
def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_detection = True):
|
||||
|
||||
img_path = ""
|
||||
|
||||
#-----------------------
|
||||
def load_image(img):
|
||||
|
||||
exact_image = False
|
||||
if type(img).__module__ == np.__name__:
|
||||
@ -176,56 +115,218 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
|
||||
base64_img = False
|
||||
if len(img) > 11 and img[0:11] == "data:image/":
|
||||
base64_img = True
|
||||
|
||||
#-----------------------
|
||||
|
||||
opencv_path = get_opencv_path()
|
||||
face_detector_path = opencv_path+"haarcascade_frontalface_default.xml"
|
||||
eye_detector_path = opencv_path+"haarcascade_eye.xml"
|
||||
|
||||
if os.path.isfile(face_detector_path) != True:
|
||||
raise ValueError("Confirm that opencv is installed on your environment! Expected path ",face_detector_path," violated.")
|
||||
|
||||
#--------------------------------
|
||||
|
||||
face_detector = cv2.CascadeClassifier(face_detector_path)
|
||||
eye_detector = cv2.CascadeClassifier(eye_detector_path)
|
||||
#---------------------------
|
||||
|
||||
if base64_img == True:
|
||||
img = loadBase64Img(img)
|
||||
|
||||
elif exact_image != True: #image path passed as input
|
||||
|
||||
if os.path.isfile(img) != True:
|
||||
raise ValueError("Confirm that ",img," exists")
|
||||
|
||||
img = cv2.imread(img)
|
||||
|
||||
img_raw = img.copy()
|
||||
return img
|
||||
|
||||
#--------------------------------
|
||||
def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_detection = True):
|
||||
|
||||
faces = []
|
||||
detectors = ['opencv', 'ssd', 'dlib', 'mtcnn']
|
||||
|
||||
try:
|
||||
faces = face_detector.detectMultiScale(img, 1.3, 5)
|
||||
except:
|
||||
pass
|
||||
if detector_backend not in detectors:
|
||||
raise ValueError("Valid backends are ", detectors," but you passed ", detector_backend)
|
||||
|
||||
#print("found faces in ",image_path," is ",len(faces))
|
||||
#---------------------------
|
||||
|
||||
if len(faces) > 0:
|
||||
x,y,w,h = faces[0]
|
||||
detected_face = img[int(y):int(y+h), int(x):int(x+w)]
|
||||
detected_face_gray = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY)
|
||||
home = str(Path.home())
|
||||
|
||||
if detector_backend == 'opencv':
|
||||
|
||||
#get opencv configuration up first
|
||||
opencv_path = get_opencv_path()
|
||||
face_detector_path = opencv_path+"haarcascade_frontalface_default.xml"
|
||||
|
||||
if os.path.isfile(face_detector_path) != True:
|
||||
raise ValueError("Confirm that opencv is installed on your environment! Expected path ",face_detector_path," violated.")
|
||||
|
||||
face_detector = cv2.CascadeClassifier(face_detector_path)
|
||||
|
||||
#--------------------------
|
||||
|
||||
faces = []
|
||||
|
||||
try:
|
||||
faces = face_detector.detectMultiScale(img, 1.3, 5)
|
||||
except:
|
||||
pass
|
||||
|
||||
if len(faces) > 0:
|
||||
x,y,w,h = faces[0] #focus on the 1st face found in the image
|
||||
detected_face = img[int(y):int(y+h), int(x):int(x+w)]
|
||||
return detected_face
|
||||
|
||||
else: #if no face detected
|
||||
|
||||
if enforce_detection != True:
|
||||
return img
|
||||
|
||||
else:
|
||||
raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
|
||||
|
||||
elif detector_backend == 'ssd':
|
||||
|
||||
#---------------------------
|
||||
#face alignment
|
||||
#check required ssd model exists in the home/.deepface/weights folder
|
||||
|
||||
#model structure
|
||||
if os.path.isfile(home+'/.deepface/weights/deploy.prototxt') != True:
|
||||
|
||||
print("deploy.prototxt will be downloaded...")
|
||||
|
||||
url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt"
|
||||
|
||||
output = home+'/.deepface/weights/deploy.prototxt'
|
||||
|
||||
gdown.download(url, output, quiet=False)
|
||||
|
||||
|
||||
#pre-trained weights
|
||||
if os.path.isfile(home+'/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel') != True:
|
||||
|
||||
print("res10_300x300_ssd_iter_140000.caffemodel will be downloaded...")
|
||||
|
||||
url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
|
||||
|
||||
output = home+'/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel'
|
||||
|
||||
gdown.download(url, output, quiet=False)
|
||||
|
||||
#---------------------------
|
||||
|
||||
ssd_detector = cv2.dnn.readNetFromCaffe(
|
||||
home+"/.deepface/weights/deploy.prototxt",
|
||||
home+"/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel"
|
||||
)
|
||||
|
||||
ssd_labels = ["img_id", "is_face", "confidence", "left", "top", "right", "bottom"]
|
||||
|
||||
target_size = (300, 300)
|
||||
|
||||
base_img = img.copy() #we will restore base_img to img later
|
||||
|
||||
original_size = img.shape
|
||||
|
||||
img = cv2.resize(img, target_size)
|
||||
|
||||
aspect_ratio_x = (original_size[1] / target_size[1])
|
||||
aspect_ratio_y = (original_size[0] / target_size[0])
|
||||
|
||||
imageBlob = cv2.dnn.blobFromImage(image = img)
|
||||
|
||||
ssd_detector.setInput(imageBlob)
|
||||
detections = ssd_detector.forward()
|
||||
|
||||
detections_df = pd.DataFrame(detections[0][0], columns = ssd_labels)
|
||||
|
||||
detections_df = detections_df[detections_df['is_face'] == 1] #0: background, 1: face
|
||||
detections_df = detections_df[detections_df['confidence'] >= 0.90]
|
||||
|
||||
detections_df['left'] = (detections_df['left'] * 300).astype(int)
|
||||
detections_df['bottom'] = (detections_df['bottom'] * 300).astype(int)
|
||||
detections_df['right'] = (detections_df['right'] * 300).astype(int)
|
||||
detections_df['top'] = (detections_df['top'] * 300).astype(int)
|
||||
|
||||
if detections_df.shape[0] > 0:
|
||||
|
||||
#TODO: sort detections_df
|
||||
|
||||
#get the first face in the image
|
||||
instance = detections_df.iloc[0]
|
||||
|
||||
left = instance["left"]
|
||||
right = instance["right"]
|
||||
bottom = instance["bottom"]
|
||||
top = instance["top"]
|
||||
|
||||
detected_face = base_img[int(top*aspect_ratio_y):int(bottom*aspect_ratio_y), int(left*aspect_ratio_x):int(right*aspect_ratio_x)]
|
||||
|
||||
return detected_face
|
||||
|
||||
else: #if no face detected
|
||||
|
||||
if enforce_detection != True:
|
||||
img = base_img.copy()
|
||||
return img
|
||||
|
||||
else:
|
||||
raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
|
||||
|
||||
elif detector_backend == 'dlib':
|
||||
import dlib #this is not a must library within deepface. that's why, I didn't put this import to a global level. version: 19.20.0
|
||||
|
||||
detector = dlib.get_frontal_face_detector()
|
||||
|
||||
detections = detector(img, 1)
|
||||
|
||||
if len(detections) > 0:
|
||||
|
||||
for idx, d in enumerate(detections):
|
||||
left = d.left(); right = d.right()
|
||||
top = d.top(); bottom = d.bottom()
|
||||
|
||||
detected_face = img[top:bottom, left:right]
|
||||
|
||||
return detected_face
|
||||
|
||||
else: #if no face detected
|
||||
|
||||
if enforce_detection != True:
|
||||
return img
|
||||
|
||||
else:
|
||||
raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
|
||||
|
||||
elif detector_backend == 'mtcnn':
|
||||
|
||||
from mtcnn import MTCNN #this is not a must library within deepface
|
||||
|
||||
mtcnn_detector = MTCNN()
|
||||
|
||||
detections = mtcnn_detector.detect_faces(img)
|
||||
|
||||
if len(detections) > 0:
|
||||
detection = detections[0]
|
||||
x, y, w, h = detection["box"]
|
||||
detected_face = img[int(y):int(y+h), int(x):int(x+w)]
|
||||
return detected_face
|
||||
|
||||
else: #if no face detected
|
||||
if enforce_detection != True:
|
||||
return img
|
||||
|
||||
else:
|
||||
raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
|
||||
|
||||
return 0
|
||||
|
||||
def align_face(img, detector_backend = 'opencv'):
|
||||
|
||||
home = str(Path.home())
|
||||
|
||||
if (detector_backend == 'opencv') or (detector_backend == 'ssd'):
|
||||
|
||||
opencv_path = get_opencv_path()
|
||||
eye_detector_path = opencv_path+"haarcascade_eye.xml"
|
||||
eye_detector = cv2.CascadeClassifier(eye_detector_path)
|
||||
|
||||
detected_face_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #eye detector expects gray scale image
|
||||
|
||||
eyes = eye_detector.detectMultiScale(detected_face_gray)
|
||||
|
||||
if len(eyes) >= 2:
|
||||
|
||||
#find the largest 2 eye
|
||||
|
||||
base_eyes = eyes[:, 2]
|
||||
|
||||
items = []
|
||||
@ -243,11 +344,9 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
|
||||
eye_1 = eyes[0]; eye_2 = eyes[1]
|
||||
|
||||
if eye_1[0] < eye_2[0]:
|
||||
left_eye = eye_1
|
||||
right_eye = eye_2
|
||||
left_eye = eye_1; right_eye = eye_2
|
||||
else:
|
||||
left_eye = eye_2
|
||||
right_eye = eye_1
|
||||
left_eye = eye_2; right_eye = eye_1
|
||||
|
||||
#-----------------------
|
||||
#find center of eyes
|
||||
@ -290,49 +389,139 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
|
||||
if direction == -1:
|
||||
angle = 90 - angle
|
||||
|
||||
img = Image.fromarray(img_raw)
|
||||
img = Image.fromarray(img)
|
||||
img = np.array(img.rotate(direction * angle))
|
||||
|
||||
#you recover the base image and face detection disappeared. apply again.
|
||||
faces = face_detector.detectMultiScale(img, 1.3, 5)
|
||||
if len(faces) > 0:
|
||||
x,y,w,h = faces[0]
|
||||
detected_face = img[int(y):int(y+h), int(x):int(x+w)]
|
||||
return img
|
||||
|
||||
else:
|
||||
return img
|
||||
|
||||
elif detector_backend == 'dlib':
|
||||
|
||||
#check required file exists in the home/.deepface/weights folder
|
||||
|
||||
if os.path.isfile(home+'/.deepface/weights/shape_predictor_5_face_landmarks.dat') != True:
|
||||
|
||||
print("shape_predictor_5_face_landmarks.dat.bz2 is going to be downloaded")
|
||||
|
||||
url = "http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2"
|
||||
output = home+'/.deepface/weights/'+url.split("/")[-1]
|
||||
|
||||
gdown.download(url, output, quiet=False)
|
||||
|
||||
zipfile = bz2.BZ2File(output)
|
||||
data = zipfile.read()
|
||||
newfilepath = output[:-4] #discard .bz2 extension
|
||||
open(newfilepath, 'wb').write(data)
|
||||
|
||||
#------------------------------
|
||||
|
||||
import dlib
|
||||
|
||||
detector = dlib.get_frontal_face_detector()
|
||||
sp = dlib.shape_predictor(home+"/.deepface/weights/shape_predictor_5_face_landmarks.dat")
|
||||
|
||||
detections = detector(img, 1)
|
||||
|
||||
if len(detections) > 0:
|
||||
detected_face = detections[0]
|
||||
img_shape = sp(img, detected_face)
|
||||
img_aligned = dlib.get_face_chip(img, img_shape)
|
||||
return img_aligned
|
||||
else:
|
||||
return img
|
||||
|
||||
elif detector_backend == 'mtcnn':
|
||||
from mtcnn import MTCNN
|
||||
mtcnn_detector = MTCNN()
|
||||
detections = mtcnn_detector.detect_faces(img)
|
||||
|
||||
if len(detections) > 0:
|
||||
detection = detections[0]
|
||||
|
||||
keypoints = detection["keypoints"]
|
||||
left_eye = keypoints["left_eye"]
|
||||
right_eye = keypoints["right_eye"]
|
||||
|
||||
left_eye_x, left_eye_y = left_eye
|
||||
right_eye_x, right_eye_y = right_eye
|
||||
|
||||
#-----------------------
|
||||
|
||||
#face alignment block end
|
||||
#---------------------------
|
||||
|
||||
#face alignment block needs colorful images. that's why, converting to gray scale logic moved to here.
|
||||
if grayscale == True:
|
||||
detected_face = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
detected_face = cv2.resize(detected_face, target_size)
|
||||
|
||||
img_pixels = image.img_to_array(detected_face)
|
||||
img_pixels = np.expand_dims(img_pixels, axis = 0)
|
||||
|
||||
#normalize input in [0, 1]
|
||||
img_pixels /= 255
|
||||
|
||||
return img_pixels
|
||||
|
||||
else:
|
||||
|
||||
if (exact_image == True) or (enforce_detection != True):
|
||||
#find rotation direction
|
||||
|
||||
if left_eye_y > right_eye_y:
|
||||
point_3rd = (right_eye_x, left_eye_y)
|
||||
direction = -1 #rotate same direction to clock
|
||||
else:
|
||||
point_3rd = (left_eye_x, right_eye_y)
|
||||
direction = 1 #rotate inverse direction of clock
|
||||
|
||||
if grayscale == True:
|
||||
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||
#-----------------------
|
||||
#find length of triangle edges
|
||||
|
||||
a = distance(left_eye, point_3rd)
|
||||
b = distance(right_eye, point_3rd)
|
||||
c = distance(right_eye, left_eye)
|
||||
|
||||
#-----------------------
|
||||
#apply cosine rule
|
||||
|
||||
if b != 0 and c != 0: #this multiplication causes division by zero in cos_a calculation
|
||||
|
||||
cos_a = (b*b + c*c - a*a)/(2*b*c)
|
||||
angle = np.arccos(cos_a) #angle in radian
|
||||
angle = (angle * 180) / math.pi #radian to degree
|
||||
|
||||
#-----------------------
|
||||
#rotate base image
|
||||
|
||||
if direction == -1:
|
||||
angle = 90 - angle
|
||||
|
||||
img = Image.fromarray(img)
|
||||
img = np.array(img.rotate(direction * angle))
|
||||
|
||||
return img #return img anyway
|
||||
|
||||
img = cv2.resize(img, target_size)
|
||||
img_pixels = image.img_to_array(img)
|
||||
img_pixels = np.expand_dims(img_pixels, axis = 0)
|
||||
img_pixels /= 255
|
||||
return img_pixels
|
||||
else:
|
||||
raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
|
||||
|
||||
return img
|
||||
|
||||
def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_detection = True, detector_backend = 'opencv'):
|
||||
|
||||
#img might be path, base64 or numpy array. Convert it to numpy whatever it is.
|
||||
img = load_image(img)
|
||||
img_base = img.copy()
|
||||
|
||||
#face detection
|
||||
img = detect_face(img = img, detector_backend = detector_backend, grayscale = grayscale, enforce_detection = enforce_detection)
|
||||
|
||||
#--------------------------
|
||||
|
||||
#face alignment
|
||||
#img = align_face(img = img, detector_backend = detector_backend)
|
||||
img = align_face(img = img_base, detector_backend = detector_backend)
|
||||
|
||||
img = detect_face(img = img, detector_backend = detector_backend, grayscale = grayscale, enforce_detection = False)
|
||||
|
||||
#note: if you apply align first and detect second, it might be problematic for pictures including more than one faces.
|
||||
#we detected one face and align the base image based on the detected one.
|
||||
#pros: aligned images have many black pixels if you align detected face
|
||||
#cons: this requires to apply detection twice.
|
||||
|
||||
#--------------------------
|
||||
|
||||
#post-processing
|
||||
if grayscale == True:
|
||||
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
img = cv2.resize(img, target_size)
|
||||
img_pixels = image.img_to_array(img)
|
||||
img_pixels = np.expand_dims(img_pixels, axis = 0)
|
||||
img_pixels /= 255 #normalize input in [0, 1]
|
||||
|
||||
return img_pixels
|
||||
|
||||
def allocateMemory():
|
||||
|
||||
#find allocated memories
|
||||
@ -418,5 +607,3 @@ def allocateMemory():
|
||||
print("DeepFace will run on CPU")
|
||||
else:
|
||||
print("DeepFace will run on CPU")
|
||||
|
||||
#------------------------------
|
||||
|
||||
@ -8,7 +8,7 @@ os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
|
||||
|
||||
dataset = [
|
||||
['dataset/img1.jpg', 'dataset/img2.jpg', True],
|
||||
['dataset/img5.jpg', 'dataset/img6.jpg', True]
|
||||
['dataset/img1.jpg', 'dataset/img6.jpg', True]
|
||||
]
|
||||
|
||||
print("-----------------------------------------")
|
||||
@ -192,7 +192,7 @@ for i in range(0, len(dataset)):
|
||||
#-----------------------------------
|
||||
print("--------------------------")
|
||||
|
||||
print("Pre-trained ensemled method")
|
||||
print("Pre-trained ensemble method")
|
||||
|
||||
from deepface import DeepFace
|
||||
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user