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https://github.com/serengil/deepface.git
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reformatted the functions.py file
This commit is contained in:
Roelof Ruis
parent
f3b0b76976
commit
6f00d5956c
@ -1,6 +1,5 @@
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import os
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import numpy as np
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import pandas as pd
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import cv2
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import base64
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from pathlib import Path
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@ -8,243 +7,254 @@ from pathlib import Path
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from deepface.detectors import FaceDetector
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import tensorflow as tf
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tf_version = tf.__version__
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tf_major_version = int(tf_version.split(".")[0])
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tf_minor_version = int(tf_version.split(".")[1])
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if tf_major_version == 1:
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import keras
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from keras.preprocessing.image import load_img, save_img, img_to_array
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from keras.applications.imagenet_utils import preprocess_input
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from keras.preprocessing import image
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import keras
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from keras.preprocessing.image import load_img, save_img, img_to_array
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from keras.applications.imagenet_utils import preprocess_input
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from keras.preprocessing import image
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elif tf_major_version == 2:
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from tensorflow import keras
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from tensorflow.keras.preprocessing.image import load_img, save_img, img_to_array
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from tensorflow.keras.applications.imagenet_utils import preprocess_input
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from tensorflow.keras.preprocessing import image
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from tensorflow import keras
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from tensorflow.keras.preprocessing.image import load_img, save_img, img_to_array
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from tensorflow.keras.applications.imagenet_utils import preprocess_input
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from tensorflow.keras.preprocessing import image
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#--------------------------------------------------
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def initialize_input(img1_path, img2_path = None):
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# --------------------------------------------------
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if type(img1_path) == list:
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bulkProcess = True
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img_list = img1_path.copy()
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else:
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bulkProcess = False
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def initialize_input(img1_path, img2_path=None):
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if type(img1_path) == list:
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bulkProcess = True
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img_list = img1_path.copy()
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else:
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bulkProcess = False
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if (
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(type(img2_path) == str and img2_path != None) #exact image path, base64 image
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or (isinstance(img2_path, np.ndarray) and img2_path.any()) #numpy array
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):
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img_list = [[img1_path, img2_path]]
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else: #analyze function passes just img1_path
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img_list = [img1_path]
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if (
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(type(img2_path) == str and img2_path != None) # exact image path, base64 image
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or (isinstance(img2_path, np.ndarray) and img2_path.any()) # numpy array
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):
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img_list = [[img1_path, img2_path]]
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else: # analyze function passes just img1_path
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img_list = [img1_path]
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return img_list, bulkProcess
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return img_list, bulkProcess
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def initialize_folder():
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home = get_deepface_home()
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home = get_deepface_home()
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if not os.path.exists(home+"/.deepface"):
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os.makedirs(home+"/.deepface")
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print("Directory ", home, "/.deepface created")
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if not os.path.exists(home + "/.deepface"):
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os.makedirs(home + "/.deepface")
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print("Directory ", home, "/.deepface created")
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if not os.path.exists(home + "/.deepface/weights"):
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os.makedirs(home + "/.deepface/weights")
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print("Directory ", home, "/.deepface/weights created")
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if not os.path.exists(home+"/.deepface/weights"):
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os.makedirs(home+"/.deepface/weights")
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print("Directory ", home, "/.deepface/weights created")
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def get_deepface_home():
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return str(os.getenv('DEEPFACE_HOME', default=Path.home()))
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return str(os.getenv('DEEPFACE_HOME', default=Path.home()))
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def loadBase64Img(uri):
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encoded_data = uri.split(',')[1]
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nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
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img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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return img
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encoded_data = uri.split(',')[1]
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nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
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img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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return img
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def load_image(img):
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exact_image = False
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if type(img).__module__ == np.__name__:
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exact_image = True
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exact_image = False
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if type(img).__module__ == np.__name__:
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exact_image = True
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base64_img = False
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if len(img) > 11 and img[0:11] == "data:image/":
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base64_img = True
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base64_img = False
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if len(img) > 11 and img[0:11] == "data:image/":
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base64_img = True
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# ---------------------------
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#---------------------------
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if base64_img == True:
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img = loadBase64Img(img)
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if base64_img == True:
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img = loadBase64Img(img)
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elif exact_image != True: # image path passed as input
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if os.path.isfile(img) != True:
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raise ValueError("Confirm that ", img, " exists")
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elif exact_image != True: #image path passed as input
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if os.path.isfile(img) != True:
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raise ValueError("Confirm that ",img," exists")
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img = cv2.imread(img)
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img = cv2.imread(img)
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return img
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return img
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def detect_face(img, detector_backend = 'opencv', enforce_detection = True, align = True):
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detected_face, img_region = None, [0, 0, img.shape[0], img.shape[1]] # Assume by default that nothing is detected.
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def detect_face(img, detector_backend='opencv', enforce_detection=True, align=True):
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detected_face, img_region = None, [0, 0, img.shape[0], img.shape[1]] # Assume by default that nothing is detected.
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# ----------------------------------------------
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# people would like to skip detection and alignment if they already have pre-processed images
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if detector_backend == 'skip':
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return img, img_region
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# ----------------------------------------------
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# people would like to skip detection and alignment if they already have pre-processed images
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if detector_backend == 'skip':
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return img, img_region
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# ----------------------------------------------
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# Detector stored in a global variable in FaceDetector object.
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# this call should be completed very fast because it will return found in memory
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# it will not build face detector model in each call (consider for loops)
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face_detector = FaceDetector.build_model(detector_backend)
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# ----------------------------------------------
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# Detector stored in a global variable in FaceDetector object.
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# this call should be completed very fast because it will return found in memory
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# it will not build face detector model in each call (consider for loops)
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face_detector = FaceDetector.build_model(detector_backend)
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try:
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faces = FaceDetector.detect_faces(face_detector, detector_backend, img, align)
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if len(faces) > 0:
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detected_face, img_region = faces[0]
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try:
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faces = FaceDetector.detect_faces(face_detector, detector_backend, img, align)
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if len(faces) > 0:
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detected_face, img_region = faces[0]
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except: # if detected face shape is (0, 0) and alignment cannot be performed, this block will be run
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pass
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except: # if detected face shape is (0, 0) and alignment cannot be performed, this block will be run
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pass
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if isinstance(detected_face, np.ndarray):
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return detected_face, img_region
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else:
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if detected_face is None:
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if not enforce_detection:
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return img, img_region
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else:
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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.")
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if isinstance(detected_face, np.ndarray):
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return detected_face, img_region
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else:
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if detected_face is None:
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if not enforce_detection:
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return img, img_region
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else:
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raise ValueError(
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"Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.")
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def normalize_input(img, normalization = 'base'):
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#issue 131 declares that some normalization techniques improves the accuracy
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def normalize_input(img, normalization='base'):
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# issue 131 declares that some normalization techniques improves the accuracy
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if normalization == 'base':
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return img
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else:
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#@trevorgribble and @davedgd contributed this feature
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if normalization == 'base':
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return img
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else:
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# @trevorgribble and @davedgd contributed this feature
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img *= 255 #restore input in scale of [0, 255] because it was normalized in scale of [0, 1] in preprocess_face
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img *= 255 # restore input in scale of [0, 255] because it was normalized in scale of [0, 1] in preprocess_face
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if normalization == 'raw':
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pass #return just restored pixels
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if normalization == 'raw':
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pass # return just restored pixels
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elif normalization == 'Facenet':
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mean, std = img.mean(), img.std()
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img = (img - mean) / std
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elif normalization == 'Facenet':
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mean, std = img.mean(), img.std()
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img = (img - mean) / std
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elif(normalization=="Facenet2018"):
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# simply / 127.5 - 1 (similar to facenet 2018 model preprocessing step as @iamrishab posted)
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img /= 127.5
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img -= 1
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elif (normalization == "Facenet2018"):
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# simply / 127.5 - 1 (similar to facenet 2018 model preprocessing step as @iamrishab posted)
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img /= 127.5
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img -= 1
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elif normalization == 'VGGFace':
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# mean subtraction based on VGGFace1 training data
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img[..., 0] -= 93.5940
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img[..., 1] -= 104.7624
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img[..., 2] -= 129.1863
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elif normalization == 'VGGFace':
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# mean subtraction based on VGGFace1 training data
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img[..., 0] -= 93.5940
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img[..., 1] -= 104.7624
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img[..., 2] -= 129.1863
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elif(normalization == 'VGGFace2'):
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# mean subtraction based on VGGFace2 training data
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img[..., 0] -= 91.4953
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img[..., 1] -= 103.8827
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img[..., 2] -= 131.0912
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elif (normalization == 'VGGFace2'):
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# mean subtraction based on VGGFace2 training data
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img[..., 0] -= 91.4953
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img[..., 1] -= 103.8827
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img[..., 2] -= 131.0912
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elif(normalization == 'ArcFace'):
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#Reference study: The faces are cropped and resized to 112×112,
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#and each pixel (ranged between [0, 255]) in RGB images is normalised
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#by subtracting 127.5 then divided by 128.
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img -= 127.5
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img /= 128
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elif (normalization == 'ArcFace'):
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# Reference study: The faces are cropped and resized to 112×112,
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# and each pixel (ranged between [0, 255]) in RGB images is normalised
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# by subtracting 127.5 then divided by 128.
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img -= 127.5
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img /= 128
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#-----------------------------
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# -----------------------------
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return img
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return img
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def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_detection = True, detector_backend = 'opencv', align = True):
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#img might be path, base64 or numpy array. Convert it to numpy whatever it is.
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img = load_image(img)
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base_img = img.copy()
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def preprocess_face(img, target_size=(224, 224), grayscale=False, enforce_detection=True, detector_backend='opencv',
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align=True):
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# img might be path, base64 or numpy array. Convert it to numpy whatever it is.
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img = load_image(img)
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base_img = img.copy()
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img, region = detect_face(img = img, detector_backend = detector_backend, enforce_detection = enforce_detection, align = align)
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img, region = detect_face(img=img, detector_backend=detector_backend, enforce_detection=enforce_detection,
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align=align)
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#--------------------------
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# --------------------------
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if img.shape[0] == 0 or img.shape[1] == 0:
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if enforce_detection == True:
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raise ValueError("Detected face shape is ", img.shape,". Consider to set enforce_detection argument to False.")
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else: #restore base image
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img = base_img.copy()
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if img.shape[0] == 0 or img.shape[1] == 0:
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if enforce_detection == True:
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raise ValueError("Detected face shape is ", img.shape,
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". Consider to set enforce_detection argument to False.")
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else: # restore base image
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img = base_img.copy()
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#--------------------------
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# --------------------------
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#post-processing
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if grayscale == True:
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# post-processing
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if grayscale == True:
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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#---------------------------------------------------
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#resize image to expected shape
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# ---------------------------------------------------
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# resize image to expected shape
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# img = cv2.resize(img, target_size) #resize causes transformation on base image, adding black pixels to resize will not deform the base image
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# img = cv2.resize(img, target_size) #resize causes transformation on base image, adding black pixels to resize will not deform the base image
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factor_0 = target_size[0] / img.shape[0]
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factor_1 = target_size[1] / img.shape[1]
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factor = min(factor_0, factor_1)
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factor_0 = target_size[0] / img.shape[0]
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factor_1 = target_size[1] / img.shape[1]
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factor = min(factor_0, factor_1)
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dsize = (int(img.shape[1] * factor), int(img.shape[0] * factor))
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img = cv2.resize(img, dsize)
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dsize = (int(img.shape[1] * factor), int(img.shape[0] * factor))
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img = cv2.resize(img, dsize)
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# Then pad the other side to the target size by adding black pixels
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diff_0 = target_size[0] - img.shape[0]
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diff_1 = target_size[1] - img.shape[1]
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if grayscale == False:
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# Put the base image in the middle of the padded image
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img = np.pad(img, ((diff_0 // 2, diff_0 - diff_0 // 2), (diff_1 // 2, diff_1 - diff_1 // 2), (0, 0)), 'constant')
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else:
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img = np.pad(img, ((diff_0 // 2, diff_0 - diff_0 // 2), (diff_1 // 2, diff_1 - diff_1 // 2)), 'constant')
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# Then pad the other side to the target size by adding black pixels
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diff_0 = target_size[0] - img.shape[0]
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diff_1 = target_size[1] - img.shape[1]
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if grayscale == False:
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# Put the base image in the middle of the padded image
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img = np.pad(img, ((diff_0 // 2, diff_0 - diff_0 // 2), (diff_1 // 2, diff_1 - diff_1 // 2), (0, 0)),
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'constant')
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else:
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img = np.pad(img, ((diff_0 // 2, diff_0 - diff_0 // 2), (diff_1 // 2, diff_1 - diff_1 // 2)), 'constant')
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#double check: if target image is not still the same size with target.
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if img.shape[0:2] != target_size:
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img = cv2.resize(img, target_size)
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# double check: if target image is not still the same size with target.
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if img.shape[0:2] != target_size:
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img = cv2.resize(img, target_size)
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#---------------------------------------------------
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# ---------------------------------------------------
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#normalizing the image pixels
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# normalizing the image pixels
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img_pixels = image.img_to_array(img) #what this line doing? must?
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img_pixels = np.expand_dims(img_pixels, axis = 0)
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img_pixels /= 255 #normalize input in [0, 1]
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img_pixels = image.img_to_array(img) # what this line doing? must?
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img_pixels = np.expand_dims(img_pixels, axis=0)
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img_pixels /= 255 # normalize input in [0, 1]
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#---------------------------------------------------
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# ---------------------------------------------------
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return img_pixels, region
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return img_pixels, region
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def find_input_shape(model):
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# face recognition models have different size of inputs
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# my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
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#face recognition models have different size of inputs
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#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
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input_shape = model.layers[0].input_shape
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input_shape = model.layers[0].input_shape
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if type(input_shape) == list:
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input_shape = input_shape[0][1:3]
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else:
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input_shape = input_shape[1:3]
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if type(input_shape) == list:
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input_shape = input_shape[0][1:3]
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else:
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input_shape = input_shape[1:3]
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# ----------------------
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# issue 289: it seems that tf 2.5 expects you to resize images with (x, y)
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# whereas its older versions expect (y, x)
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#----------------------
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#issue 289: it seems that tf 2.5 expects you to resize images with (x, y)
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#whereas its older versions expect (y, x)
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if tf_major_version == 2 and tf_minor_version >= 5:
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x = input_shape[0];
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y = input_shape[1]
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input_shape = (y, x)
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if tf_major_version == 2 and tf_minor_version >= 5:
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x = input_shape[0]; y = input_shape[1]
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input_shape = (y, x)
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# ----------------------
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#----------------------
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if type(input_shape) == list: # issue 197: some people got array here instead of tuple
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input_shape = tuple(input_shape)
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if type(input_shape) == list: #issue 197: some people got array here instead of tuple
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input_shape = tuple(input_shape)
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return input_shape
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return input_shape
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