mirror of
https://github.com/serengil/deepface.git
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599 lines
19 KiB
Python
599 lines
19 KiB
Python
import os
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import numpy as np
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import pandas as pd
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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 cv2
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from pathlib import Path
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import gdown
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import hashlib
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import math
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from PIL import Image
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import copy
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import base64
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import multiprocessing
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import subprocess
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import tensorflow as tf
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import keras
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import bz2
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from deepface.commons import distance
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from mtcnn import MTCNN # 0.1.0
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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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def initializeFolder():
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home = str(Path.home())
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if not os.path.exists(home + "/.deepface"):
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os.mkdir(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.mkdir(home + "/.deepface/weights")
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print("Directory ", home, "/.deepface/weights created")
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def findThreshold(model_name, distance_metric):
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threshold = 0.40
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if model_name == 'VGG-Face':
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if distance_metric == 'cosine':
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threshold = 0.40
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elif distance_metric == 'euclidean':
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threshold = 0.55
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elif distance_metric == 'euclidean_l2':
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threshold = 0.75
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elif model_name == 'OpenFace':
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if distance_metric == 'cosine':
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threshold = 0.10
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elif distance_metric == 'euclidean':
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threshold = 0.55
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elif distance_metric == 'euclidean_l2':
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threshold = 0.55
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elif model_name == 'Facenet':
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if distance_metric == 'cosine':
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threshold = 0.40
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elif distance_metric == 'euclidean':
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threshold = 10
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elif distance_metric == 'euclidean_l2':
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threshold = 0.80
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elif model_name == 'DeepFace':
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if distance_metric == 'cosine':
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threshold = 0.23
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elif distance_metric == 'euclidean':
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threshold = 64
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elif distance_metric == 'euclidean_l2':
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threshold = 0.64
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elif model_name == 'DeepID':
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if distance_metric == 'cosine':
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threshold = 0.015
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elif distance_metric == 'euclidean':
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threshold = 45
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elif distance_metric == 'euclidean_l2':
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threshold = 0.17
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elif model_name == 'Dlib':
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if distance_metric == 'cosine':
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threshold = 0.07
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elif distance_metric == 'euclidean':
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threshold = 0.60
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elif distance_metric == 'euclidean_l2':
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threshold = 0.60
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return threshold
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def get_opencv_path():
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opencv_home = cv2.__file__
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folders = opencv_home.split(os.path.sep)[0:-1]
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path = folders[0]
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for folder in folders[1:]:
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path = path + "/" + folder
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return path + "/data/"
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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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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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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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img = cv2.imread(img)
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return img
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def detect_face(img, detector_backend='opencv', grayscale=False, enforce_detection=True):
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home = str(Path.home())
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if detector_backend == 'opencv':
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# get opencv configuration up first
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opencv_path = get_opencv_path()
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face_detector_path = opencv_path + "haarcascade_frontalface_default.xml"
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if os.path.isfile(face_detector_path) != True:
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raise ValueError("Confirm that opencv is installed on your environment! Expected path ", face_detector_path,
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" violated.")
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face_detector = cv2.CascadeClassifier(face_detector_path)
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# --------------------------
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faces = []
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try:
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faces = face_detector.detectMultiScale(img, 1.3, 5)
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except:
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pass
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if len(faces) > 0:
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detected_faces = []
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for face in faces:
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print(face)
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x, y, w, h = face
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detected_face = img[int(y):int(y + h), int(x):int(x + w)]
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detected_faces.append(detected_face)
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return detected_faces
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else: # if no face detected
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if enforce_detection != True:
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return img
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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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elif detector_backend == 'ssd':
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# ---------------------------
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# check required ssd model exists in the home/.deepface/weights folder
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# model structure
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if os.path.isfile(home + '/.deepface/weights/deploy.prototxt') != True:
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print("deploy.prototxt will be downloaded...")
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url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt"
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output = home + '/.deepface/weights/deploy.prototxt'
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gdown.download(url, output, quiet=False)
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# pre-trained weights
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if os.path.isfile(home + '/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel') != True:
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print("res10_300x300_ssd_iter_140000.caffemodel will be downloaded...")
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url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
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output = home + '/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel'
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gdown.download(url, output, quiet=False)
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# ---------------------------
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ssd_detector = cv2.dnn.readNetFromCaffe(
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home + "/.deepface/weights/deploy.prototxt",
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home + "/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel"
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)
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ssd_labels = ["img_id", "is_face", "confidence", "left", "top", "right", "bottom"]
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target_size = (300, 300)
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base_img = img.copy() # we will restore base_img to img later
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original_size = img.shape
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img = cv2.resize(img, target_size)
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aspect_ratio_x = (original_size[1] / target_size[1])
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aspect_ratio_y = (original_size[0] / target_size[0])
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imageBlob = cv2.dnn.blobFromImage(image=img)
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ssd_detector.setInput(imageBlob)
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detections = ssd_detector.forward()
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detections_df = pd.DataFrame(detections[0][0], columns=ssd_labels)
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detections_df = detections_df[detections_df['is_face'] == 1] # 0: background, 1: face
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detections_df = detections_df[detections_df['confidence'] >= 0.90]
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detections_df['left'] = (detections_df['left'] * 300).astype(int)
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detections_df['bottom'] = (detections_df['bottom'] * 300).astype(int)
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detections_df['right'] = (detections_df['right'] * 300).astype(int)
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detections_df['top'] = (detections_df['top'] * 300).astype(int)
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if detections_df.shape[0] > 0:
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# TODO: sort detections_df
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# get the first face in the image
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instance = detections_df.iloc[0]
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left = instance["left"]
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right = instance["right"]
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bottom = instance["bottom"]
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top = instance["top"]
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detected_face = base_img[int(top * aspect_ratio_y):int(bottom * aspect_ratio_y),
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int(left * aspect_ratio_x):int(right * aspect_ratio_x)]
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return detected_face
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else: # if no face detected
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if enforce_detection != True:
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img = base_img.copy()
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return img
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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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elif detector_backend == 'dlib':
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import \
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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
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detector = dlib.get_frontal_face_detector()
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detections = detector(img, 1)
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if len(detections) > 0:
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for idx, d in enumerate(detections):
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left = d.left();
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right = d.right()
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top = d.top();
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bottom = d.bottom()
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detected_face = img[top:bottom, left:right]
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return detected_face
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else: # if no face detected
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if enforce_detection != True:
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return img
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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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elif detector_backend == 'mtcnn':
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mtcnn_detector = MTCNN()
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detections = mtcnn_detector.detect_faces(img)
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if len(detections) > 0:
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detected_faces = []
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for detection in detections:
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x, y, w, h = detection["box"]
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detected_face = img[int(y):int(y + h), int(x):int(x + w)]
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detected_faces.append(detected_face)
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return detected_faces
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else: # if no face detected
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if enforce_detection != True:
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return img
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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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else:
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detectors = ['opencv', 'ssd', 'dlib', 'mtcnn']
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raise ValueError("Valid backends are ", detectors, " but you passed ", detector_backend)
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return 0
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def alignment_procedure(img, left_eye, right_eye):
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# this function aligns given face in img based on left and right eye coordinates
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left_eye_x, left_eye_y = left_eye
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right_eye_x, right_eye_y = right_eye
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# -----------------------
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# find rotation direction
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if left_eye_y > right_eye_y:
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point_3rd = (right_eye_x, left_eye_y)
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direction = -1 # rotate same direction to clock
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else:
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point_3rd = (left_eye_x, right_eye_y)
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direction = 1 # rotate inverse direction of clock
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# -----------------------
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# find length of triangle edges
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a = distance.findEuclideanDistance(np.array(left_eye), np.array(point_3rd))
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b = distance.findEuclideanDistance(np.array(right_eye), np.array(point_3rd))
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c = distance.findEuclideanDistance(np.array(right_eye), np.array(left_eye))
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# -----------------------
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# apply cosine rule
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if b != 0 and c != 0: # this multiplication causes division by zero in cos_a calculation
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cos_a = (b * b + c * c - a * a) / (2 * b * c)
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angle = np.arccos(cos_a) # angle in radian
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angle = (angle * 180) / math.pi # radian to degree
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# -----------------------
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# rotate base image
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if direction == -1:
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angle = 90 - angle
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img = Image.fromarray(img)
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img = np.array(img.rotate(direction * angle))
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# -----------------------
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return img # return img anyway
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def align_face(img, detector_backend='opencv'):
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home = str(Path.home())
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if (detector_backend == 'opencv') or (detector_backend == 'ssd'):
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opencv_path = get_opencv_path()
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eye_detector_path = opencv_path + "haarcascade_eye.xml"
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eye_detector = cv2.CascadeClassifier(eye_detector_path)
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detected_face_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # eye detector expects gray scale image
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eyes = eye_detector.detectMultiScale(detected_face_gray)
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if len(eyes) >= 2:
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# find the largest 2 eye
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base_eyes = eyes[:, 2]
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items = []
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for i in range(0, len(base_eyes)):
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item = (base_eyes[i], i)
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items.append(item)
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df = pd.DataFrame(items, columns=["length", "idx"]).sort_values(by=['length'], ascending=False)
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eyes = eyes[df.idx.values[0:2]] # eyes variable stores the largest 2 eye
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# -----------------------
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# decide left and right eye
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eye_1 = eyes[0];
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eye_2 = eyes[1]
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if eye_1[0] < eye_2[0]:
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left_eye = eye_1;
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right_eye = eye_2
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else:
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left_eye = eye_2;
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right_eye = eye_1
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# -----------------------
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# find center of eyes
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left_eye = (int(left_eye[0] + (left_eye[2] / 2)), int(left_eye[1] + (left_eye[3] / 2)))
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right_eye = (int(right_eye[0] + (right_eye[2] / 2)), int(right_eye[1] + (right_eye[3] / 2)))
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img = alignment_procedure(img, left_eye, right_eye)
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return img # return img anyway
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elif detector_backend == 'dlib':
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# check required file exists in the home/.deepface/weights folder
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if os.path.isfile(home + '/.deepface/weights/shape_predictor_5_face_landmarks.dat') != True:
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print("shape_predictor_5_face_landmarks.dat.bz2 is going to be downloaded")
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url = "http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2"
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output = home + '/.deepface/weights/' + url.split("/")[-1]
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gdown.download(url, output, quiet=False)
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zipfile = bz2.BZ2File(output)
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data = zipfile.read()
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newfilepath = output[:-4] # discard .bz2 extension
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open(newfilepath, 'wb').write(data)
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# ------------------------------
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import dlib # this is not a must dependency in deepface
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detector = dlib.get_frontal_face_detector()
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sp = dlib.shape_predictor(home + "/.deepface/weights/shape_predictor_5_face_landmarks.dat")
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detections = detector(img, 1)
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if len(detections) > 0:
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detected_face = detections[0]
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img_shape = sp(img, detected_face)
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img = dlib.get_face_chip(img, img_shape, size=img.shape[0])
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return img # return img anyway
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elif detector_backend == 'mtcnn':
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mtcnn_detector = MTCNN()
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detections = mtcnn_detector.detect_faces(img)
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if len(detections) > 0:
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detection = detections[0]
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keypoints = detection["keypoints"]
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left_eye = keypoints["left_eye"]
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right_eye = keypoints["right_eye"]
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img = alignment_procedure(img, left_eye, right_eye)
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return img # return img anyway
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def preprocess_face(img, target_size=(224, 224), grayscale=False, enforce_detection=True, detector_backend='opencv'):
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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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imgs = detect_face(img=img, detector_backend=detector_backend, grayscale=grayscale,
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enforce_detection=enforce_detection)
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# --------------------------
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for i in range(len(imgs)):
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img = imgs[i]
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if img.shape[0] > 0 and img.shape[1] > 0:
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imgs[i] = align_face(img=img, detector_backend=detector_backend)
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else:
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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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imgs[i] = base_img.copy()
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# --------------------------
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# post-processing
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pixels = []
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for img in imgs:
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if grayscale == True:
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img = cv2.resize(img, target_size)
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img_pixels = image.img_to_array(img)
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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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pixels.append(img_pixels)
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return {'processed': pixels, 'original': imgs}
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def allocateMemory():
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# find allocated memories
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gpu_indexes = []
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memory_usage_percentages = [];
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available_memories = [];
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total_memories = [];
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utilizations = []
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power_usages = [];
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power_capacities = []
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try:
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result = subprocess.check_output(['nvidia-smi'])
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dashboard = result.decode("utf-8").split("=|")
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dashboard = dashboard[1].split("\n")
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gpu_idx = 0
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for line in dashboard:
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if ("MiB" in line):
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power_info = line.split("|")[1]
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power_capacity = int(power_info.split("/")[-1].replace("W", ""))
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power_usage = int((power_info.split("/")[-2]).strip().split(" ")[-1].replace("W", ""))
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power_usages.append(power_usage)
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power_capacities.append(power_capacity)
|
|
|
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# ----------------------------
|
|
|
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memory_info = line.split("|")[2].replace("MiB", "").split("/")
|
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utilization_info = int(line.split("|")[3].split("%")[0])
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|
|
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allocated = int(memory_info[0])
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total_memory = int(memory_info[1])
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available_memory = total_memory - allocated
|
|
|
|
total_memories.append(total_memory)
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|
available_memories.append(available_memory)
|
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memory_usage_percentages.append(round(100 * int(allocated) / int(total_memory), 4))
|
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utilizations.append(utilization_info)
|
|
gpu_indexes.append(gpu_idx)
|
|
|
|
gpu_idx = gpu_idx + 1
|
|
|
|
gpu_count = gpu_idx * 1
|
|
|
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except Exception as err:
|
|
gpu_count = 0
|
|
# print(str(err))
|
|
|
|
# ------------------------------
|
|
|
|
df = pd.DataFrame(gpu_indexes, columns=["gpu_index"])
|
|
df["total_memories_in_mb"] = total_memories
|
|
df["available_memories_in_mb"] = available_memories
|
|
df["memory_usage_percentage"] = memory_usage_percentages
|
|
df["utilizations"] = utilizations
|
|
df["power_usages_in_watts"] = power_usages
|
|
df["power_capacities_in_watts"] = power_capacities
|
|
|
|
df = df.sort_values(by=["available_memories_in_mb"], ascending=False).reset_index(drop=True)
|
|
|
|
# ------------------------------
|
|
|
|
required_memory = 10000 # All deepface models require 9016 MiB
|
|
|
|
if df.shape[0] > 0: # has gpu
|
|
if df.iloc[0].available_memories_in_mb > required_memory:
|
|
my_gpu = str(int(df.iloc[0].gpu_index))
|
|
os.environ["CUDA_VISIBLE_DEVICES"] = my_gpu
|
|
|
|
# ------------------------------
|
|
# tf allocates all memory by default
|
|
# this block avoids greedy approach
|
|
|
|
config = tf.ConfigProto()
|
|
config.gpu_options.allow_growth = True
|
|
session = tf.Session(config=config)
|
|
keras.backend.set_session(session)
|
|
|
|
print("DeepFace will run on GPU (gpu_", my_gpu, ")")
|
|
else:
|
|
# this case has gpu but no enough memory to allocate
|
|
os.environ["CUDA_VISIBLE_DEVICES"] = "" # run it on cpu
|
|
print("Even though the system has GPUs, there is no enough space in memory to allocate.")
|
|
print("DeepFace will run on CPU")
|
|
else:
|
|
print("DeepFace will run on CPU")
|