mirror of
https://github.com/serengil/deepface.git
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386 lines
11 KiB
Python
386 lines
11 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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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 distance(a, b):
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x1 = a[0]; y1 = a[1]
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x2 = b[0]; y2 = b[1]
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return math.sqrt(((x2 - x1) * (x2 - x1)) + ((y2 - y1) * (y2 - y1)))
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def findFileHash(file):
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BLOCK_SIZE = 65536 # The size of each read from the file
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file_hash = hashlib.sha256() # Create the hash object, can use something other than `.sha256()` if you wish
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with open(file, 'rb') as f: # Open the file to read it's bytes
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fb = f.read(BLOCK_SIZE) # Read from the file. Take in the amount declared above
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while len(fb) > 0: # While there is still data being read from the file
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file_hash.update(fb) # Update the hash
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fb = f.read(BLOCK_SIZE) # Read the next block from the file
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return file_hash.hexdigest()
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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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#----------------------------------
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"""
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#avoid interrupted file download
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weight_hashes = [
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['age_model_weights.h5', '0aeff75734bfe794113756d2bfd0ac823d51e9422c8961125b570871d3c2b114']
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, ['facenet_weights.h5', '90659cc97bfda5999120f95d8e122f4d262cca11715a21e59ba024bcce816d5c']
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, ['facial_expression_model_weights.h5', 'e8e8851d3fa05c001b1c27fd8841dfe08d7f82bb786a53ad8776725b7a1e824c']
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, ['gender_model_weights.h5', '45513ce5678549112d25ab85b1926fb65986507d49c674a3d04b2ba70dba2eb5']
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, ['openface_weights.h5', '5b41897ec6dd762cee20575eee54ed4d719a78cb982b2080a87dc14887d88a7a']
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, ['race_model_single_batch.h5', 'eb22b28b1f6dfce65b64040af4e86003a5edccb169a1a338470dde270b6f5e54']
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, ['vgg_face_weights.h5', '759266b9614d0fd5d65b97bf716818b746cc77ab5944c7bffc937c6ba9455d8c']
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]
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for i in weight_hashes:
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weight_file = home+"/.deepface/weights/"+i[0]
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expected_hash = i[1]
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#check file exits
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if os.path.isfile(weight_file) == True:
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current_hash = findFileHash(weight_file)
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if current_hash != expected_hash:
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print("hash violated for ", i[0],". It's going to be removed.")
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os.remove(weight_file)
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"""
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#----------------------------------
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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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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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face_detector_path = path+"/data/haarcascade_frontalface_default.xml"
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eye_detector_path = path+"/data/haarcascade_eye.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," violated.")
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return path+"/data/"
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def detectFace(img, target_size=(224, 224), grayscale = False):
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#-----------------------
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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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opencv_path = get_opencv_path()
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face_detector_path = opencv_path+"haarcascade_frontalface_default.xml"
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eye_detector_path = opencv_path+"haarcascade_eye.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," violated.")
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#--------------------------------
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face_detector = cv2.CascadeClassifier(face_detector_path)
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eye_detector = cv2.CascadeClassifier(eye_detector_path)
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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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img_raw = img.copy()
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#--------------------------------
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faces = face_detector.detectMultiScale(img, 1.3, 5)
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#print("found faces in ",image_path," is ",len(faces))
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if len(faces) > 0:
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x,y,w,h = faces[0]
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detected_face = img[int(y):int(y+h), int(x):int(x+w)]
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detected_face_gray = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY)
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#---------------------------
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#face alignment
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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]]
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#-----------------------
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#decide left and right eye
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eye_1 = eyes[0]; 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_center = (int(left_eye[0] + (left_eye[2] / 2)), int(left_eye[1] + (left_eye[3] / 2)))
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left_eye_x = left_eye_center[0]; left_eye_y = left_eye_center[1]
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right_eye_center = (int(right_eye[0] + (right_eye[2]/2)), int(right_eye[1] + (right_eye[3]/2)))
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right_eye_x = right_eye_center[0]; right_eye_y = right_eye_center[1]
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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(left_eye_center, point_3rd)
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b = distance(right_eye_center, point_3rd)
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c = distance(right_eye_center, left_eye_center)
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#-----------------------
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#apply cosine rule
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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_raw)
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img = np.array(img.rotate(direction * angle))
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#you recover the base image and face detection disappeared. apply again.
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faces = face_detector.detectMultiScale(img, 1.3, 5)
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if len(faces) > 0:
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x,y,w,h = faces[0]
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detected_face = img[int(y):int(y+h), int(x):int(x+w)]
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#-----------------------
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#face alignment block end
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#---------------------------
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#face alignment block needs colorful images. that's why, converting to gray scale logic moved to here.
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if grayscale == True:
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detected_face = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY)
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detected_face = cv2.resize(detected_face, target_size)
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img_pixels = image.img_to_array(detected_face)
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img_pixels = np.expand_dims(img_pixels, axis = 0)
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#normalize input in [0, 1]
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img_pixels /= 255
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return img_pixels
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else:
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if exact_image == True:
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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
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return img_pixels
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else:
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raise ValueError("Face could not be detected in ", img,". Please confirm that the picture is a face photo.")
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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 = []; available_memories = []; total_memories = []; utilizations = []
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power_usages = []; 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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allocated = int(memory_info[0])
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total_memory = int(memory_info[1])
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available_memory = total_memory - allocated
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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)
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gpu_indexes.append(gpu_idx)
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gpu_idx = gpu_idx + 1
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gpu_count = gpu_idx * 1
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except Exception as err:
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gpu_count = 0
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#print(str(err))
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#------------------------------
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df = pd.DataFrame(gpu_indexes, columns = ["gpu_index"])
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df["total_memories_in_mb"] = total_memories
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df["available_memories_in_mb"] = available_memories
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df["memory_usage_percentage"] = memory_usage_percentages
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df["utilizations"] = utilizations
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df["power_usages_in_watts"] = power_usages
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df["power_capacities_in_watts"] = power_capacities
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df = df.sort_values(by = ["available_memories_in_mb"], ascending = False).reset_index(drop = True)
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#------------------------------
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required_memory = 10000 #All deepface models require 9016 MiB
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if df.shape[0] > 0: #has gpu
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if df.iloc[0].available_memories_in_mb > required_memory:
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my_gpu = str(int(df.iloc[0].gpu_index))
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os.environ["CUDA_VISIBLE_DEVICES"] = my_gpu
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#------------------------------
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#tf allocates all memory by default
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#this block avoids greedy approach
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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session = tf.Session(config=config)
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keras.backend.set_session(session)
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print("DeepFace will run on GPU (gpu_", my_gpu,")")
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else:
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#this case has gpu but no enough memory to allocate
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os.environ["CUDA_VISIBLE_DEVICES"] = "" #run it on cpu
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print("Even though the system has GPUs, there is no enough space in memory to allocate.")
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print("DeepFace will run on CPU")
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else:
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print("DeepFace will run on CPU")
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#------------------------------
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