def getConvImage(name, layer=2, index=0):
image = model_images.readImage(name, cv_2=True)
image = model_images.getFaces(image)[0]
images = getConvList(image, layer=8)
img = images[index]
img = Image.fromarray(img)
return img
def extract_faces(source=None, destination=None):
if (source == None): source = os.path.join(os.getcwd(), 'lfw_data')
if (destination == None):
destination = os.path.join(os.getcwd(), 'lfw_data_faces')
for folder in os.listdir(source):
folder_path = os.path.join(source, folder)
os.mkdir(os.path.join(destination, folder))
for img in os.listdir(folder_path):
img_path = os.path.join(folder_path, img)
print(img_path)
faces = getFaces(readImage(img_path, cv_2=True))
if len(faces) > 0:
face = faces[0]
face.save(os.path.join(destination, folder, img))
import numpy as np
import cv2
from PIL import Image
import time
import pickle
import os
import pandas as pd
loaded_model = pickle.load(open('model.sav', 'rb'))
while True:
tic = time.time()
IMG = readImage('download.jpg', cv_2=True)
faces = getFaces(IMG)
if len(faces) != 0:
face = faces[0]
vec = getFeatureVector(face)
cluster = loaded_model.predict([vec])
path = os.path.join(os.getcwd(), 'Clusters')
cl_path = os.path.join(path, 'Cluster_' + str(cluster[0]),
'cluster_' + str(cluster[0]) + '_data.csv')
df = pd.read_csv(cl_path)
vectors = df.to_numpy()
for r_idx, og_vec in enumerate(vectors):
if verifyFaceVector(og_vec, vec, print_score=True):
import os
import numpy as np
from PIL import Image
from model_images import getFaces, highlightFaces, readImage, cv2_to_pil
path = os.path.join(os.getcwd(), 'IMDB_Data')
new_path = os.path.join(os.getcwd(), 'Data')
file_no = 0
for file in os.listdir(path):
print('Processing file: ', file_no, '\n')
img_dir = os.path.join(path, file)
list_images = os.listdir(img_dir)
new_file = os.path.join(new_path, 'file_' + str(file_no))
os.mkdir(new_file)
img_no = 0
for image in list_images:
img = readImage(os.path.join(img_dir, image), cv_2=True)
images = getFaces(img)
if len(images) != 0:
img = images[0]
name = str(img_no) + '.jpg'
save_path = os.path.join(new_file, name)
img.save(save_path)
img_no += 1
file_no += 1
cl_np = np.asarray(df.drop(columns = 'Unnamed: 0'))
match_idx = verifyVecMat(np.asarray(vec_matrix[idx]), cl_np)
if match_idx != None: match_index.append(idx_col[match_idx])
else: match_index.append(None)
time_2 = time.time()
print('Task: Query Dataframe Time Required: ', time_2-time_1)
return match_index
preload_dataframe()
while True:
scr_pil = np.array(ImageGrab.grab(bbox = (0, 100, 400, 300)))
scr_cv = cv2.cvtColor(scr_pil, cv2.COLOR_BGR2RGB)
time0 = time.time()
faces = getFaces(scr_cv)
time1 = time.time()
print('Task: Extract Faces Time Required: ', time1-time0)
faces = np.asarray([np.asarray(face) for face in faces])
time2 = time.time()
print('Task: Faces as Numpy Time Required: ', time2-time1)
print(len(faces), ' faces detected.')
text = []
if len(faces) > 0:
time3 = time.time()
vec_matrix = getFeatureVector(faces, matrix = True)
time4 = time.time()