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Merge pull request #9 from Xu-jianwen/dev

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Xu-jianwen 2021-08-11 13:19:37 +08:00 committed by GitHub
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1 changed files with 39 additions and 26 deletions
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65
video_face_rec.py
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@ -1,10 +1,10 @@
import os
from tqdm import tqdm
import pickle
import numpy as np
import pandas as pd
import cv2
import time
import re
import os
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
@ -18,9 +18,13 @@ def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', dist
face_detector = FaceDetector.build_model(detector_backend)
print("Detector backend is ", detector_backend)
model = DeepFace.build_model(model_name)
print(model_name, "is built")
threshold = dst.findThreshold(model_name, distance_metric)
input_shape = (224, 224); input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
text_color = (255, 255, 255)
employees = []
if os.path.isdir(db_path) == True:
for r, d, f in os.walk(db_path):
@ -28,39 +32,48 @@ def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', dist
if('.jpg' in file):
exact_path = r + "/" + file
employees.append(exact_path)
if len(employees) == 0:
print("WARNING: There is no image in this path ( ", db_path,") . Face recognition will not be performed.")
if len(employees) > 0:
model = DeepFace.build_model(model_name)
print(model_name, "is built")
if os.path.isdir(db_path) == True:
file_name = "representations_%s.pkl" % (model_name)
file_name = file_name.replace("-", "_").lower()
input_shape = functions.find_input_shape(model)
input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
if os.path.exists(db_path+"/"+file_name):
threshold = dst.findThreshold(model_name, distance_metric)
tic = time.time()
print("Representations for images in ",db_path," folder were previously stored in ", file_name, ". If you added new instances after this file creation, then please delete this file and call find function again. It will create it again.")
pbar = tqdm(range(0, len(employees)), desc='Finding embeddings')
embeddings = []
for index in pbar:
employee = employees[index]
pbar.set_description("Finding embedding for %s" % (employee.split("/")[-1]))
embedding = []
img = functions.preprocess_face(img = employee, target_size = (input_shape_y, input_shape_x), enforce_detection = False, detector_backend = detector_backend)
img_representation = model.predict(img)[0,:]
f = open(db_path+'/'+file_name, 'rb')
embeddings = pickle.load(f)
embedding.append(employee)
embedding.append(img_representation)
embeddings.append(embedding)
print("There are ", len(embeddings)," representations found in ",file_name)
else:
if len(employees) == 0:
print("WARNING: There is no image in this path ( ", db_path,") . Face recognition will not be performed.")
if len(employees) > 0:
input_shape = functions.find_input_shape(model)
input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
tic = time.time()
pbar = tqdm(range(0, len(employees)), desc='Finding embeddings')
embeddings = []
for index in pbar:
employee = employees[index]
pbar.set_description("Finding embedding for %s" % (employee.split("/")[-1]))
embedding = []
img = functions.preprocess_face(img = employee, target_size = (input_shape_y, input_shape_x), enforce_detection = False, detector_backend = detector_backend)
img_representation = model.predict(img)[0,:]
embedding.append(employee)
embedding.append(img_representation)
embeddings.append(embedding)
f = open(db_path+'/'+file_name, "wb")
pickle.dump(embeddings, f)
f.close()
toc = time.time()
print("Embeddings found for given data set in ", toc-tic," seconds")
df = pd.DataFrame(embeddings, columns = ['employee', 'embedding'])
df['distance_metric'] = distance_metric
toc = time.time()
print("Embeddings found for given data set in ", toc-tic," seconds")
frame_count = 0
start_time = time.time()