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common input initializer for img pairs as arrays

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serengil 2020-11-30 11:47:30 +03:00
parent edae2a799c
commit 3057d82e0f
2 changed files with 43 additions and 34 deletions
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65
deepface/DeepFace.py
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@ -44,16 +44,8 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
model = None, enforce_detection = True, detector_backend = 'mtcnn'):
tic = time.time()
if type(img1_path) == list:
bulkProcess = True
img_list = img1_path.copy()
else:
bulkProcess = False
img_list = [[img1_path, img2_path]]
#------------------------------
img_list, bulkProcess = initialize_input(img1_path, img2_path)
functions.initialize_detector(detector_backend = detector_backend)
resp_objects = []
@ -212,8 +204,8 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
if model == None:
model = build_model(model_name)
else: #model != None
print("Already built model is passed")
"""else: #model != None
print("Already built model is passed")"""
#------------------------------
#face recognition models have different size of inputs
@ -326,15 +318,7 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
def analyze(img_path, actions = [], models = {}, enforce_detection = True
, detector_backend = 'mtcnn'):
if type(img_path) == list:
img_paths = img_path.copy()
bulkProcess = True
else:
img_paths = [img_path]
bulkProcess = False
#---------------------------------
img_paths, bulkProcess = initialize_input(img_path)
functions.initialize_detector(detector_backend = detector_backend)
#---------------------------------
@ -474,21 +458,16 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True
def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'):
model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
metric_names = ['cosine', 'euclidean', 'euclidean_l2']
tic = time.time()
if type(img_path) == list:
bulkProcess = True
img_paths = img_path.copy()
else:
bulkProcess = False
img_paths = [img_path]
img_paths, bulkProcess = initialize_input(img_path)
functions.initialize_detector(detector_backend = detector_backend)
#-------------------------------
functions.initialize_detector(detector_backend = detector_backend)
#model metric pairs for ensemble
model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
metric_names = ['cosine', 'euclidean', 'euclidean_l2']
#-------------------------------
@ -811,6 +790,32 @@ def detectFace(img_path, detector_backend = 'mtcnn'):
img = functions.preprocess_face(img = img_path, detector_backend = detector_backend)[0] #preprocess_face returns (1, 224, 224, 3)
return img[:, :, ::-1] #bgr to rgb
def initialize_input(img1_path, img2_path = None):
"""
verify, analyze and find functions build complex machine learning models in every call.
To avoid memory problems, you can pass image pairs as array.
This function manages this usage is enabled or not
E.g.
result = DeepFace.verify("img1.jpg", "img2.jpg")
results = DeepFace.verify([['img1.jpg', 'img2.jpg'], ['img1.jpg', 'img3.jpg']])
"""
if type(img1_path) == list:
bulkProcess = True
img_list = img1_path.copy()
else:
bulkProcess = False
if img2_path != None:
img_list = [[img1_path, img2_path]]
else:
img_list = [img1_path]
return img_list, bulkProcess
#---------------------------
#main

12
tests/unit_tests.py
View File

@ -14,7 +14,7 @@ dataset = [
]
print("-----------------------------------------")
"""
print("Face detectors test")
print("opencv detector")
@ -94,7 +94,7 @@ print("Race: ", demography["dominant_race"])
print("Emotion: ", demography["dominant_emotion"])
print("-----------------------------------------")
"""
print("Face recognition tests")
dataset = [
@ -118,13 +118,17 @@ metrics = ['cosine', 'euclidean', 'euclidean_l2']
passed_tests = 0; test_cases = 0
for model in models:
prebuilt_model = DeepFace.build_model(model)
print(model," is built")
for metric in metrics:
for instance in dataset:
img1 = instance[0]
img2 = instance[1]
result = instance[2]
resp_obj = DeepFace.verify(img1, img2, model_name = model, distance_metric = metric)
resp_obj = DeepFace.verify(img1, img2
, model_name = model, model = prebuilt_model
, distance_metric = metric)
prediction = resp_obj["verified"]
distance = round(resp_obj["distance"], 2)
@ -142,7 +146,7 @@ for model in models:
test_cases = test_cases + 1
print(img1.split("/")[-1], "and", img2.split("/")[-1],"are", classified_label, "as same person based on", model,"model and",metric,"distance. Distance:",distance,", Threshold:", required_threshold,"(",test_result_label,")")
print(img1.split("/")[-1], "-", img2.split("/")[-1], classified_label, "as same person based on", model,"and",metric,". Distance:",distance,", Threshold:", required_threshold,"(",test_result_label,")")
print("--------------------------")