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new test scenario added

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This commit is contained in:
Şefik Serangil 2020-09-03 09:30:21 +03:00
parent 130733f63f
commit 1a77cb039f
1 changed files with 43 additions and 17 deletions
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62
tests/unit_tests.py
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@ -3,6 +3,7 @@ import json
import os import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
#----------------------------------------- #-----------------------------------------
dataset = [ dataset = [
@ -21,23 +22,6 @@ print(df.head())
print("-----------------------------------------") print("-----------------------------------------")
print("Ensemble for find function")
df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset", model_name = "Ensemble")
print(df.head())
print("-----------------------------------------")
print("Ensemble for verify function")
resp_obj = DeepFace.verify(dataset, model_name = "Ensemble")
for i in range(0, len(dataset)):
item = resp_obj['pair_%s' % (i+1)]
verified = item["verified"]
score = item["score"]
print(verified)
print("-----------------------------------------")
print("Bulk face recognition tests") print("Bulk face recognition tests")
resp_obj = DeepFace.verify(dataset) resp_obj = DeepFace.verify(dataset)
@ -158,6 +142,7 @@ from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
#----------------------------------- #-----------------------------------
print("--------------------------") print("--------------------------")
print("Verify function with passing pre-trained model") print("Verify function with passing pre-trained model")
vggface_model = VGGFace.loadModel() vggface_model = VGGFace.loadModel()
@ -166,6 +151,7 @@ print(resp_obj)
#----------------------------------- #-----------------------------------
print("--------------------------") print("--------------------------")
print("Analyze function with passing pre-trained model") print("Analyze function with passing pre-trained model")
from deepface.extendedmodels import Age, Gender, Race, Emotion from deepface.extendedmodels import Age, Gender, Race, Emotion
@ -185,5 +171,45 @@ resp_obj = DeepFace.analyze("dataset/img1.jpg", models=facial_attribute_models)
print(resp_obj) print(resp_obj)
#----------------------------------- #-----------------------------------
print("--------------------------")
print("Ensemble for find function")
df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset", model_name = "Ensemble")
print(df.head())
#-----------------------------------
print("--------------------------")
print("Ensemble for verify function")
resp_obj = DeepFace.verify(dataset, model_name = "Ensemble")
for i in range(0, len(dataset)):
item = resp_obj['pair_%s' % (i+1)]
verified = item["verified"]
score = item["score"]
print(verified)
#-----------------------------------
print("--------------------------")
print("Pre-trained ensemled method")
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
model = {}
model["VGG-Face"] = VGGFace.loadModel()
print("VGG loaded")
model["Facenet"] = Facenet.loadModel()
print("Facenet loaded")
model["OpenFace"] = OpenFace.loadModel()
print("OpenFace loaded")
model["DeepFace"] = FbDeepFace.loadModel()
print("DeepFace loaded")
df = DeepFace.find("dataset/img1.jpg", db_path = "dataset", model_name = 'Ensemble', model=model, enforce_detection=False)
print(df)
#-----------------------------------
print("--------------------------") print("--------------------------")