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unit tests

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This commit is contained in:
Sefik Ilkin Serengil 2022-05-20 13:20:24 +01:00
parent eeaf1253da
commit 6438075233
1 changed files with 24 additions and 39 deletions
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63
tests/unit_tests.py
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@ -60,7 +60,7 @@ def test_cases():
for detector in detectors:
img = DeepFace.detectFace("dataset/img11.jpg", detector_backend = detector)
evaluate( img.shape[0] > 0 and img.shape[1] > 0 )
evaluate(img.shape[0] > 0 and img.shape[1] > 0)
print(detector," test is done")
print("-----------------------------------------")
@ -68,7 +68,7 @@ def test_cases():
img_path = "dataset/img1.jpg"
embedding = DeepFace.represent(img_path)
print("Function returned ", len(embedding), "dimensional vector")
evaluate( len(embedding) > 0 )
evaluate(len(embedding) > 0)
print("-----------------------------------------")
@ -86,7 +86,7 @@ def test_cases():
df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset")
print(df.head())
evaluate( df.shape[0] > 0 )
evaluate(df.shape[0] > 0)
print("-----------------------------------------")
@ -96,8 +96,8 @@ def test_cases():
demography = DeepFace.analyze(img)
print(demography)
evaluate( demography["age"] > 20 and demography["age"] < 40 )
evaluate( demography["gender"] == "Woman" )
evaluate(demography["age"] > 20 and demography["age"] < 40)
evaluate(demography["gender"] == "Woman")
print("-----------------------------------------")
@ -113,10 +113,10 @@ def test_cases():
print("Race: ", demography["dominant_race"])
print("Emotion: ", demography["dominant_emotion"])
evaluate( demography.get("age") is not None )
evaluate( demography.get("gender") is not None )
evaluate( demography.get("dominant_race") is not None )
evaluate( demography.get("dominant_emotion") is not None )
evaluate(demography.get("age") is not None)
evaluate(demography.get("gender") is not None)
evaluate(demography.get("dominant_race") is not None)
evaluate(demography.get("dominant_emotion") is not None)
print("-----------------------------------------")
@ -128,20 +128,16 @@ def test_cases():
print("Race: ", demography.get("dominant_race"))
print("Emotion: ", demography.get("dominant_emotion"))
evaluate( demography.get("age") is not None )
evaluate( demography.get("gender") is not None )
evaluate( demography.get("dominant_race") is None )
evaluate( demography.get("dominant_emotion") is None )
evaluate(demography.get("age") is not None)
evaluate(demography.get("gender") is not None)
evaluate(demography.get("dominant_race") is None)
evaluate(demography.get("dominant_emotion") is None)
print("-----------------------------------------")
print("Face recognition tests")
passed_tests = 0; test_cases = 0
print("Facial recognition tests")
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]
@ -150,43 +146,32 @@ def test_cases():
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)
threshold = resp_obj["threshold"]
evaluate( prediction == result )
passed = prediction == result
test_result_label = "failed"
if prediction == result:
passed_tests = passed_tests + 1
evaluate(passed)
if passed:
test_result_label = "passed"
else:
test_result_label = "failed"
if prediction == True:
classified_label = "verified"
else:
classified_label = "unverified"
test_cases = test_cases + 1
print(img1.split("/")[-1], "-", img2.split("/")[-1], classified_label, "as same person based on", model,"and",metric,". Distance:",distance,", Threshold:", threshold,"(",test_result_label,")")
print("--------------------------")
#-----------------------------------------
print("Passed unit tests: ",passed_tests," / ",test_cases)
min_score = 70
accuracy = 100 * passed_tests / test_cases
accuracy = round(accuracy, 2)
print("--------------------------")
#-----------------------------------
print("--------------------------")
print("Passing numpy array to analyze function")
@ -195,8 +180,8 @@ def test_cases():
resp_obj = DeepFace.analyze(img)
print(resp_obj)
evaluate( resp_obj["age"] > 20 and resp_obj["age"] < 40 )
evaluate( resp_obj["gender"] == "Woman" )
evaluate(resp_obj["age"] > 20 and resp_obj["age"] < 40)
evaluate(resp_obj["gender"] == "Woman")
print("--------------------------")
@ -208,7 +193,7 @@ def test_cases():
res = DeepFace.verify(img1, img2)
print(res)
evaluate( res["verified"] == True )
evaluate(res["verified"] == True)
print("--------------------------")
@ -220,7 +205,7 @@ def test_cases():
print(df.head())
evaluate( df.shape[0] > 0 )
evaluate(df.shape[0] > 0)
print("--------------------------")