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Sefik Ilkin Serengil 2022-06-22 14:11:35 +01:00
parent 48d1c90589
commit 5d767e2d49
3 changed files with 28 additions and 55 deletions
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26
deepface/DeepFace.py
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@ -418,25 +418,21 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
resp_obj["age"] = int(apparent_age) #int cast is for the exception - object of type 'float32' is not JSON serializable
elif action == 'gender':
try:
if img_224 is None:
img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
gender_predictions = models['gender'].predict(img_224)[0,:]
if img_224 is None:
img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True)
gender_labels = ["Woman", "Man"]
resp_obj["gender"] = {}
gender_predictions = models['gender'].predict(img_224)[0,:]
for i in range(0, len(gender_labels)):
gender_label = gender_labels[i]
gender_prediction = 100 * gender_predictions[i]
resp_obj["gender"][gender_label] = gender_prediction
gender_labels = ["Woman", "Man"]
resp_obj["gender"] = {}
for i, gender_label in enumerate(gender_labels):
gender_prediction = 100 * gender_predictions[i]
resp_obj["gender"][gender_label] = gender_prediction
resp_obj["dominant_gender"] = gender_labels[np.argmax(gender_predictions)]
resp_obj["dominant_gender"] = gender_labels[np.argmax(gender_predictions)]
except Exception as e:
resp_obj["dominant_gender"] = None
resp_obj["gender"] = None
resp_obj["error"] = e
elif action == 'race':
if img_224 is None:
img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True) #just emotion model expects grayscale images

35
tests/test_nonbinary_gender.py
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@ -1,35 +0,0 @@
from deepface import DeepFace
dataset = [
'dataset/img1.jpg',
'dataset/img5.jpg',
'dataset/img6.jpg',
'dataset/img7.jpg',
'dataset/img9.jpg',
'dataset/img11.jpg',
'dataset/img11.jpg',
]
def test_gender_prediction():
detectors = ['opencv', 'ssd', 'retinaface', 'mtcnn'] # dlib not tested
for detector in detectors:
test_gender_prediction_with_detector(detector)
def test_gender_prediction_with_detector(detector):
results = DeepFace.analyze(dataset, actions=('gender',), detector_backend=detector, prog_bar=False,
enforce_detection=False)
for result in results:
assert 'gender' in result.keys()
assert 'dominant_gender' in result.keys() and result["dominant_gender"] in ["Man", "Woman"]
if result["dominant_gender"] == "Man":
assert result["gender"]["Man"] > result["gender"]["Woman"]
else:
assert result["gender"]["Man"] < result["gender"]["Woman"]
print(f'detector {detector} passed')
return True
if __name__ == "__main__":
test_gender_prediction()

22
tests/unit_tests.py
View File

@ -3,7 +3,6 @@ import os
import tensorflow as tf
import cv2
from deepface import DeepFace
from tests.test_nonbinary_gender import test_gender_prediction, test_gender_prediction_with_detector
print("-----------------------------------------")
@ -207,14 +206,27 @@ def test_cases():
print("--------------------------")
print("non-binary gender tests")
def run_gender_prediction_test():
for detector in detectors:
evaluate(test_gender_prediction_with_detector(detector))
#interface validation - no need to call evaluate here
for img1_path, img2_path, verified in dataset:
for detector in detectors:
result = DeepFace.analyze(img1_path, actions=('gender',), detector_backend=detector, enforce_detection=False)
print(result)
assert 'gender' in result.keys()
assert 'dominant_gender' in result.keys() and result["dominant_gender"] in ["Man", "Woman"]
if result["dominant_gender"] == "Man":
assert result["gender"]["Man"] > result["gender"]["Woman"]
else:
assert result["gender"]["Man"] < result["gender"]["Woman"]
# ---------------------------------------------
test_cases()
run_gender_prediction_test()
print("num of test cases run: " + str(num_cases))
print("succeeded test cases: " + str(succeed_cases))