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Taking gender_prediction directly

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sasael 2022-06-22 15:36:20 +03:00
parent 2135941912
commit 3f2fa48bee
1 changed files with 2 additions and 9 deletions
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deepface/DeepFace.py
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@ -424,13 +424,12 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
gender_predictions = models['gender'].predict(img_224)[0,:]
sum_of_predictions = gender_predictions.sum()
gender_labels = ["Woman", "Man"]
resp_obj["gender"] = {}
for i in range(0, len(gender_labels)):
gender_label = gender_labels[i]
gender_prediction = 100 * gender_predictions[i] / sum_of_predictions
gender_prediction = 100 * gender_predictions[i]
resp_obj["gender"][gender_label] = gender_prediction
resp_obj["dominant_gender"] = gender_labels[np.argmax(gender_predictions)]
@ -471,13 +470,7 @@ def analyze(img_path, actions = ('emotion', 'age', 'gender', 'race') , models =
if bulkProcess == True:
return resp_objects
# resp_obj = {}
#
# for i in range(0, len(resp_objects)):
# resp_item = resp_objects[i]
# resp_obj["instance_%d" % (i+1)] = resp_item
#
# return resp_obj
def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True, prog_bar = True, normalization = 'base', silent=False):