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[update] refactor response object creation in analyze function

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NatLee 2025-01-07 05:33:47 +08:00
parent c44af00269
commit ad577b4206
1 changed files with 17 additions and 15 deletions
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32
deepface/modules/demography.py
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@ -162,10 +162,8 @@ def analyze(
# Convert the list of valid faces to a numpy array
faces_array = np.array(valid_faces)
# Create placeholder response objects for each face
for _ in range(len(valid_faces)):
resp_objects.append({})
# Preprocess the result to a list of dictionaries
resp_objects = []
# For each action, predict the corresponding attribute
pbar = tqdm(
@ -177,6 +175,7 @@ def analyze(
for index in pbar:
action = actions[index]
pbar.set_description(f"Action: {action}")
resp_object = {}
if action == "emotion":
# Build the emotion model
@ -191,15 +190,15 @@ def analyze(
# Process predictions for each face
for idx, predictions in enumerate(emotion_predictions):
sum_of_predictions = predictions.sum()
resp_objects[idx]["emotion"] = {}
resp_object["emotion"] = {}
# Calculate emotion probabilities and store in response
for i, emotion_label in enumerate(Emotion.labels):
emotion_probability = 100 * predictions[i] / sum_of_predictions
resp_objects[idx]["emotion"][emotion_label] = emotion_probability
resp_object["emotion"][emotion_label] = emotion_probability
# Store dominant emotion
resp_objects[idx]["dominant_emotion"] = Emotion.labels[np.argmax(predictions)]
resp_object["dominant_emotion"] = Emotion.labels[np.argmax(predictions)]
elif action == "age":
# Build the age model
@ -209,11 +208,11 @@ def analyze(
# Handle single vs multiple age predictions
if faces_array.shape[0] == 1:
# Single face case - reshape predictions to 2D array for consistent handling
resp_objects[idx]["age"] = int(np.argmax(age_predictions))
resp_object["age"] = int(np.argmax(age_predictions))
else:
# Multiple face case - iterate over each prediction
for idx, age in enumerate(age_predictions):
resp_objects[idx]["age"] = int(age)
resp_object["age"] = int(age)
elif action == "gender":
@ -228,13 +227,13 @@ def analyze(
# Process predictions for each face
for idx, predictions in enumerate(gender_predictions):
resp_objects[idx]["gender"] = {}
resp_object["gender"] = {}
for i, gender_label in enumerate(Gender.labels):
gender_prediction = 100 * predictions[i]
resp_objects[idx]["gender"][gender_label] = gender_prediction
resp_object["gender"][gender_label] = gender_prediction
resp_objects[idx]["dominant_gender"] = Gender.labels[np.argmax(predictions)]
resp_object["dominant_gender"] = Gender.labels[np.argmax(predictions)]
elif action == "race":
# Build the race model
@ -248,13 +247,16 @@ def analyze(
for idx, predictions in enumerate(race_predictions):
sum_of_predictions = predictions.sum()
resp_objects[idx]["race"] = {}
resp_object["race"] = {}
for i, race_label in enumerate(Race.labels):
race_prediction = 100 * predictions[i] / sum_of_predictions
resp_objects[idx]["race"][race_label] = race_prediction
resp_object["race"][race_label] = race_prediction
resp_objects[idx]["dominant_race"] = Race.labels[np.argmax(predictions)]
resp_object["dominant_race"] = Race.labels[np.argmax(predictions)]
# Add the response object to the list of response objects
resp_objects.append(resp_object)
# Add the face region and confidence to the response objects
for idx, resp_obj in enumerate(resp_objects):