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https://github.com/serengil/deepface.git
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[fix] use prediction shape to avoid confuse situation of predictions
This commit is contained in:
NatLee
parent
ad577b4206
commit
52a38ba21a
@ -1,5 +1,5 @@
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# built-in dependencies
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from typing import Any, Dict, List, Union
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from typing import Any, Dict, List, Union, Optional
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# 3rd party dependencies
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import numpy as np
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@ -117,8 +117,6 @@ def analyze(
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f"Invalid action passed ({repr(action)})). "
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"Valid actions are `emotion`, `age`, `gender`, `race`."
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)
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# ---------------------------------
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resp_objects = []
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img_objs = detection.extract_faces(
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img_path=img_path,
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@ -130,137 +128,105 @@ def analyze(
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anti_spoofing=anti_spoofing,
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)
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# Anti-spoofing check
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if anti_spoofing and any(img_obj.get("is_real", True) is False for img_obj in img_objs):
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raise ValueError("Spoof detected in the given image.")
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# Prepare the input for the model
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valid_faces = []
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face_regions = []
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face_confidences = []
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for img_obj in img_objs:
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# Extract the face content
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def preprocess_face(img_obj: Dict[str, Any]) -> Optional[np.ndarray]:
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"""
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Preprocess the face image for analysis.
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"""
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img_content = img_obj["face"]
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# Check if the face content is empty
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if img_content.shape[0] == 0 or img_content.shape[1] == 0:
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continue
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return None
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img_content = img_content[:, :, ::-1] # BGR to RGB
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return preprocessing.resize_image(img=img_content, target_size=(224, 224))
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# Convert the image to RGB format from BGR
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img_content = img_content[:, :, ::-1]
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# Resize the image to the target size for the model
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img_content = preprocessing.resize_image(img=img_content, target_size=(224, 224))
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valid_faces.append(img_content)
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face_regions.append(img_obj["facial_area"])
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face_confidences.append(img_obj["confidence"])
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# If no valid faces are found, return an empty list
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if not valid_faces:
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# Filter out empty faces
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face_data = [(preprocess_face(img_obj), img_obj["facial_area"], img_obj["confidence"])
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for img_obj in img_objs if img_obj["face"].size > 0]
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if not face_data:
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return []
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# Convert the list of valid faces to a numpy array
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# Unpack the face data
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valid_faces, face_regions, face_confidences = zip(*face_data)
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faces_array = np.array(valid_faces)
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# Preprocess the result to a list of dictionaries
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resp_objects = []
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# Initialize the results list with face regions and confidence scores
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results = [{"region": region, "face_confidence": conf}
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for region, conf in zip(face_regions, face_confidences)]
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# For each action, predict the corresponding attribute
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# Iterate over the actions and perform analysis
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pbar = tqdm(
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range(0, len(actions)),
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actions,
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desc="Finding actions",
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disable=silent if len(actions) > 1 else True,
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)
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for index in pbar:
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action = actions[index]
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for action in pbar:
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pbar.set_description(f"Action: {action}")
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resp_object = {}
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model = modeling.build_model(task="facial_attribute", model_name=action.capitalize())
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predictions = model.predict(faces_array)
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# If the model returns a single prediction, reshape it to match the number of faces
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# Use number of faces and number of predictions shape to determine the correct shape of predictions
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# For example, if there are 1 face to predict with Emotion model, reshape predictions to (1, 7)
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if faces_array.shape[0] == 1 and len(predictions.shape) == 1:
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# For models like `Emotion`, which return a single prediction for a single face
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predictions = predictions.reshape(1, -1)
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# Update the results with the predictions
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# ----------------------------------------
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# For emotion, calculate the percentage of each emotion and find the dominant emotion
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if action == "emotion":
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# Build the emotion model
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model = modeling.build_model(task="facial_attribute", model_name="Emotion")
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emotion_predictions = model.predict(faces_array)
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# Handle single vs multiple emotion predictions
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if len(emotion_predictions.shape) == 1:
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# Single face case - reshape predictions to 2D array for consistent handling
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emotion_predictions = emotion_predictions.reshape(1, -1)
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# Process predictions for each face
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for idx, predictions in enumerate(emotion_predictions):
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sum_of_predictions = predictions.sum()
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resp_object["emotion"] = {}
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# Calculate emotion probabilities and store in response
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for i, emotion_label in enumerate(Emotion.labels):
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emotion_probability = 100 * predictions[i] / sum_of_predictions
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resp_object["emotion"][emotion_label] = emotion_probability
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# Store dominant emotion
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resp_object["dominant_emotion"] = Emotion.labels[np.argmax(predictions)]
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emotion_results = [
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{
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"emotion": {
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label: 100 * pred[i] / pred.sum()
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for i, label in enumerate(Emotion.labels)
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},
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"dominant_emotion": Emotion.labels[np.argmax(pred)]
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}
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for pred in predictions
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]
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for result, emotion_result in zip(results, emotion_results):
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result.update(emotion_result)
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# ----------------------------------------
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# For age, find the dominant age category (0-100)
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elif action == "age":
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# Build the age model
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model = modeling.build_model(task="facial_attribute", model_name="Age")
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age_predictions = model.predict(faces_array)
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# Handle single vs multiple age predictions
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if faces_array.shape[0] == 1:
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# Single face case - reshape predictions to 2D array for consistent handling
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resp_object["age"] = int(np.argmax(age_predictions))
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else:
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# Multiple face case - iterate over each prediction
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for idx, age in enumerate(age_predictions):
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resp_object["age"] = int(age)
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age_results = [{"age": int(np.argmax(pred) if len(pred.shape) > 0 else pred)}
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for pred in predictions]
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for result, age_result in zip(results, age_results):
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result.update(age_result)
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# ----------------------------------------
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# For gender, calculate the percentage of each gender and find the dominant gender
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elif action == "gender":
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# Build the gender model
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model = modeling.build_model(task="facial_attribute", model_name="Gender")
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gender_predictions = model.predict(faces_array)
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# Handle single vs multiple gender predictions
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if len(gender_predictions.shape) == 1:
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# Single face case - reshape predictions to 2D array for consistent handling
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gender_predictions = gender_predictions.reshape(1, -1)
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# Process predictions for each face
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for idx, predictions in enumerate(gender_predictions):
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resp_object["gender"] = {}
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for i, gender_label in enumerate(Gender.labels):
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gender_prediction = 100 * predictions[i]
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resp_object["gender"][gender_label] = gender_prediction
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resp_object["dominant_gender"] = Gender.labels[np.argmax(predictions)]
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gender_results = [
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{
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"gender": {
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label: 100 * pred[i]
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for i, label in enumerate(Gender.labels)
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},
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"dominant_gender": Gender.labels[np.argmax(pred)]
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}
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for pred in predictions
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]
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for result, gender_result in zip(results, gender_results):
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result.update(gender_result)
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# ----------------------------------------
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# For race, calculate the percentage of each race and find the dominant race
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elif action == "race":
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# Build the race model
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model = modeling.build_model(task="facial_attribute", model_name="Race")
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race_predictions = model.predict(faces_array)
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race_results = [
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{
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"race": {
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label: 100 * pred[i] / pred.sum()
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for i, label in enumerate(Race.labels)
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},
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"dominant_race": Race.labels[np.argmax(pred)]
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}
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for pred in predictions
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]
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for result, race_result in zip(results, race_results):
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result.update(race_result)
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# Handle single vs multiple race predictions
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if len(race_predictions.shape) == 1:
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# Single face case - reshape predictions to 2D array for consistent handling
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race_predictions = race_predictions.reshape(1, -1)
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for idx, predictions in enumerate(race_predictions):
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sum_of_predictions = predictions.sum()
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resp_object["race"] = {}
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for i, race_label in enumerate(Race.labels):
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race_prediction = 100 * predictions[i] / sum_of_predictions
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resp_object["race"][race_label] = race_prediction
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resp_object["dominant_race"] = Race.labels[np.argmax(predictions)]
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# Add the response object to the list of response objects
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resp_objects.append(resp_object)
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# Add the face region and confidence to the response objects
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for idx, resp_obj in enumerate(resp_objects):
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resp_obj["region"] = face_regions[idx]
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resp_obj["face_confidence"] = face_confidences[idx]
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return resp_objects
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return results
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