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REVERT demography.py
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h-alice
parent
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commit
6a7bbdb926
@ -1,5 +1,5 @@
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# built-in dependencies
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from typing import Any, Dict, List, Union, Optional
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from typing import Any, Dict, List, Union
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# 3rd party dependencies
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import numpy as np
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@ -9,6 +9,7 @@ from tqdm import tqdm
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from deepface.modules import modeling, detection, preprocessing
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from deepface.models.demography import Gender, Race, Emotion
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def analyze(
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img_path: Union[str, np.ndarray],
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actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
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@ -116,6 +117,8 @@ 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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@ -127,105 +130,84 @@ def analyze(
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anti_spoofing=anti_spoofing,
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)
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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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for img_obj in img_objs:
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if anti_spoofing is True and img_obj.get("is_real", True) is False:
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raise ValueError("Spoof detected in the given image.")
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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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img_region = img_obj["facial_area"]
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img_confidence = img_obj["confidence"]
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if img_content.shape[0] == 0 or img_content.shape[1] == 0:
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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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continue
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# Filter out empty faces
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face_data = [
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(
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preprocess_face(img_obj),
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img_obj["facial_area"],
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img_obj["confidence"]
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# rgb to bgr
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img_content = img_content[:, :, ::-1]
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# resize input image
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img_content = preprocessing.resize_image(img=img_content, target_size=(224, 224))
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obj = {}
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# facial attribute analysis
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pbar = tqdm(
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range(0, len(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 img_obj in img_objs if img_obj["face"].size > 0
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]
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for index in pbar:
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action = actions[index]
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pbar.set_description(f"Action: {action}")
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if not face_data:
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return []
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if action == "emotion":
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emotion_predictions = modeling.build_model(
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task="facial_attribute", model_name="Emotion"
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).predict(img_content)
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sum_of_predictions = emotion_predictions.sum()
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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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# 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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# Iterate over the actions and perform analysis
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pbar = tqdm(
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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 action in pbar:
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pbar.set_description(f"Action: {action}")
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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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# Determine the correct shape of predictions by using number of faces and predictions shape.
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# Example: For 1 face with Emotion model, predictions will be reshaped 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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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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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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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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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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return results
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obj["emotion"] = {}
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for i, emotion_label in enumerate(Emotion.labels):
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emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions
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obj["emotion"][emotion_label] = emotion_prediction
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obj["dominant_emotion"] = Emotion.labels[np.argmax(emotion_predictions)]
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elif action == "age":
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apparent_age = modeling.build_model(
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task="facial_attribute", model_name="Age"
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).predict(img_content)
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# int cast is for exception - object of type 'float32' is not JSON serializable
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print(apparent_age.shape)
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obj["age"] = int(apparent_age)
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elif action == "gender":
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gender_predictions = modeling.build_model(
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task="facial_attribute", model_name="Gender"
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).predict(img_content)
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obj["gender"] = {}
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for i, gender_label in enumerate(Gender.labels):
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gender_prediction = 100 * gender_predictions[i]
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obj["gender"][gender_label] = gender_prediction
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obj["dominant_gender"] = Gender.labels[np.argmax(gender_predictions)]
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elif action == "race":
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race_predictions = modeling.build_model(
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task="facial_attribute", model_name="Race"
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).predict(img_content)
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sum_of_predictions = race_predictions.sum()
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obj["race"] = {}
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for i, race_label in enumerate(Race.labels):
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race_prediction = 100 * race_predictions[i] / sum_of_predictions
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obj["race"][race_label] = race_prediction
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obj["dominant_race"] = Race.labels[np.argmax(race_predictions)]
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# -----------------------------
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# mention facial areas
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obj["region"] = img_region
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# include image confidence
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obj["face_confidence"] = img_confidence
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resp_objects.append(obj)
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return resp_objects
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