90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
from deepface import DeepFace
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from deepface.commons import functions, distance as dst
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import pandas as pd
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import time
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def face_find(
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img_path,
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global_df,
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model_name="Facenet512",
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distance_metric="cosine",
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enforce_detection=True,
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detector_backend="opencv",
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align=True,
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normalization="base",
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silent=False,
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):
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tic = time.time()
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target_size = functions.find_target_size(model_name=model_name)
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# now, we got representations for facial database
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df = global_df
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# img path might have more than once face
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target_objs = functions.extract_faces(
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img=img_path,
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target_size=target_size,
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detector_backend=detector_backend,
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grayscale=False,
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enforce_detection=enforce_detection,
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align=align,
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)
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resp_obj = []
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for target_img, target_region, _ in target_objs:
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target_embedding_obj = DeepFace.represent(
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img_path=target_img,
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model_name=model_name,
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enforce_detection=enforce_detection,
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detector_backend="skip",
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align=align,
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normalization=normalization,
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)
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target_representation = target_embedding_obj[0]["embedding"]
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result_df = df.copy() # df will be filtered in each img
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result_df["source_x"] = target_region["x"]
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result_df["source_y"] = target_region["y"]
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result_df["source_w"] = target_region["w"]
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result_df["source_h"] = target_region["h"]
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distances = []
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for index, instance in df.iterrows():
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source_representation = instance[f"{model_name}_representation"]
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if distance_metric == "cosine":
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distance = dst.findCosineDistance(source_representation, target_representation)
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elif distance_metric == "euclidean":
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distance = dst.findEuclideanDistance(source_representation, target_representation)
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elif distance_metric == "euclidean_l2":
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distance = dst.findEuclideanDistance(
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dst.l2_normalize(source_representation),
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dst.l2_normalize(target_representation),
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)
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else:
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raise ValueError(f"invalid distance metric passes - {distance_metric}")
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distances.append(distance)
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# ---------------------------
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result_df[f"{model_name}_{distance_metric}"] = distances
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threshold = dst.findThreshold(model_name, distance_metric)
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result_df = result_df.drop(columns=[f"{model_name}_representation"])
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result_df = result_df[result_df[f"{model_name}_{distance_metric}"] <= threshold]
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result_df = result_df.sort_values(
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by=[f"{model_name}_{distance_metric}"], ascending=True
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).reset_index(drop=True)
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resp_obj.append(result_df)
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# -----------------------------------
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toc = time.time()
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if not silent:
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print("find function lasts ", toc - tic, " seconds")
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return resp_obj |