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Merge pull request #1032 from AndreaLanfranchi/al20240222-find-simpler
Simplify find data initialization
This commit is contained in:
Sefik Ilkin Serengil
GitHub
commit
af73d3dfe4
@ -100,6 +100,7 @@ def find(
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file_name = f"representations_{model_name}.pkl"
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file_name = file_name.replace("-", "_").lower()
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datastore_path = os.path.join(db_path, file_name)
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representations = []
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df_cols = [
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"identity",
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@ -110,77 +111,46 @@ def find(
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"target_h",
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]
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if os.path.exists(datastore_path):
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with open(datastore_path, "rb") as f:
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representations = pickle.load(f)
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# Ensure the proper pickle file exists
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if not os.path.exists(datastore_path):
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with open(datastore_path, "wb") as f:
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pickle.dump([], f)
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if len(representations) > 0 and len(representations[0]) != len(df_cols):
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raise ValueError(
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f"Seems existing {datastore_path} is out-of-the-date."
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"Please delete it and re-run."
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)
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# Load the representations from the pickle file
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with open(datastore_path, "rb") as f:
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representations = pickle.load(f)
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alpha_employees = __list_images(path=db_path)
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beta_employees = [representation[0] for representation in representations]
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newbies = list(set(alpha_employees) - set(beta_employees))
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oldies = list(set(beta_employees) - set(alpha_employees))
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if newbies:
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logger.warn(
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f"Items {newbies} were added into {db_path}"
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f" just after data source {datastore_path} created!"
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)
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newbies_representations = __find_bulk_embeddings(
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employees=newbies,
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model_name=model_name,
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target_size=target_size,
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detector_backend=detector_backend,
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enforce_detection=enforce_detection,
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align=align,
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normalization=normalization,
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silent=silent,
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)
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representations = representations + newbies_representations
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if oldies:
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logger.warn(
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f"Items {oldies} were dropped from {db_path}"
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f" just after data source {datastore_path} created!"
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)
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representations = [rep for rep in representations if rep[0] not in oldies]
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if newbies or oldies:
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if len(representations) == 0:
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raise ValueError(f"There is no image in {db_path} anymore!")
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# save new representations
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with open(datastore_path, "wb") as f:
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pickle.dump(representations, f)
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if not silent:
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logger.info(
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f"{len(newbies)} new representations are just added"
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f" whereas {len(oldies)} represented one(s) are just dropped"
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f" in {os.path.join(db_path,file_name)} file."
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)
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if not silent:
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logger.info(f"There are {len(representations)} representations found in {file_name}")
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else: # create representation.pkl from scratch
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employees = __list_images(path=db_path)
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if len(employees) == 0:
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# Check if the representations are out-of-date
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if len(representations) > 0:
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if len(representations[0]) != len(df_cols):
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raise ValueError(
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f"Could not find any valid image in {db_path} folder!"
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"Valid images are .jpg, .jpeg or .png files.",
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f"Seems existing {datastore_path} is out-of-the-date."
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"Please delete it and re-run."
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)
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pickled_images = [representation[0] for representation in representations]
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else:
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pickled_images = []
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# ------------------------
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# find representations for db images
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representations = __find_bulk_embeddings(
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employees=employees,
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# Get the list of images on storage
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storage_images = __list_images(path=db_path)
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# Enforce data consistency amongst on disk images and pickle file
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must_save_pickle = False
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new_images = list(set(storage_images) - set(pickled_images)) # images added to storage
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old_images = list(set(pickled_images) - set(storage_images)) # images removed from storage
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if not silent and (len(new_images) > 0 or len(old_images) > 0):
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logger.info(f"Found {len(new_images)} new images and {len(old_images)} removed images")
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# remove old images first
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if len(old_images)>0:
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representations = [rep for rep in representations if rep[0] not in old_images]
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must_save_pickle = True
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# find representations for new images
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if len(new_images)>0:
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representations += __find_bulk_embeddings(
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employees=new_images,
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model_name=model_name,
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target_size=target_size,
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detector_backend=detector_backend,
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@ -188,15 +158,21 @@ def find(
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align=align,
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normalization=normalization,
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silent=silent,
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)
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# -------------------------------
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) # add new images
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must_save_pickle = True
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if must_save_pickle:
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with open(datastore_path, "wb") as f:
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pickle.dump(representations, f)
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if not silent:
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logger.info(f"Representations stored in {datastore_path} file.")
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logger.info(f"There are now {len(representations)} representations in {file_name}")
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# Should we have no representations bailout
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if len(representations) == 0:
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if not silent:
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toc = time.time()
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logger.info(f"find function duration {toc - tic} seconds")
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return []
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# ----------------------------
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# now, we got representations for facial database
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@ -287,10 +263,9 @@ def find(
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# -----------------------------------
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toc = time.time()
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if not silent:
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logger.info(f"find function lasts {toc - tic} seconds")
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toc = time.time()
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logger.info(f"find function duration {toc - tic} seconds")
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return resp_obj
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@ -1,3 +1,4 @@
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import os
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import cv2
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import pandas as pd
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from deepface import DeepFace
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@ -10,7 +11,7 @@ threshold = verification.find_threshold(model_name="VGG-Face", distance_metric="
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def test_find_with_exact_path():
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img_path = "dataset/img1.jpg"
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img_path = os.path.join("dataset","img1.jpg")
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dfs = DeepFace.find(img_path=img_path, db_path="dataset", silent=True)
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assert len(dfs) > 0
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for df in dfs:
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@ -30,7 +31,7 @@ def test_find_with_exact_path():
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def test_find_with_array_input():
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img_path = "dataset/img1.jpg"
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img_path = os.path.join("dataset","img1.jpg")
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img1 = cv2.imread(img_path)
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dfs = DeepFace.find(img1, db_path="dataset", silent=True)
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assert len(dfs) > 0
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@ -52,7 +53,7 @@ def test_find_with_array_input():
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def test_find_with_extracted_faces():
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img_path = "dataset/img1.jpg"
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img_path = os.path.join("dataset","img1.jpg")
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face_objs = DeepFace.extract_faces(img_path)
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img = face_objs[0]["face"]
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dfs = DeepFace.find(img, db_path="dataset", detector_backend="skip", silent=True)
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