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ensemble verify becomes cleaner
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serengil
parent
45f17417af
commit
9f436f39a7
@ -50,34 +50,30 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
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#--------------------------------
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if model_name == 'Ensemble':
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return Boosting.verify(model = model, img_list = img_list, bulkProcess = bulkProcess, enforce_detection = enforce_detection, detector_backend = detector_backend)
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#ensemble learning block end
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model_names = ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
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metrics = ["cosine", "euclidean", "euclidean_l2"]
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else:
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model_names = []; metrics = []
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model_names.append(model_name)
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metrics.append(distance_metric)
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#--------------------------------
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#ensemble learning disabled
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if model == None:
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model = build_model(model_name)
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#------------------------------
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#face recognition models have different size of inputs
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#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
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input_shape = model.layers[0].input_shape
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if type(input_shape) == list:
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input_shape = input_shape[0][1:3]
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if model == None:
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if model_name == 'Ensemble':
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models = Boosting.loadModel()
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else:
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model = build_model(model_name)
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models = {}
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models[model_name] = model
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else:
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input_shape = input_shape[1:3]
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if model_name == 'Ensemble':
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Boosting.validate_model(model)
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else:
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models = {}
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models[model_name] = model
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input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
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#------------------------------
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#tuned thresholds for model and metric pair
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threshold = functions.findThreshold(model_name, distance_metric)
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#------------------------------
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#calling deepface in a for loop causes lots of progress bars. this prevents it.
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@ -85,63 +81,113 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
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pbar = tqdm(range(0,len(img_list)), desc='Verification', disable = disable_option)
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#for instance in img_list:
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for index in pbar:
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instance = img_list[index]
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if type(instance) == list and len(instance) >= 2:
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img1_path = instance[0]
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img2_path = instance[1]
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#----------------------
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#crop and align faces
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img1 = functions.preprocess_face(img=img1_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
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img2 = functions.preprocess_face(img=img2_path, target_size=(input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
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#----------------------
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#find embeddings
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img1_representation = model.predict(img1)[0,:]
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img2_representation = model.predict(img2)[0,:]
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#----------------------
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#find distances between embeddings
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if distance_metric == 'cosine':
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distance = dst.findCosineDistance(img1_representation, img2_representation)
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elif distance_metric == 'euclidean':
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distance = dst.findEuclideanDistance(img1_representation, img2_representation)
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elif distance_metric == 'euclidean_l2':
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distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
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else:
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raise ValueError("Invalid distance_metric passed - ", distance_metric)
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#----------------------
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#decision
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if distance <= threshold:
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identified = True
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else:
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identified = False
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#----------------------
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#response object
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img1_path = instance[0]; img2_path = instance[1]
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resp_obj = {
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"verified": identified
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, "distance": distance
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, "max_threshold_to_verify": threshold
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, "model": model_name
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, "similarity_metric": distance_metric
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ensemble_features = []
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for i in model_names:
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custom_model = models[i]
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}
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#decide input shape
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input_shape = functions.find_input_shape(custom_model)
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input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
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#----------------------
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#detect and align faces
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img1 = functions.preprocess_face(img=img1_path
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, target_size=(input_shape_y, input_shape_x)
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, enforce_detection = enforce_detection
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, detector_backend = detector_backend)
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img2 = functions.preprocess_face(img=img2_path
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, target_size=(input_shape_y, input_shape_x)
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, enforce_detection = enforce_detection
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, detector_backend = detector_backend)
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#----------------------
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#find embeddings
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img1_representation = custom_model.predict(img1)[0,:]
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img2_representation = custom_model.predict(img2)[0,:]
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#----------------------
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#find distances between embeddings
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for j in metrics:
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if j == 'cosine':
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distance = dst.findCosineDistance(img1_representation, img2_representation)
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elif j == 'euclidean':
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distance = dst.findEuclideanDistance(img1_representation, img2_representation)
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elif j == 'euclidean_l2':
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distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
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else:
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raise ValueError("Invalid distance_metric passed - ", distance_metric)
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#----------------------
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#decision
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if model_name != 'Ensemble':
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threshold = functions.findThreshold(i, j)
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if distance <= threshold:
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identified = True
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else:
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identified = False
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resp_obj = {
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"verified": identified
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, "distance": distance
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, "max_threshold_to_verify": threshold
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, "model": model_name
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, "similarity_metric": distance_metric
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}
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if bulkProcess == True:
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resp_objects.append(resp_obj)
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else:
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return resp_obj
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else: #Ensemble
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#this returns same with OpenFace - euclidean_l2
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if i == 'OpenFace' and j == 'euclidean':
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continue
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else:
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ensemble_features.append(distance)
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#----------------------
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if bulkProcess == True:
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resp_objects.append(resp_obj)
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else:
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return resp_obj
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if model_name == 'Ensemble':
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boosted_tree = Boosting.build_gbm()
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prediction = boosted_tree.predict(np.expand_dims(np.array(ensemble_features), axis=0))[0]
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verified = np.argmax(prediction) == 1
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score = prediction[np.argmax(prediction)]
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resp_obj = {
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"verified": verified
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, "score": score
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, "distance": ensemble_features
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, "model": ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
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, "similarity_metric": ["cosine", "euclidean", "euclidean_l2"]
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}
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if bulkProcess == True:
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resp_objects.append(resp_obj)
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else:
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return resp_obj
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#----------------------
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else:
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@ -313,16 +359,8 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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#-------------------------------
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#model metric pairs for ensemble
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model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
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metric_names = ['cosine', 'euclidean', 'euclidean_l2']
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#-------------------------------
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if os.path.isdir(db_path) == True:
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#---------------------------------------
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if model == None:
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if model_name == 'Ensemble':
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@ -331,14 +369,28 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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else: #model is not ensemble
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model = build_model(model_name)
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models = {}
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models[model_name] = model
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else: #model != None
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print("Already built model is passed")
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if model_name == 'Ensemble':
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Boosting.validate_model(model)
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models = model.copy()
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else:
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models = {}
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models[model_name] = model
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#---------------------------------------
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if model_name == 'Ensemble':
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model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
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metric_names = ['cosine', 'euclidean', 'euclidean_l2']
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elif model_name != 'Ensemble':
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model_names = []; metric_names = []
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model_names.append(model_name)
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metric_names.append(distance_metric)
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#---------------------------------------
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@ -354,7 +406,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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print("There are ", len(representations)," representations found in ",file_name)
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else:
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else: #create representation.pkl from scratch
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employees = []
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for r, d, f in os.walk(db_path): # r=root, d=directories, f = files
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@ -376,49 +428,28 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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#for employee in employees:
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for index in pbar:
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employee = employees[index]
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instance = []
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instance.append(employee)
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if model_name != 'Ensemble':
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for j in model_names:
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custom_model = models[j]
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#input_shape = model.layers[0].input_shape[1:3] #my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
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input_shape = model.layers[0].input_shape
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if type(input_shape) == list:
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input_shape = input_shape[0][1:3]
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else:
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input_shape = input_shape[1:3]
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#----------------------------------
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#decide input shape
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input_shape = functions.find_input_shape(custom_model)
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input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
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img = functions.preprocess_face(img = employee, target_size = (input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
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representation = model.predict(img)[0,:]
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#----------------------------------
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instance = []
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instance.append(employee)
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img = functions.preprocess_face(img = employee
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, target_size = (input_shape_y, input_shape_x)
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, enforce_detection = enforce_detection
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, detector_backend = detector_backend)
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representation = custom_model.predict(img)[0,:]
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instance.append(representation)
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else: #ensemble learning
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instance = []
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instance.append(employee)
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for j in model_names:
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ensemble_model = models[j]
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#input_shape = model.layers[0].input_shape[1:3] #my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
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input_shape = ensemble_model.layers[0].input_shape
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if type(input_shape) == list:
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input_shape = input_shape[0][1:3]
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else:
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input_shape = input_shape[1:3]
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input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
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img = functions.preprocess_face(img = employee, target_size = (input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
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representation = ensemble_model.predict(img)[0,:]
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instance.append(representation)
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#-------------------------------
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@ -431,14 +462,18 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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print("Representations stored in ",db_path,"/",file_name," file. Please delete this file when you add new identities in your database.")
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#----------------------------
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#we got representations for database
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#now, we got representations for facial database
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if model_name != 'Ensemble':
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df = pd.DataFrame(representations, columns = ["identity", "representation"])
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df = pd.DataFrame(representations, columns = ["identity", "%s_representation" % (model_name)])
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else: #ensemble learning
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df = pd.DataFrame(representations, columns = ["identity", "VGG-Face_representation", "Facenet_representation", "OpenFace_representation", "DeepFace_representation"])
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df_base = df.copy()
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columns = ['identity']
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[columns.append('%s_representation' % i) for i in model_names]
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df = pd.DataFrame(representations, columns = columns)
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df_base = df.copy() #df will be filtered in each img. we will restore it for the next item.
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resp_obj = []
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@ -448,61 +483,74 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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#find representation for passed image
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if model_name == 'Ensemble':
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for j in model_names:
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ensemble_model = models[j]
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for j in model_names:
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custom_model = models[j]
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#--------------------------------
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#decide input shape
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input_shape = functions.find_input_shape(custom_model)
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#--------------------------------
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img = functions.preprocess_face(img = img_path, target_size = input_shape
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, enforce_detection = enforce_detection
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, detector_backend = detector_backend)
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#input_shape = ensemble_model.layers[0].input_shape[1:3] #my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
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input_shape = ensemble_model.layers[0].input_shape
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if type(input_shape) == list:
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input_shape = input_shape[0][1:3]
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else:
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input_shape = input_shape[1:3]
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img = functions.preprocess_face(img = img_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)
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target_representation = ensemble_model.predict(img)[0,:]
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for k in metric_names:
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distances = []
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for index, instance in df.iterrows():
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source_representation = instance["%s_representation" % (j)]
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if k == 'cosine':
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distance = dst.findCosineDistance(source_representation, target_representation)
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elif k == 'euclidean':
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distance = dst.findEuclideanDistance(source_representation, target_representation)
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elif k == 'euclidean_l2':
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distance = dst.findEuclideanDistance(dst.l2_normalize(source_representation), dst.l2_normalize(target_representation))
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distances.append(distance)
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target_representation = custom_model.predict(img)[0,:]
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for k in metric_names:
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distances = []
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for index, instance in df.iterrows():
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source_representation = instance["%s_representation" % (j)]
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if j == 'OpenFace' and k == 'euclidean':
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continue
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else:
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df["%s_%s" % (j, k)] = distances
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#----------------------------------
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if k == 'cosine':
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distance = dst.findCosineDistance(source_representation, target_representation)
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elif k == 'euclidean':
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distance = dst.findEuclideanDistance(source_representation, target_representation)
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elif k == 'euclidean_l2':
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distance = dst.findEuclideanDistance(dst.l2_normalize(source_representation), dst.l2_normalize(target_representation))
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distances.append(distance)
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#---------------------------
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if model_name == 'Ensemble' and j == 'OpenFace' and k == 'euclidean':
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continue
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else:
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df["%s_%s" % (j, k)] = distances
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if model_name != 'Ensemble':
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threshold = functions.findThreshold(j, k)
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df = df.drop(columns = ["%s_representation" % (j)])
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df = df[df["%s_%s" % (j, k)] <= threshold]
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df = df.sort_values(by = ["%s_%s" % (j, k)], ascending=True).reset_index(drop=True)
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resp_obj.append(df)
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df = df_base.copy() #restore df for the next iteration
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#----------------------------------
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if model_name == 'Ensemble':
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feature_names = []
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for j in model_names:
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for k in metric_names:
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if j == 'OpenFace' and k == 'euclidean':
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if model_name == 'Ensemble' and j == 'OpenFace' and k == 'euclidean':
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continue
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else:
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feature = '%s_%s' % (j, k)
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feature_names.append(feature)
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#print(df[feature_names].head())
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#print(df.head())
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x = df[feature_names].values
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#--------------------------------------
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boosted_tree = Boosting.build_gbm()
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#----------------------------------
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#lightgbm model
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deepface_ensemble = Boosting.build_gbm()
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y = deepface_ensemble.predict(x)
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y = boosted_tree.predict(x)
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verified_labels = []; scores = []
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for i in y:
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@ -522,51 +570,8 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
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resp_obj.append(df)
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df = df_base.copy() #restore df for the next iteration
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#----------------------------------
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if model_name != 'Ensemble':
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#input_shape = model.layers[0].input_shape[1:3] #my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
|
||||
|
||||
input_shape = model.layers[0].input_shape
|
||||
|
||||
if type(input_shape) == list:
|
||||
input_shape = input_shape[0][1:3]
|
||||
else:
|
||||
input_shape = input_shape[1:3]
|
||||
|
||||
input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
|
||||
|
||||
#------------------------
|
||||
|
||||
img = functions.preprocess_face(img = img_path, target_size = (input_shape_y, input_shape_x), enforce_detection = enforce_detection, detector_backend = detector_backend)
|
||||
target_representation = model.predict(img)[0,:]
|
||||
|
||||
distances = []
|
||||
for index, instance in df.iterrows():
|
||||
source_representation = instance["representation"]
|
||||
|
||||
if distance_metric == 'cosine':
|
||||
distance = dst.findCosineDistance(source_representation, target_representation)
|
||||
elif distance_metric == 'euclidean':
|
||||
distance = dst.findEuclideanDistance(source_representation, target_representation)
|
||||
elif distance_metric == 'euclidean_l2':
|
||||
distance = dst.findEuclideanDistance(dst.l2_normalize(source_representation), dst.l2_normalize(target_representation))
|
||||
else:
|
||||
raise ValueError("Invalid distance_metric passed - ", distance_metric)
|
||||
|
||||
distances.append(distance)
|
||||
|
||||
threshold = functions.findThreshold(model_name, distance_metric)
|
||||
|
||||
df["distance"] = distances
|
||||
df = df.drop(columns = ["representation"])
|
||||
df = df[df.distance <= threshold]
|
||||
|
||||
df = df.sort_values(by = ["distance"], ascending=True).reset_index(drop=True)
|
||||
resp_obj.append(df)
|
||||
df = df_base.copy() #restore df for the next iteration
|
||||
#----------------------------------
|
||||
|
||||
toc = time.time()
|
||||
|
||||
|
||||
@ -56,124 +56,3 @@ def build_gbm():
|
||||
deepface_ensemble = lgb.Booster(model_file = ensemble_model_path)
|
||||
|
||||
return deepface_ensemble
|
||||
|
||||
def verify(model, img_list, bulkProcess, enforce_detection, detector_backend):
|
||||
print("Ensemble learning enabled")
|
||||
|
||||
if model == None:
|
||||
model = loadModel()
|
||||
|
||||
validate_model(model)
|
||||
|
||||
#--------------------------
|
||||
|
||||
model_names = ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
|
||||
metrics = ["cosine", "euclidean", "euclidean_l2"]
|
||||
|
||||
resp_objects = []
|
||||
|
||||
#--------------------------
|
||||
|
||||
if model == None:
|
||||
model = loadModel()
|
||||
|
||||
#--------------------------
|
||||
|
||||
validate_model(model)
|
||||
|
||||
#--------------------------
|
||||
|
||||
pbar = tqdm(range(0,len(img_list)), desc='Verification')
|
||||
|
||||
for index in pbar:
|
||||
instance = img_list[index]
|
||||
|
||||
if type(instance) == list and len(instance) >= 2:
|
||||
img1_path = instance[0]
|
||||
img2_path = instance[1]
|
||||
|
||||
ensemble_features = []; ensemble_features_string = "["
|
||||
|
||||
for i in model_names:
|
||||
custom_model = model[i]
|
||||
|
||||
input_shape = custom_model.layers[0].input_shape
|
||||
|
||||
if type(input_shape) == list:
|
||||
input_shape = input_shape[0][1:3]
|
||||
else:
|
||||
input_shape = input_shape[1:3]
|
||||
|
||||
#----------------------------------
|
||||
|
||||
img1 = functions.preprocess_face(img = img1_path, target_size = input_shape
|
||||
, enforce_detection = enforce_detection
|
||||
, detector_backend = detector_backend)
|
||||
|
||||
img2 = functions.preprocess_face(img = img2_path, target_size = input_shape
|
||||
, enforce_detection = enforce_detection
|
||||
, detector_backend = detector_backend)
|
||||
|
||||
img1_representation = custom_model.predict(img1)[0,:]
|
||||
img2_representation = custom_model.predict(img2)[0,:]
|
||||
|
||||
for j in metrics:
|
||||
if j == 'cosine':
|
||||
distance = dst.findCosineDistance(img1_representation, img2_representation)
|
||||
elif j == 'euclidean':
|
||||
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
|
||||
elif j == 'euclidean_l2':
|
||||
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
|
||||
|
||||
#------------------------
|
||||
|
||||
#this returns same with OpenFace - euclidean_l2
|
||||
if i == 'OpenFace' and j == 'euclidean':
|
||||
continue
|
||||
else:
|
||||
ensemble_features.append(distance)
|
||||
|
||||
if len(ensemble_features) > 1:
|
||||
ensemble_features_string += ", "
|
||||
|
||||
ensemble_features_string += str(distance)
|
||||
|
||||
ensemble_features_string += "]"
|
||||
|
||||
#-------------------------------
|
||||
|
||||
deepface_ensemble = build_gbm()
|
||||
|
||||
#-------------------------------
|
||||
|
||||
prediction = deepface_ensemble.predict(np.expand_dims(np.array(ensemble_features), axis=0))[0]
|
||||
|
||||
verified = np.argmax(prediction) == 1
|
||||
|
||||
score = prediction[np.argmax(prediction)]
|
||||
|
||||
#print("verified: ", verified,", score: ", score)
|
||||
|
||||
resp_obj = {
|
||||
"verified": verified
|
||||
, "score": score
|
||||
, "distance": ensemble_features_string
|
||||
, "model": ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
|
||||
, "similarity_metric": ["cosine", "euclidean", "euclidean_l2"]
|
||||
}
|
||||
|
||||
if bulkProcess == True:
|
||||
resp_objects.append(resp_obj)
|
||||
else:
|
||||
return resp_obj
|
||||
|
||||
if bulkProcess == True:
|
||||
resp_obj = {}
|
||||
|
||||
for i in range(0, len(resp_objects)):
|
||||
resp_item = resp_objects[i]
|
||||
resp_obj["pair_%d" % (i+1)] = resp_item
|
||||
|
||||
return resp_obj
|
||||
|
||||
|
||||
@ -418,6 +418,8 @@ def align_face(img, detector_backend = 'opencv'):
|
||||
|
||||
def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_detection = True, detector_backend = 'opencv'):
|
||||
|
||||
#img_path = copy.copy(img)
|
||||
|
||||
#img might be path, base64 or numpy array. Convert it to numpy whatever it is.
|
||||
img = load_image(img)
|
||||
base_img = img.copy()
|
||||
@ -447,3 +449,18 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
|
||||
img_pixels /= 255 #normalize input in [0, 1]
|
||||
|
||||
return img_pixels
|
||||
|
||||
def find_input_shape(model):
|
||||
|
||||
#face recognition models have different size of inputs
|
||||
#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
|
||||
|
||||
input_shape = model.layers[0].input_shape
|
||||
|
||||
if type(input_shape) == list:
|
||||
input_shape = input_shape[0][1:3]
|
||||
else:
|
||||
input_shape = input_shape[1:3]
|
||||
|
||||
return input_shape
|
||||
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 99 KiB |
@ -35,7 +35,7 @@ print(res)
|
||||
|
||||
print("-----------------------------------------")
|
||||
|
||||
print("Large scale face recognition")
|
||||
print("Single find function test")
|
||||
|
||||
df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset"
|
||||
#, model_name = 'Dlib'
|
||||
@ -44,7 +44,30 @@ print(df.head())
|
||||
|
||||
print("-----------------------------------------")
|
||||
|
||||
print("Bulk face recognition tests")
|
||||
print("Pre-built model for single find function test")
|
||||
|
||||
model_name = "VGG-Face"
|
||||
model = DeepFace.build_model(model_name)
|
||||
print(model_name," is built")
|
||||
|
||||
df = DeepFace.find(img_path = "dataset/img1.jpg", db_path = "dataset"
|
||||
, model_name = model_name, model = model
|
||||
)
|
||||
print(df.head())
|
||||
|
||||
print("-----------------------------------------")
|
||||
|
||||
print("Bulk find function tests")
|
||||
|
||||
dfs = DeepFace.find(img_path = ["dataset/img1.jpg", "dataset/img2.jpg"], db_path = "dataset"
|
||||
#, model_name = 'Dlib'
|
||||
)
|
||||
print(dfs[0].head())
|
||||
print(dfs[1].head())
|
||||
|
||||
print("-----------------------------------------")
|
||||
|
||||
print("Bulk verification tests")
|
||||
|
||||
resp_obj = DeepFace.verify(dataset)
|
||||
print(resp_obj)
|
||||
@ -241,4 +264,3 @@ print(df)
|
||||
|
||||
#-----------------------------------
|
||||
print("--------------------------")
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user