diff --git a/deepface/DeepFace.py b/deepface/DeepFace.py index d0fb876..d24a762 100644 --- a/deepface/DeepFace.py +++ b/deepface/DeepFace.py @@ -13,20 +13,20 @@ from deepface.extendedmodels import Age, Gender, Race, Emotion from deepface.commons import functions, realtime, distance as dst def build_model(model_name): - + """ This function builds a deepface model Parameters: model_name (string): face recognition or facial attribute model VGG-Face, Facenet, OpenFace, DeepFace, DeepID for face recognition Age, Gender, Emotion, Race for facial attributes - + Returns: built deepface model """ - + models = { - 'VGG-Face': VGGFace.loadModel, + 'VGG-Face': VGGFace.loadModel, 'OpenFace': OpenFace.loadModel, 'Facenet': Facenet.loadModel, 'DeepFace': FbDeepFace.loadModel, @@ -40,7 +40,7 @@ def build_model(model_name): } model = models.get(model_name) - + if model: model = model() #print('Using {} model backend'.format(model_name)) @@ -48,35 +48,34 @@ def build_model(model_name): else: raise ValueError('Invalid model_name passed - {}'.format(model_name)) -def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', - model = None, enforce_detection = True, detector_backend = 'mtcnn'): - +def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'): + """ - This function verifies an image pair is same person or different persons. - + This function verifies an image pair is same person or different persons. + Parameters: img1_path, img2_path: exact image path, numpy array or based64 encoded images could be passed. If you are going to call verify function for a list of image pairs, then you should pass an array instead of calling the function in for loops. - + e.g. img1_path = [ - ['img1.jpg', 'img2.jpg'], + ['img1.jpg', 'img2.jpg'], ['img2.jpg', 'img3.jpg'] ] - + model_name (string): VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib, ArcFace or Ensemble - + distance_metric (string): cosine, euclidean, euclidean_l2 - + model: Built deepface model. A face recognition model is built every call of verify function. You can pass pre-built face recognition model optionally if you will call verify function several times. - + model = DeepFace.build_model('VGG-Face') - + enforce_detection (boolean): If any face could not be detected in an image, then verify function will return exception. Set this to False not to have this exception. This might be convenient for low resolution images. - + detector_backend (string): set face detector backend as mtcnn, opencv, ssd or dlib - + Returns: Verify function returns a dictionary. If img1_path is a list of image pairs, then the function will return list of dictionary. - + { "verified": True , "distance": 0.2563 @@ -84,18 +83,18 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = , "model": "VGG-Face" , "similarity_metric": "cosine" } - + """ - + tic = time.time() - - img_list, bulkProcess = functions.initialize_input(img1_path, img2_path) + + img_list, bulkProcess = functions.initialize_input(img1_path, img2_path) functions.initialize_detector(detector_backend = detector_backend) resp_objects = [] - + #-------------------------------- - + if model_name == 'Ensemble': model_names = ["VGG-Face", "Facenet", "OpenFace", "DeepFace"] metrics = ["cosine", "euclidean", "euclidean_l2"] @@ -103,9 +102,9 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = model_names = []; metrics = [] model_names.append(model_name) metrics.append(distance_metric) - + #-------------------------------- - + if model == None: if model_name == 'Ensemble': models = Boosting.loadModel() @@ -120,54 +119,54 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = else: models = {} models[model_name] = model - + #------------------------------ - + #calling deepface in a for loop causes lots of progress bars. this prevents it. disable_option = False if len(img_list) > 1 else True - + pbar = tqdm(range(0,len(img_list)), desc='Verification', disable = disable_option) - + 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 = [] - + for i in model_names: custom_model = models[i] - + #decide input shape - input_shape = functions.find_input_shape(custom_model) + input_shape = functions.find_input_shape(custom_model) input_shape_x = input_shape[0]; input_shape_y = input_shape[1] - + #---------------------- #detect and align faces - + img1 = functions.preprocess_face(img=img1_path , target_size=(input_shape_y, input_shape_x) , enforce_detection = enforce_detection , detector_backend = detector_backend) - + img2 = functions.preprocess_face(img=img2_path , target_size=(input_shape_y, input_shape_x) , enforce_detection = enforce_detection , detector_backend = detector_backend) - + #---------------------- #find embeddings - + img1_representation = custom_model.predict(img1)[0,:] img2_representation = custom_model.predict(img2)[0,:] - + #---------------------- #find distances between embeddings - + for j in metrics: - + if j == 'cosine': distance = dst.findCosineDistance(img1_representation, img2_representation) elif j == 'euclidean': @@ -176,53 +175,53 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation)) else: raise ValueError("Invalid distance_metric passed - ", distance_metric) - + distance = np.float64(distance) #causes trobule for euclideans in api calls if this is not set (issue #175) #---------------------- #decision - + if model_name != 'Ensemble': - + threshold = dst.findThreshold(i, j) if distance <= threshold: identified = True else: identified = False - + resp_obj = { "verified": identified , "distance": distance , "max_threshold_to_verify": threshold , "model": model_name , "similarity_metric": distance_metric - + } - + if bulkProcess == True: resp_objects.append(resp_obj) else: return resp_obj - + else: #Ensemble - + #this returns same with OpenFace - euclidean_l2 if i == 'OpenFace' and j == 'euclidean': continue else: ensemble_features.append(distance) - + #---------------------- - + if model_name == 'Ensemble': - + boosted_tree = Boosting.build_gbm() - + prediction = boosted_tree.predict(np.expand_dims(np.array(ensemble_features), axis=0))[0] - + verified = np.argmax(prediction) == 1 score = prediction[np.argmax(prediction)] - + resp_obj = { "verified": verified , "score": score @@ -230,12 +229,12 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = , "model": ["VGG-Face", "Facenet", "OpenFace", "DeepFace"] , "similarity_metric": ["cosine", "euclidean", "euclidean_l2"] } - + if bulkProcess == True: resp_objects.append(resp_obj) else: return resp_obj - + #---------------------- else: @@ -244,93 +243,91 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = #------------------------- toc = time.time() - + 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 -def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] - , models = {}, enforce_detection = True - , detector_backend = 'mtcnn'): - +def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] , models = {}, enforce_detection = True, detector_backend = 'mtcnn'): + """ This function analyzes facial attributes including age, gender, emotion and race - + Parameters: img_path: exact image path, numpy array or base64 encoded image could be passed. If you are going to analyze lots of images, then set this to list. e.g. img_path = ['img1.jpg', 'img2.jpg'] - + actions (list): The default is ['age', 'gender', 'emotion', 'race']. You can drop some of those attributes. - + models: facial attribute analysis models are built in every call of analyze function. You can pass pre-built models to speed the function up. - + models = {} models['age'] = DeepFace.build_model('Age') models['gender'] = DeepFace.build_model('Gender') models['emotion'] = DeepFace.build_model('Emotion') models['race'] = DeepFace.build_model('race') - + enforce_detection (boolean): The function throws exception if a face could not be detected. Set this to True if you don't want to get exception. This might be convenient for low resolution images. - + detector_backend (string): set face detector backend as mtcnn, opencv, ssd or dlib. Returns: The function returns a dictionary. If img_path is a list, then it will return list of dictionary. - + { "region": {'x': 230, 'y': 120, 'w': 36, 'h': 45}, "age": 28.66, "gender": "woman", "dominant_emotion": "neutral", "emotion": { - 'sad': 37.65260875225067, - 'angry': 0.15512987738475204, - 'surprise': 0.0022171278033056296, - 'fear': 1.2489334680140018, - 'happy': 4.609785228967667, - 'disgust': 9.698561953541684e-07, + 'sad': 37.65260875225067, + 'angry': 0.15512987738475204, + 'surprise': 0.0022171278033056296, + 'fear': 1.2489334680140018, + 'happy': 4.609785228967667, + 'disgust': 9.698561953541684e-07, 'neutral': 56.33133053779602 } "dominant_race": "white", "race": { - 'indian': 0.5480832420289516, - 'asian': 0.7830780930817127, - 'latino hispanic': 2.0677512511610985, - 'black': 0.06337375962175429, - 'middle eastern': 3.088453598320484, + 'indian': 0.5480832420289516, + 'asian': 0.7830780930817127, + 'latino hispanic': 2.0677512511610985, + 'black': 0.06337375962175429, + 'middle eastern': 3.088453598320484, 'white': 93.44925880432129 } } - + """ img_paths, bulkProcess = functions.initialize_input(img_path) functions.initialize_detector(detector_backend = detector_backend) - + #--------------------------------- - + built_models = list(models.keys()) - + #--------------------------------- - + #pre-trained models passed but it doesn't exist in actions if len(built_models) > 0: if 'emotion' in built_models and 'emotion' not in actions: actions.append('emotion') - + if 'age' in built_models and 'age' not in actions: actions.append('age') - + if 'gender' in built_models and 'gender' not in actions: actions.append('gender') - + if 'race' in built_models and 'race' not in actions: actions.append('race') - + #--------------------------------- if 'emotion' in actions and 'emotion' not in built_models: @@ -344,20 +341,20 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] if 'race' in actions and 'race' not in built_models: models['race'] = build_model('Race') - + #--------------------------------- resp_objects = [] - + disable_option = False if len(img_paths) > 1 else True - + global_pbar = tqdm(range(0,len(img_paths)), desc='Analyzing', disable = disable_option) - + for j in global_pbar: img_path = img_paths[j] resp_obj = {} - + disable_option = False if len(actions) > 1 else True pbar = tqdm(range(0,len(actions)), desc='Finding actions', disable = disable_option) @@ -366,12 +363,12 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] region = [] # x, y, w, h of the detected face region region_labels = ['x', 'y', 'w', 'h'] - + #facial attribute analysis for index in pbar: action = actions[index] pbar.set_description("Action: %s" % (action)) - + if action == 'emotion': emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'] img, region = functions.preprocess_face(img = img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True) @@ -386,23 +383,23 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] sum_of_predictions = emotion_predictions.sum() resp_obj["emotion"] = {} - + for i in range(0, len(emotion_labels)): emotion_label = emotion_labels[i] emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions resp_obj["emotion"][emotion_label] = emotion_prediction - + resp_obj["dominant_emotion"] = emotion_labels[np.argmax(emotion_predictions)] elif action == 'age': if img_224 is None: img_224, region = functions.preprocess_face(img = img_path, target_size = (224, 224), grayscale = False, enforce_detection = enforce_detection, detector_backend = detector_backend, return_region = True) - + resp_obj["region"] = {} for i, parameter in enumerate(region_labels): resp_obj["region"][parameter] = region[i] - + age_predictions = models['age'].predict(img_224)[0,:] apparent_age = Age.findApparentAge(age_predictions) @@ -416,7 +413,7 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] for i, parameter in enumerate(region_labels): resp_obj["region"][parameter] = region[i] - + gender_prediction = models['gender'].predict(img_224)[0,:] if np.argmax(gender_prediction) == 0: @@ -436,28 +433,28 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] for i, parameter in enumerate(region_labels): resp_obj["region"][parameter] = region[i] - + sum_of_predictions = race_predictions.sum() - + resp_obj["race"] = {} for i in range(0, len(race_labels)): race_label = race_labels[i] race_prediction = 100 * race_predictions[i] / sum_of_predictions resp_obj["race"][race_label] = race_prediction - + resp_obj["dominant_race"] = race_labels[np.argmax(race_predictions)] - + #--------------------------------- - + 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["instance_%d" % (i+1)] = resp_item @@ -465,63 +462,63 @@ def analyze(img_path, actions = ['emotion', 'age', 'gender', 'race'] return resp_obj def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'): - + """ This function applies verification several times and find an identity in a database - + Parameters: img_path: exact image path, numpy array or based64 encoded image. If you are going to find several identities, then you should pass img_path as array instead of calling find function in a for loop. e.g. img_path = ["img1.jpg", "img2.jpg"] - + db_path (string): You should store some .jpg files in a folder and pass the exact folder path to this. - + model_name (string): VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib or Ensemble - + distance_metric (string): cosine, euclidean, euclidean_l2 - + model: built deepface model. A face recognition models are built in every call of find function. You can pass pre-built models to speed the function up. - + model = DeepFace.build_model('VGG-Face') - + enforce_detection (boolean): The function throws exception if a face could not be detected. Set this to True if you don't want to get exception. This might be convenient for low resolution images. - + detector_backend (string): set face detector backend as mtcnn, opencv, ssd or dlib - + Returns: This function returns pandas data frame. If a list of images is passed to img_path, then it will return list of pandas data frame. """ - + tic = time.time() - + img_paths, bulkProcess = functions.initialize_input(img_path) functions.initialize_detector(detector_backend = detector_backend) - + #------------------------------- - + if os.path.isdir(db_path) == True: - + if model == None: - + if model_name == 'Ensemble': - print("Ensemble learning enabled") + print("Ensemble learning enabled") models = Boosting.loadModel() - + else: #model is not ensemble model = build_model(model_name) models = {} models[model_name] = model - + else: #model != None print("Already built model is passed") - + if model_name == 'Ensemble': - Boosting.validate_model(model) + Boosting.validate_model(model) models = model.copy() else: models = {} models[model_name] = model - + #--------------------------------------- - + if model_name == 'Ensemble': model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace'] metric_names = ['cosine', 'euclidean', 'euclidean_l2'] @@ -529,149 +526,149 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model_names = []; metric_names = [] model_names.append(model_name) metric_names.append(distance_metric) - + #--------------------------------------- - + file_name = "representations_%s.pkl" % (model_name) file_name = file_name.replace("-", "_").lower() - + if path.exists(db_path+"/"+file_name): - + print("WARNING: Representations for images in ",db_path," folder were previously stored in ", file_name, ". If you added new instances after this file creation, then please delete this file and call find function again. It will create it again.") - + f = open(db_path+'/'+file_name, 'rb') representations = pickle.load(f) - + print("There are ", len(representations)," representations found in ",file_name) - + else: #create representation.pkl from scratch employees = [] - + for r, d, f in os.walk(db_path): # r=root, d=directories, f = files for file in f: if ('.jpg' in file.lower()) or ('.png' in file.lower()): exact_path = r + "/" + file employees.append(exact_path) - + if len(employees) == 0: raise ValueError("There is no image in ", db_path," folder! Validate .jpg or .png files exist in this path.") - + #------------------------ #find representations for db images - + representations = [] - + pbar = tqdm(range(0,len(employees)), desc='Finding representations') - + #for employee in employees: for index in pbar: employee = employees[index] - + instance = [] instance.append(employee) - + for j in model_names: custom_model = models[j] - + #---------------------------------- #decide input shape - - input_shape = functions.find_input_shape(custom_model) + + input_shape = functions.find_input_shape(custom_model) input_shape_x = input_shape[0]; input_shape_y = input_shape[1] - + #---------------------------------- - + img = functions.preprocess_face(img = employee , target_size = (input_shape_y, input_shape_x) , enforce_detection = enforce_detection , detector_backend = detector_backend) - + representation = custom_model.predict(img)[0,:] instance.append(representation) - + #------------------------------- - + representations.append(instance) - + f = open(db_path+'/'+file_name, "wb") pickle.dump(representations, f) f.close() - + print("Representations stored in ",db_path,"/",file_name," file. Please delete this file when you add new identities in your database.") - + #---------------------------- #now, we got representations for facial database - + if model_name != 'Ensemble': df = pd.DataFrame(representations, columns = ["identity", "%s_representation" % (model_name)]) else: #ensemble learning - + columns = ['identity'] [columns.append('%s_representation' % i) for i in model_names] - + df = pd.DataFrame(representations, columns = columns) - + df_base = df.copy() #df will be filtered in each img. we will restore it for the next item. - + resp_obj = [] - + global_pbar = tqdm(range(0,len(img_paths)), desc='Analyzing') for j in global_pbar: img_path = img_paths[j] - + #find representation for passed image - + for j in model_names: custom_model = models[j] - + #-------------------------------- #decide input shape - input_shape = functions.find_input_shape(custom_model) + input_shape = functions.find_input_shape(custom_model) 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 = custom_model.predict(img)[0,:] - + for k in metric_names: distances = [] for index, instance in df.iterrows(): source_representation = instance["%s_representation" % (j)] - + if k == 'cosine': distance = dst.findCosineDistance(source_representation, target_representation) elif k == 'euclidean': distance = dst.findEuclideanDistance(source_representation, target_representation) elif k == 'euclidean_l2': distance = dst.findEuclideanDistance(dst.l2_normalize(source_representation), dst.l2_normalize(target_representation)) - + distances.append(distance) - + #--------------------------- - + if model_name == 'Ensemble' and j == 'OpenFace' and k == 'euclidean': continue else: df["%s_%s" % (j, k)] = distances - + if model_name != 'Ensemble': threshold = dst.findThreshold(j, k) df = df.drop(columns = ["%s_representation" % (j)]) df = df[df["%s_%s" % (j, k)] <= threshold] - + df = df.sort_values(by = ["%s_%s" % (j, k)], ascending=True).reset_index(drop=True) - + resp_obj.append(df) df = df_base.copy() #restore df for the next iteration - + #---------------------------------- - + if model_name == 'Ensemble': - + feature_names = [] for j in model_names: for k in metric_names: @@ -680,106 +677,145 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', else: feature = '%s_%s' % (j, k) feature_names.append(feature) - + #print(df.head()) - + x = df[feature_names].values - + #-------------------------------------- - + boosted_tree = Boosting.build_gbm() - + y = boosted_tree.predict(x) - + verified_labels = []; scores = [] for i in y: verified = np.argmax(i) == 1 score = i[np.argmax(i)] - + verified_labels.append(verified) scores.append(score) - + df['verified'] = verified_labels df['score'] = scores - + df = df[df.verified == True] #df = df[df.score > 0.99] #confidence score df = df.sort_values(by = ["score"], ascending=False).reset_index(drop=True) df = df[['identity', 'verified', 'score']] - + resp_obj.append(df) df = df_base.copy() #restore df for the next iteration - + #---------------------------------- - + toc = time.time() - + print("find function lasts ",toc-tic," seconds") - + if len(resp_obj) == 1: return resp_obj[0] - + return resp_obj - + else: raise ValueError("Passed db_path does not exist!") - + return None - -def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine' - , enable_face_analysis = True - , source = 0, time_threshold = 5, frame_threshold = 5): - + +def represent(img_path, model_name = 'VGG-Face', distance_metric = 'euclidean', model = None, enforce_detection = True, detector_backend = 'mtcnn'): + + """ + This function represents facial images as vectors. + + Parameters: + img_path: exact image path, numpy array or based64 encoded images could be passed. + + model_name (string): VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib, ArcFace. + + distance_metric (string): cosine, euclidean, euclidean_l2 + + model: Built deepface model. A face recognition model is built every call of verify function. You can pass pre-built face recognition model optionally if you will call verify function several times. Consider to pass model if you are going to call represent function in a for loop. + + model = DeepFace.build_model('VGG-Face') + + enforce_detection (boolean): If any face could not be detected in an image, then verify function will return exception. Set this to False not to have this exception. This might be convenient for low resolution images. + + detector_backend (string): set face detector backend as mtcnn, opencv, ssd or dlib + + Returns: + Represent function returns a multidimensional vector. The number of dimensions is changing based on the reference model. E.g. FaceNet returns 128 dimensional vector; VGG-Face returns 2622 dimensional vector. + """ + + if model is None: + model = build_model(model_name) + + #--------------------------------- + + #decide input shape + input_shape = input_shape_x, input_shape_y= functions.find_input_shape(model) + + img = functions.preprocess_face(img = img_path + , target_size=(input_shape_y, input_shape_x) + , enforce_detection = enforce_detection + , detector_backend = detector_backend) + + embedding = model.predict(img)[0].tolist() + + return embedding + +def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True, source = 0, time_threshold = 5, frame_threshold = 5): + """ This function applies real time face recognition and facial attribute analysis - + Parameters: db_path (string): facial database path. You should store some .jpg files in this folder. - + model_name (string): VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib or Ensemble - + distance_metric (string): cosine, euclidean, euclidean_l2 - + enable_facial_analysis (boolean): Set this to False to just run face recognition - + source: Set this to 0 for access web cam. Otherwise, pass exact video path. - + time_threshold (int): how many second analyzed image will be displayed - + frame_threshold (int): how many frames required to focus on face - + """ - + if time_threshold < 1: raise ValueError("time_threshold must be greater than the value 1 but you passed "+str(time_threshold)) - + if frame_threshold < 1: raise ValueError("frame_threshold must be greater than the value 1 but you passed "+str(frame_threshold)) - + functions.initialize_detector(detector_backend = 'opencv') - + realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis , source = source, time_threshold = time_threshold, frame_threshold = frame_threshold) def detectFace(img_path, detector_backend = 'mtcnn'): - + """ - This function applies pre-processing stages of a face recognition pipeline including detection and alignment - + This function applies pre-processing stages of a face recognition pipeline including detection and alignment + Parameters: img_path: exact image path, numpy array or base64 encoded image - + detector_backend (string): face detection backends are mtcnn, opencv, ssd or dlib - + Returns: deteced and aligned face in numpy format """ - + functions.initialize_detector(detector_backend = detector_backend) - + img = functions.preprocess_face(img = img_path, detector_backend = detector_backend)[0] #preprocess_face returns (1, 224, 224, 3) return img[:, :, ::-1] #bgr to rgb - + #--------------------------- #main diff --git a/tests/unit_tests.py b/tests/unit_tests.py index 9089b86..6fc0500 100644 --- a/tests/unit_tests.py +++ b/tests/unit_tests.py @@ -26,6 +26,18 @@ from deepface.extendedmodels import Age, Gender, Race, Emotion #----------------------------------------- +img_path = "dataset/img1.jpg" +embedding = DeepFace.represent(img_path) +print("Function returned ", len(embedding), "dimensional vector") + +model_name = "VGG-Face" +model = DeepFace.build_model(model_name) +print(model_name," is built") +embedding = DeepFace.represent(img_path, model = model) +print("Represent function returned ", len(embedding), "dimensional vector") + +#----------------------------------------- + dataset = [ ['dataset/img1.jpg', 'dataset/img2.jpg', True], ['dataset/img1.jpg', 'dataset/img6.jpg', True] @@ -64,9 +76,9 @@ print("-----------------------------------------") print("Pre-built model for single find function test") -model_name = "VGG-Face" -model = DeepFace.build_model(model_name) -print(model_name," is built") +#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 @@ -140,10 +152,10 @@ dataset = [ ['dataset/img5.jpg', 'dataset/img6.jpg', True], ['dataset/img6.jpg', 'dataset/img7.jpg', True], ['dataset/img8.jpg', 'dataset/img9.jpg', True], - + ['dataset/img1.jpg', 'dataset/img11.jpg', True], ['dataset/img2.jpg', 'dataset/img11.jpg', True], - + ['dataset/img1.jpg', 'dataset/img3.jpg', False], ['dataset/img2.jpg', 'dataset/img3.jpg', False], ['dataset/img6.jpg', 'dataset/img8.jpg', False], @@ -163,29 +175,29 @@ for model in models: img1 = instance[0] img2 = instance[1] result = instance[2] - + resp_obj = DeepFace.verify(img1, img2 , model_name = model, model = prebuilt_model , distance_metric = metric) - + prediction = resp_obj["verified"] distance = round(resp_obj["distance"], 2) required_threshold = resp_obj["max_threshold_to_verify"] - + test_result_label = "failed" if prediction == result: passed_tests = passed_tests + 1 test_result_label = "passed" - + if prediction == True: classified_label = "verified" else: classified_label = "unverified" - + test_cases = test_cases + 1 - + print(img1.split("/")[-1], "-", img2.split("/")[-1], classified_label, "as same person based on", model,"and",metric,". Distance:",distance,", Threshold:", required_threshold,"(",test_result_label,")") - + print("--------------------------") #-----------------------------------------