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Merge pull request #17 from uriafranko/uria-franko
passing already built models to analyze function as well
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Sefik Ilkin Serengil
GitHub
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fc70aa0aad
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Contributors.md
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1
Contributors.md
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@ -0,0 +1 @@
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uriafranko
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@ -20,73 +20,74 @@ from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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from deepface.extendedmodels import Age, Gender, Race, Emotion
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from deepface.commons import functions, realtime, distance as dst
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def verify(img1_path, img2_path=''
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, model_name ='VGG-Face', distance_metric = 'cosine', model = None):
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tic = time.time()
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if type(img1_path) == list:
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bulkProcess = True
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img_list = img1_path.copy()
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else:
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bulkProcess = False
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img_list = [[img1_path, img2_path]]
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#------------------------------
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if model == None:
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if model_name == 'VGG-Face':
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print("Using VGG-Face model backend and", distance_metric,"distance.")
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model = VGGFace.loadModel()
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elif model_name == 'OpenFace':
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print("Using OpenFace model backend", distance_metric,"distance.")
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model = OpenFace.loadModel()
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elif model_name == 'Facenet':
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print("Using Facenet model backend", distance_metric,"distance.")
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model = Facenet.loadModel()
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elif model_name == 'DeepFace':
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print("Using FB DeepFace model backend", distance_metric,"distance.")
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model = FbDeepFace.loadModel()
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else:
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raise ValueError("Invalid model_name passed - ", model_name)
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else: #model != None
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print("Already built model is passed")
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#------------------------------
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#face recognition models have different size of inputs
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input_shape = model.layers[0].input_shape[1:3]
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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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resp_objects = []
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for instance in img_list:
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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.detectFace(img1_path, input_shape)
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img2 = functions.detectFace(img2_path, input_shape)
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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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@ -95,18 +96,18 @@ def verify(img1_path, img2_path=''
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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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resp_obj = "{"
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resp_obj += "\"verified\": "+identified
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resp_obj += ", \"distance\": "+str(distance)
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@ -114,30 +115,30 @@ def verify(img1_path, img2_path=''
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resp_obj += ", \"model\": \""+model_name+"\""
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resp_obj += ", \"similarity_metric\": \""+distance_metric+"\""
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resp_obj += "}"
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resp_obj = json.loads(resp_obj) #string to json
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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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K.clear_session()
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return resp_obj
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#----------------------
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else:
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raise ValueError("Invalid arguments passed to verify function: ", instance)
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#-------------------------
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toc = time.time()
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#print("identification lasts ",toc-tic," seconds")
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if bulkProcess == True:
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resp_obj = "{"
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for i in range(0, len(resp_objects)):
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resp_item = json.dumps(resp_objects[i])
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resp_item = json.dumps(resp_objects[i])
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if i > 0:
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resp_obj += ", "
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@ -148,7 +149,8 @@ def verify(img1_path, img2_path=''
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return resp_obj
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#return resp_objects
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def analyze(img_path, actions= []):
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def analyze(img_path, actions= [], models= {}):
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if type(img_path) == list:
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img_paths = img_path.copy()
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@ -156,129 +158,141 @@ def analyze(img_path, actions= []):
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else:
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img_paths = [img_path]
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bulkProcess = False
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#---------------------------------
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#if a specific target is not passed, then find them all
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if len(actions) == 0:
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actions= ['emotion', 'age', 'gender', 'race']
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print("Actions to do: ", actions)
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#---------------------------------
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if 'emotion' in actions:
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emotion_model = Emotion.loadModel()
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if 'emotion' in models:
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emotion_model = models['emotion']
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else:
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emotion_model = Emotion.loadModel()
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if 'age' in actions:
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age_model = Age.loadModel()
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if 'age' in models:
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age_model = models['age']
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else:
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age_model = Age.loadModel()
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if 'gender' in actions:
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gender_model = Gender.loadModel()
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if 'gender' in models:
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gender_model = models['gender']
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else:
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gender_model = Gender.loadModel()
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if 'race' in actions:
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race_model = Race.loadModel()
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if 'race' in models:
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race_model = models['race']
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else:
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race_model = Race.loadModel()
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#---------------------------------
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resp_objects = []
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for img_path in img_paths:
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resp_obj = "{"
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#TO-DO: do this in parallel
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pbar = tqdm(range(0,len(actions)), desc='Finding actions')
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action_idx = 0
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#for action in actions:
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for index in pbar:
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action = actions[index]
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pbar.set_description("Action: %s" % (action))
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if action_idx > 0:
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resp_obj += ", "
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if action == 'emotion':
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emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
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img = functions.detectFace(img_path, (48, 48), True)
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emotion_predictions = emotion_model.predict(img)[0,:]
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sum_of_predictions = emotion_predictions.sum()
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emotion_obj = "\"emotion\": {"
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for i in range(0, len(emotion_labels)):
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emotion_label = emotion_labels[i]
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emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions
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if i > 0: emotion_obj += ", "
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emotion_obj += "\"%s\": %s" % (emotion_label, emotion_prediction)
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emotion_obj += "}"
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emotion_obj += ", \"dominant_emotion\": \"%s\"" % (emotion_labels[np.argmax(emotion_predictions)])
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resp_obj += emotion_obj
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elif action == 'age':
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img = functions.detectFace(img_path, (224, 224), False) #just emotion model expects grayscale images
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#print("age prediction")
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age_predictions = age_model.predict(img)[0,:]
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apparent_age = Age.findApparentAge(age_predictions)
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resp_obj += "\"age\": %s" % (apparent_age)
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elif action == 'gender':
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img = functions.detectFace(img_path, (224, 224), False) #just emotion model expects grayscale images
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#print("gender prediction")
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gender_prediction = gender_model.predict(img)[0,:]
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if np.argmax(gender_prediction) == 0:
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gender = "Woman"
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elif np.argmax(gender_prediction) == 1:
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gender = "Man"
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resp_obj += "\"gender\": \"%s\"" % (gender)
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elif action == 'race':
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img = functions.detectFace(img_path, (224, 224), False) #just emotion model expects grayscale images
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race_predictions = race_model.predict(img)[0,:]
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race_labels = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
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sum_of_predictions = race_predictions.sum()
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race_obj = "\"race\": {"
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for i in range(0, len(race_labels)):
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race_label = race_labels[i]
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race_prediction = 100 * race_predictions[i] / sum_of_predictions
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if i > 0: race_obj += ", "
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race_obj += "\"%s\": %s" % (race_label, race_prediction)
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race_obj += "}"
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race_obj += ", \"dominant_race\": \"%s\"" % (race_labels[np.argmax(race_predictions)])
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resp_obj += race_obj
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action_idx = action_idx + 1
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resp_obj += "}"
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resp_obj = json.loads(resp_obj)
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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 bulkProcess == True:
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resp_obj = "{"
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for i in range(0, len(resp_objects)):
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resp_item = json.dumps(resp_objects[i])
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resp_item = json.dumps(resp_objects[i])
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if i > 0:
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resp_obj += ", "
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@ -289,13 +303,16 @@ def analyze(img_path, actions= []):
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return resp_obj
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#return resp_objects
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def detectFace(img_path):
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img = functions.detectFace(img_path)[0] #detectFace returns (1, 224, 224, 3)
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return img[:, :, ::-1] #bgr to rgb
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def stream(db_path, model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True):
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realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis)
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#---------------------------
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functions.initializeFolder()
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