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https://github.com/serengil/deepface.git
synced 2025-06-06 11:35:21 +00:00
json resp handled with dict instead of string
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serengil
parent
4d9e252d93
commit
cdea61c043
@ -12,7 +12,7 @@ from tqdm import tqdm
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import json
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import pickle
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID, DlibWrapper
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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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@ -24,7 +24,7 @@ def build_model(model_name):
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'Facenet': Facenet.loadModel,
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'DeepFace': FbDeepFace.loadModel,
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'DeepID': DeepID.loadModel,
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'Dlib': DlibResNet_,
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'Dlib': DlibWrapper.loadModel,
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'Emotion': Emotion.loadModel,
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'Age': Age.loadModel,
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'Gender': Gender.loadModel,
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@ -35,15 +35,13 @@ def build_model(model_name):
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if model:
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model = model()
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print('Using {} model backend'.format(model_name))
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#print('Using {} model backend'.format(model_name))
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return model
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else:
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raise ValueError('Invalid model_name passed - {}'.format(model_name))
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def verify(img1_path, img2_path = '', model_name='VGG-Face', distance_metric='cosine',
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model=None, enforce_detection=True, detector_backend = 'opencv'):
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# this is not a must because it is very huge.
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def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric = 'cosine',
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model = None, enforce_detection = True, detector_backend = 'mtcnn'):
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tic = time.time()
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@ -176,24 +174,18 @@ def verify(img1_path, img2_path = '', model_name='VGG-Face', distance_metric='co
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prediction = deepface_ensemble.predict(np.expand_dims(np.array(ensemble_features), axis=0))[0]
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verified = np.argmax(prediction) == 1
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if verified: identified = "true"
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else: identified = "false"
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score = prediction[np.argmax(prediction)]
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#print("verified: ", verified,", score: ", score)
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resp_obj = "{"
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resp_obj += "\"verified\": "+identified
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resp_obj += ", \"score\": "+str(score)
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resp_obj += ", \"distance\": "+ensemble_features_string
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resp_obj += ", \"model\": [\"VGG-Face\", \"Facenet\", \"OpenFace\", \"DeepFace\"]"
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resp_obj += ", \"similarity_metric\": [\"cosine\", \"euclidean\", \"euclidean_l2\"]"
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resp_obj += "}"
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#print(resp_obj)
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resp_obj = json.loads(resp_obj) #string to json
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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_string
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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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@ -203,17 +195,12 @@ def verify(img1_path, img2_path = '', model_name='VGG-Face', distance_metric='co
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#-------------------------------
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if bulkProcess == True:
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resp_obj = "{"
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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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if i > 0:
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resp_obj += ", "
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resp_obj += "\"pair_"+str(i+1)+"\": "+resp_item
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resp_obj += "}"
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resp_obj = json.loads(resp_obj)
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resp_item = resp_objects[i]
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resp_obj["pair_%d" % (i+1)] = resp_item
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return resp_obj
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return None
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@ -295,27 +282,25 @@ def verify(img1_path, img2_path = '', model_name='VGG-Face', distance_metric='co
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#decision
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if distance <= threshold:
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identified = "true"
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identified = True
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else:
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identified = "false"
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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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resp_obj += ", \"max_threshold_to_verify\": "+str(threshold)
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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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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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#K.clear_session()
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return resp_obj
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#----------------------
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@ -343,7 +328,8 @@ def verify(img1_path, img2_path = '', model_name='VGG-Face', distance_metric='co
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return resp_obj
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#return resp_objects
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def analyze(img_path, actions = [], models = {}, enforce_detection = True, detector_backend = 'opencv'):
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def analyze(img_path, actions = [], models = {}, enforce_detection = True
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, detector_backend = 'mtcnn'):
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if type(img_path) == list:
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img_paths = img_path.copy()
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@ -405,7 +391,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
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for j in global_pbar:
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img_path = img_paths[j]
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resp_obj = "{"
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resp_obj = {}
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disable_option = False if len(actions) > 1 else True
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@ -418,9 +404,6 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
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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.preprocess_face(img = img_path, target_size = (48, 48), grayscale = True, enforce_detection = enforce_detection, detector_backend = detector_backend)
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@ -429,20 +412,14 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
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sum_of_predictions = emotion_predictions.sum()
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emotion_obj = "\"emotion\": {"
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resp_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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resp_obj["emotion"][emotion_label] = emotion_prediction
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resp_obj["dominant_emotion"] = emotion_labels[np.argmax(emotion_predictions)]
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elif action == 'age':
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if img_224 is None:
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@ -451,7 +428,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
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age_predictions = age_model.predict(img_224)[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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resp_obj["age"] = str(int(apparent_age))
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elif action == 'gender':
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if img_224 is None:
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@ -465,7 +442,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
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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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resp_obj["gender"] = gender
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elif action == 'race':
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if img_224 is None:
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@ -474,55 +451,33 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec
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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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resp_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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resp_obj["race"][race_label] = race_prediction
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resp_obj["dominant_race"] = race_labels[np.argmax(race_predictions)]
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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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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 = resp_objects[i]
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resp_obj["instance_%d" % (i+1)] = resp_item
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if i > 0:
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resp_obj += ", "
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resp_obj += "\"instance_"+str(i+1)+"\": "+resp_item
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resp_obj += "}"
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resp_obj = json.loads(resp_obj)
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return resp_obj
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#return resp_objects
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def detectFace(img_path, detector_backend = 'opencv'):
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functions.initialize_detector(detector_backend = detector_backend)
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img = functions.preprocess_face(img = img_path, detector_backend = detector_backend)[0] #preprocess_face returns (1, 224, 224, 3)
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return img[:, :, ::-1] #bgr to rgb
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def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv'):
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def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'mtcnn'):
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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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@ -863,10 +818,12 @@ def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', ena
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realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis)
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def DlibResNet_():
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#this is not a regular Keras model.
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from deepface.basemodels.DlibResNet import DlibResNet
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return DlibResNet()
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def detectFace(img_path, detector_backend = 'mtcnn'):
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functions.initialize_detector(detector_backend = detector_backend)
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img = functions.preprocess_face(img = img_path, detector_backend = detector_backend)[0] #preprocess_face returns (1, 224, 224, 3)
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return img[:, :, ::-1] #bgr to rgb
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#---------------------------
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#main
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4
deepface/basemodels/DlibWrapper.py
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4
deepface/basemodels/DlibWrapper.py
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@ -0,0 +1,4 @@
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from deepface.basemodels.DlibResNet import DlibResNet
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def loadModel():
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return DlibResNet()
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2
setup.py
2
setup.py
@ -5,7 +5,7 @@ with open("README.md", "r", encoding="utf-8") as fh:
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setuptools.setup(
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name="deepface",
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version="0.0.41",
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version="0.0.42",
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author="Sefik Ilkin Serengil",
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author_email="serengil@gmail.com",
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description="A Lightweight Face Recognition and Facial Attribute Analysis Framework (Age, Gender, Emotion, Race) for Python",
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@ -47,6 +47,7 @@ print("-----------------------------------------")
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print("Bulk face recognition tests")
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resp_obj = DeepFace.verify(dataset)
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print(resp_obj)
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print(resp_obj["pair_1"]["verified"] == True)
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print(resp_obj["pair_2"]["verified"] == True)
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@ -124,6 +125,7 @@ for model in models:
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result = instance[2]
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resp_obj = DeepFace.verify(img1, img2, model_name = model, distance_metric = metric)
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prediction = resp_obj["verified"]
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distance = round(resp_obj["distance"], 2)
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required_threshold = resp_obj["max_threshold_to_verify"]
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