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468 lines
16 KiB
Python
468 lines
16 KiB
Python
import os
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from tqdm import tqdm
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import numpy as np
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import pandas as pd
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import cv2
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import time
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import re
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import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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#from basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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#from extendedmodels import Age, Gender, Race, Emotion
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#from commons import functions, realtime, distance as dst
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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 analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
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input_shape = (224, 224)
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text_color = (255,255,255)
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employees = []
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#check passed db folder exists
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if os.path.isdir(db_path) == True:
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for r, d, f in os.walk(db_path): # r=root, d=directories, f = files
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for file in f:
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if ('.jpg' in file):
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#exact_path = os.path.join(r, file)
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exact_path = r + "/" + file
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#print(exact_path)
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employees.append(exact_path)
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#------------------------
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if len(employees) > 0:
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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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input_shape = (224, 224)
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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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input_shape = (96, 96)
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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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input_shape = (160, 160)
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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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input_shape = (152, 152)
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else:
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raise ValueError("Invalid model_name passed - ", model_name)
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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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#facial attribute analysis models
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if enable_face_analysis == True:
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tic = time.time()
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emotion_model = Emotion.loadModel()
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print("Emotion model loaded")
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age_model = Age.loadModel()
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print("Age model loaded")
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gender_model = Gender.loadModel()
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print("Gender model loaded")
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toc = time.time()
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print("Facial attibute analysis models loaded in ",toc-tic," seconds")
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#------------------------
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#find embeddings for employee list
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tic = time.time()
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pbar = tqdm(range(0, len(employees)), desc='Finding embeddings')
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embeddings = []
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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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pbar.set_description("Finding embedding for %s" % (employee.split("/")[-1]))
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embedding = []
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img = functions.detectFace(employee, input_shape)
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img_representation = model.predict(img)[0,:]
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embedding.append(employee)
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embedding.append(img_representation)
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embeddings.append(embedding)
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df = pd.DataFrame(embeddings, columns = ['employee', 'embedding'])
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df['distance_metric'] = distance_metric
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toc = time.time()
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print("Embeddings found for given data set in ", toc-tic," seconds")
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#-----------------------
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time_threshold = 5; frame_threshold = 5
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pivot_img_size = 112 #face recognition result image
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#-----------------------
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opencv_path = functions.get_opencv_path()
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face_detector_path = opencv_path+"haarcascade_frontalface_default.xml"
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face_cascade = cv2.CascadeClassifier(face_detector_path)
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#-----------------------
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freeze = False
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face_detected = False
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face_included_frames = 0 #freeze screen if face detected sequantially 5 frames
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freezed_frame = 0
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tic = time.time()
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cap = cv2.VideoCapture(0) #webcam
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#cap = cv2.VideoCapture("C:/Users/IS96273/Desktop/skype-video-1.mp4") #video
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while(True):
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ret, img = cap.read()
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#cv2.namedWindow('img', cv2.WINDOW_FREERATIO)
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#cv2.setWindowProperty('img', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
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raw_img = img.copy()
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resolution = img.shape
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resolution_x = img.shape[1]; resolution_y = img.shape[0]
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if freeze == False:
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faces = face_cascade.detectMultiScale(img, 1.3, 5)
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if len(faces) == 0:
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face_included_frames = 0
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else:
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faces = []
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detected_faces = []
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face_index = 0
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for (x,y,w,h) in faces:
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if w > 130: #discard small detected faces
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face_detected = True
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if face_index == 0:
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face_included_frames = face_included_frames + 1 #increase frame for a single face
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cv2.rectangle(img, (x,y), (x+w,y+h), (67,67,67), 1) #draw rectangle to main image
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cv2.putText(img, str(frame_threshold - face_included_frames), (int(x+w/4),int(y+h/1.5)), cv2.FONT_HERSHEY_SIMPLEX, 4, (255, 255, 255), 2)
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detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face
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#-------------------------------------
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detected_faces.append((x,y,w,h))
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face_index = face_index + 1
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#-------------------------------------
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if face_detected == True and face_included_frames == frame_threshold and freeze == False:
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freeze = True
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#base_img = img.copy()
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base_img = raw_img.copy()
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detected_faces_final = detected_faces.copy()
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tic = time.time()
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if freeze == True:
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toc = time.time()
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if (toc - tic) < time_threshold:
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if freezed_frame == 0:
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freeze_img = base_img.copy()
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#freeze_img = np.zeros(resolution, np.uint8) #here, np.uint8 handles showing white area issue
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for detected_face in detected_faces_final:
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x = detected_face[0]; y = detected_face[1]
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w = detected_face[2]; h = detected_face[3]
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cv2.rectangle(freeze_img, (x,y), (x+w,y+h), (67,67,67), 1) #draw rectangle to main image
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#-------------------------------
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#apply deep learning for custom_face
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custom_face = base_img[y:y+h, x:x+w]
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#-------------------------------
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#facial attribute analysis
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if enable_face_analysis == True:
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gray_img = functions.detectFace(custom_face, (48, 48), True)
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emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
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emotion_predictions = emotion_model.predict(gray_img)[0,:]
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sum_of_predictions = emotion_predictions.sum()
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mood_items = []
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for i in range(0, len(emotion_labels)):
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mood_item = []
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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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mood_item.append(emotion_label)
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mood_item.append(emotion_prediction)
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mood_items.append(mood_item)
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emotion_df = pd.DataFrame(mood_items, columns = ["emotion", "score"])
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emotion_df = emotion_df.sort_values(by = ["score"], ascending=False).reset_index(drop=True)
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#background of mood box
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#transparency
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overlay = freeze_img.copy()
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opacity = 0.4
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if x+w+pivot_img_size < resolution_x:
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#right
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cv2.rectangle(freeze_img
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#, (x+w,y+20)
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, (x+w,y)
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, (x+w+pivot_img_size, y+h)
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, (64,64,64),cv2.FILLED)
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cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
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elif x-pivot_img_size > 0:
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#left
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cv2.rectangle(freeze_img
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#, (x-pivot_img_size,y+20)
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, (x-pivot_img_size,y)
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, (x, y+h)
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, (64,64,64),cv2.FILLED)
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cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
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for index, instance in emotion_df.iterrows():
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emotion_label = "%s " % (instance['emotion'])
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emotion_score = instance['score']/100
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bar_x = 35 #this is the size if an emotion is 100%
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bar_x = int(bar_x * emotion_score)
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if x+w+pivot_img_size < resolution_x:
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text_location_y = y + 20 + (index+1) * 20
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text_location_x = x+w
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if text_location_y < y + h:
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cv2.putText(freeze_img, emotion_label, (text_location_x, text_location_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
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cv2.rectangle(freeze_img
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, (x+w+70, y + 13 + (index+1) * 20)
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, (x+w+70+bar_x, y + 13 + (index+1) * 20 + 5)
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, (255,255,255), cv2.FILLED)
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elif x-pivot_img_size > 0:
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text_location_y = y + 20 + (index+1) * 20
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text_location_x = x-pivot_img_size
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if text_location_y <= y+h:
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cv2.putText(freeze_img, emotion_label, (text_location_x, text_location_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
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cv2.rectangle(freeze_img
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, (x-pivot_img_size+70, y + 13 + (index+1) * 20)
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, (x-pivot_img_size+70+bar_x, y + 13 + (index+1) * 20 + 5)
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, (255,255,255), cv2.FILLED)
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#-------------------------------
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face_224 = functions.detectFace(custom_face, (224, 224), False)
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age_predictions = age_model.predict(face_224)[0,:]
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apparent_age = Age.findApparentAge(age_predictions)
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#-------------------------------
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gender_prediction = gender_model.predict(face_224)[0,:]
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if np.argmax(gender_prediction) == 0:
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gender = "W"
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elif np.argmax(gender_prediction) == 1:
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gender = "M"
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#print(str(int(apparent_age))," years old ", dominant_emotion, " ", gender)
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analysis_report = str(int(apparent_age))+" "+gender
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#-------------------------------
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info_box_color = (46,200,255)
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#top
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if y - pivot_img_size + int(pivot_img_size/5) > 0:
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triangle_coordinates = np.array( [
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(x+int(w/2), y)
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, (x+int(w/2)-int(w/10), y-int(pivot_img_size/3))
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, (x+int(w/2)+int(w/10), y-int(pivot_img_size/3))
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] )
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cv2.drawContours(freeze_img, [triangle_coordinates], 0, info_box_color, -1)
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cv2.rectangle(freeze_img, (x+int(w/5), y-pivot_img_size+int(pivot_img_size/5)), (x+w-int(w/5), y-int(pivot_img_size/3)), info_box_color, cv2.FILLED)
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cv2.putText(freeze_img, analysis_report, (x+int(w/3.5), y - int(pivot_img_size/2.1)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 111, 255), 2)
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#bottom
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elif y + h + pivot_img_size - int(pivot_img_size/5) < resolution_y:
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triangle_coordinates = np.array( [
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(x+int(w/2), y+h)
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, (x+int(w/2)-int(w/10), y+h+int(pivot_img_size/3))
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, (x+int(w/2)+int(w/10), y+h+int(pivot_img_size/3))
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] )
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cv2.drawContours(freeze_img, [triangle_coordinates], 0, info_box_color, -1)
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cv2.rectangle(freeze_img, (x+int(w/5), y + h + int(pivot_img_size/3)), (x+w-int(w/5), y+h+pivot_img_size-int(pivot_img_size/5)), info_box_color, cv2.FILLED)
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cv2.putText(freeze_img, analysis_report, (x+int(w/3.5), y + h + int(pivot_img_size/1.5)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 111, 255), 2)
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#-------------------------------
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#face recognition
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custom_face = functions.detectFace(custom_face, input_shape)
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#check detectFace function handled
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if custom_face.shape[1:3] == input_shape:
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if df.shape[0] > 0: #if there are images to verify, apply face recognition
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img1_representation = model.predict(custom_face)[0,:]
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#print(freezed_frame," - ",img1_representation[0:5])
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def findDistance(row):
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distance_metric = row['distance_metric']
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img2_representation = row['embedding']
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distance = 1000 #initialize very large value
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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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return distance
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df['distance'] = df.apply(findDistance, axis = 1)
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df = df.sort_values(by = ["distance"])
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candidate = df.iloc[0]
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employee_name = candidate['employee']
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best_distance = candidate['distance']
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if best_distance <= threshold:
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#print(employee_name)
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display_img = cv2.imread(employee_name)
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display_img = cv2.resize(display_img, (pivot_img_size, pivot_img_size))
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label = employee_name.split("/")[-1].replace(".jpg", "")
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label = re.sub('[0-9]', '', label)
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try:
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if y - pivot_img_size > 0 and x + w + pivot_img_size < resolution_x:
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#top right
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freeze_img[y - pivot_img_size:y, x+w:x+w+pivot_img_size] = display_img
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overlay = freeze_img.copy(); opacity = 0.4
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cv2.rectangle(freeze_img,(x+w,y),(x+w+pivot_img_size, y+20),(46,200,255),cv2.FILLED)
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cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
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cv2.putText(freeze_img, label, (x+w, y+10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1)
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#connect face and text
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cv2.line(freeze_img,(x+int(w/2), y), (x+3*int(w/4), y-int(pivot_img_size/2)),(67,67,67),1)
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cv2.line(freeze_img, (x+3*int(w/4), y-int(pivot_img_size/2)), (x+w, y - int(pivot_img_size/2)), (67,67,67),1)
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elif y + h + pivot_img_size < resolution_y and x - pivot_img_size > 0:
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#bottom left
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freeze_img[y+h:y+h+pivot_img_size, x-pivot_img_size:x] = display_img
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overlay = freeze_img.copy(); opacity = 0.4
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cv2.rectangle(freeze_img,(x-pivot_img_size,y+h-20),(x, y+h),(46,200,255),cv2.FILLED)
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cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
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cv2.putText(freeze_img, label, (x - pivot_img_size, y+h-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1)
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#connect face and text
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cv2.line(freeze_img,(x+int(w/2), y+h), (x+int(w/2)-int(w/4), y+h+int(pivot_img_size/2)),(67,67,67),1)
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cv2.line(freeze_img, (x+int(w/2)-int(w/4), y+h+int(pivot_img_size/2)), (x, y+h+int(pivot_img_size/2)), (67,67,67),1)
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elif y - pivot_img_size > 0 and x - pivot_img_size > 0:
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#top left
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freeze_img[y-pivot_img_size:y, x-pivot_img_size:x] = display_img
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overlay = freeze_img.copy(); opacity = 0.4
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cv2.rectangle(freeze_img,(x- pivot_img_size,y),(x, y+20),(46,200,255),cv2.FILLED)
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cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
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cv2.putText(freeze_img, label, (x - pivot_img_size, y+10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1)
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#connect face and text
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cv2.line(freeze_img,(x+int(w/2), y), (x+int(w/2)-int(w/4), y-int(pivot_img_size/2)),(67,67,67),1)
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cv2.line(freeze_img, (x+int(w/2)-int(w/4), y-int(pivot_img_size/2)), (x, y - int(pivot_img_size/2)), (67,67,67),1)
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elif x+w+pivot_img_size < resolution_x and y + h + pivot_img_size < resolution_y:
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#bottom righ
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freeze_img[y+h:y+h+pivot_img_size, x+w:x+w+pivot_img_size] = display_img
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overlay = freeze_img.copy(); opacity = 0.4
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cv2.rectangle(freeze_img,(x+w,y+h-20),(x+w+pivot_img_size, y+h),(46,200,255),cv2.FILLED)
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cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
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cv2.putText(freeze_img, label, (x+w, y+h-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1)
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#connect face and text
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cv2.line(freeze_img,(x+int(w/2), y+h), (x+int(w/2)+int(w/4), y+h+int(pivot_img_size/2)),(67,67,67),1)
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cv2.line(freeze_img, (x+int(w/2)+int(w/4), y+h+int(pivot_img_size/2)), (x+w, y+h+int(pivot_img_size/2)), (67,67,67),1)
|
|
except Exception as err:
|
|
print(str(err))
|
|
|
|
tic = time.time() #in this way, freezed image can show 5 seconds
|
|
|
|
#-------------------------------
|
|
|
|
time_left = int(time_threshold - (toc - tic) + 1)
|
|
|
|
cv2.rectangle(freeze_img, (10, 10), (90, 50), (67,67,67), -10)
|
|
cv2.putText(freeze_img, str(time_left), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1)
|
|
|
|
cv2.imshow('img', freeze_img)
|
|
|
|
freezed_frame = freezed_frame + 1
|
|
else:
|
|
face_detected = False
|
|
face_included_frames = 0
|
|
freeze = False
|
|
freezed_frame = 0
|
|
|
|
else:
|
|
cv2.imshow('img',img)
|
|
|
|
if cv2.waitKey(1) & 0xFF == ord('q'): #press q to quit
|
|
break
|
|
|
|
#kill open cv things
|
|
cap.release()
|
|
cv2.destroyAllWindows() |