stream video

.gitignore

@@ -14,4 +14,6 @@ deepface/commons/__pycache__/*
 deepface/basemodels/__pycache__/*
 deepface/extendedmodels/__pycache__/*
 deepface/subsidiarymodels/__pycache__/*
-tests/dataset/*.pkl
+tests/dataset/*.pkl
+.DS_Store
+deepface/.DS_Store

deepface/commons/realtime.py

@@ -15,11 +15,11 @@ from deepface.commons import functions, realtime, distance as dst
 
 def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 				, source = 0, time_threshold = 5, frame_threshold = 5):
-	
+
 	input_shape = (224, 224); input_shape_x = input_shape[0]; input_shape_y = input_shape[1]
-	
+
 	text_color = (255,255,255)
-	
+
 	employees = []
 	#check passed db folder exists
 	if os.path.isdir(db_path) == True:
@@ -30,54 +30,54 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 					exact_path = r + "/" + file
 					#print(exact_path)
 					employees.append(exact_path)
-					
+
 	if len(employees) == 0:
 		print("WARNING: There is no image in this path ( ", db_path,") . Face recognition will not be performed.")
-	
+
 	#------------------------
-	
+
 	if len(employees) > 0:
-		
+
 		model = DeepFace.build_model(model_name)
 		print(model_name," is built")
-		
+
 		#------------------------
-		
+
 		input_shape = functions.find_input_shape(model)
 		input_shape_x = input_shape[0]
 		input_shape_y = input_shape[1]
-		
+
 		#tuned thresholds for model and metric pair
 		threshold = dst.findThreshold(model_name, distance_metric)
-		
+
 	#------------------------
 	#facial attribute analysis models
-		
+
 	if enable_face_analysis == True:
-		
+
 		tic = time.time()
-				
+
 		emotion_model = DeepFace.build_model('Emotion')
 		print("Emotion model loaded")
-		
+
 		age_model = DeepFace.build_model('Age')
 		print("Age model loaded")
-		
+
 		gender_model = DeepFace.build_model('Gender')
 		print("Gender model loaded")
-		
+
 		toc = time.time()
-		
+
 		print("Facial attibute analysis models loaded in ",toc-tic," seconds")
-	
+
 	#------------------------
-	
+
 	#find embeddings for employee list
-	
+
 	tic = time.time()
-	
+
 	pbar = tqdm(range(0, len(employees)), desc='Finding embeddings')
-	
+
 	embeddings = []
 	#for employee in employees:
 	for index in pbar:
@@ -86,28 +86,28 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 		embedding = []
 		img = functions.preprocess_face(img = employee, target_size = (input_shape_y, input_shape_x), enforce_detection = False)
 		img_representation = model.predict(img)[0,:]
-		
+
 		embedding.append(employee)
 		embedding.append(img_representation)
 		embeddings.append(embedding)
-	
+
 	df = pd.DataFrame(embeddings, columns = ['employee', 'embedding'])
 	df['distance_metric'] = distance_metric
-	
+
 	toc = time.time()
-	
+
 	print("Embeddings found for given data set in ", toc-tic," seconds")
-	
+
 	#-----------------------
 
 	pivot_img_size = 112 #face recognition result image
 
 	#-----------------------
-	
+
 	opencv_path = functions.get_opencv_path()
 	face_detector_path = opencv_path+"haarcascade_frontalface_default.xml"
 	face_cascade = cv2.CascadeClassifier(face_detector_path)
-	
+
 	#-----------------------
 
 	freeze = False
@@ -120,83 +120,86 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 
 	while(True):
 		ret, img = cap.read()
-		
+
+		if img is None:
+			break
+
 		#cv2.namedWindow('img', cv2.WINDOW_FREERATIO)
 		#cv2.setWindowProperty('img', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
-		
+
 		raw_img = img.copy()
 		resolution = img.shape
-		
+
 		resolution_x = img.shape[1]; resolution_y = img.shape[0]
 
-		if freeze == False: 
+		if freeze == False:
 			faces = face_cascade.detectMultiScale(img, 1.3, 5)
-			
+
 			if len(faces) == 0:
 				face_included_frames = 0
-		else: 
+		else:
 			faces = []
-		
+
 		detected_faces = []
 		face_index = 0
 		for (x,y,w,h) in faces:
 			if w > 130: #discard small detected faces
-				
+
 				face_detected = True
 				if face_index == 0:
 					face_included_frames = face_included_frames + 1 #increase frame for a single face
-				
+
 				cv2.rectangle(img, (x,y), (x+w,y+h), (67,67,67), 1) #draw rectangle to main image
-				
+
 				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)
-				
+
 				detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face
-				
+
 				#-------------------------------------
-				
+
 				detected_faces.append((x,y,w,h))
 				face_index = face_index + 1
-				
+
 				#-------------------------------------
-				
+
 		if face_detected == True and face_included_frames == frame_threshold and freeze == False:
 			freeze = True
 			#base_img = img.copy()
 			base_img = raw_img.copy()
 			detected_faces_final = detected_faces.copy()
 			tic = time.time()
-		
+
 		if freeze == True:
 
 			toc = time.time()
 			if (toc - tic) < time_threshold:
-				
+
 				if freezed_frame == 0:
 					freeze_img = base_img.copy()
 					#freeze_img = np.zeros(resolution, np.uint8) #here, np.uint8 handles showing white area issue
-					
+
 					for detected_face in detected_faces_final:
 						x = detected_face[0]; y = detected_face[1]
 						w = detected_face[2]; h = detected_face[3]
-												
+
 						cv2.rectangle(freeze_img, (x,y), (x+w,y+h), (67,67,67), 1) #draw rectangle to main image
-						
+
 						#-------------------------------
-						
+
 						#apply deep learning for custom_face
-						
+
 						custom_face = base_img[y:y+h, x:x+w]
-						
+
 						#-------------------------------
 						#facial attribute analysis
-						
+
 						if enable_face_analysis == True:
-							
+
 							gray_img = functions.preprocess_face(img = custom_face, target_size = (48, 48), grayscale = True, enforce_detection = False)
 							emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
 							emotion_predictions = emotion_model.predict(gray_img)[0,:]
 							sum_of_predictions = emotion_predictions.sum()
-							
+
 							mood_items = []
 							for i in range(0, len(emotion_labels)):
 								mood_item = []
@@ -205,16 +208,16 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 								mood_item.append(emotion_label)
 								mood_item.append(emotion_prediction)
 								mood_items.append(mood_item)
-							
+
 							emotion_df = pd.DataFrame(mood_items, columns = ["emotion", "score"])
 							emotion_df = emotion_df.sort_values(by = ["score"], ascending=False).reset_index(drop=True)
-							
+
 							#background of mood box
-							
+
 							#transparency
 							overlay = freeze_img.copy()
 							opacity = 0.4
-							
+
 							if x+w+pivot_img_size < resolution_x:
 								#right
 								cv2.rectangle(freeze_img
@@ -222,9 +225,9 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 									, (x+w,y)
 									, (x+w+pivot_img_size, y+h)
 									, (64,64,64),cv2.FILLED)
-									
+
 								cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
-								
+
 							elif x-pivot_img_size > 0:
 								#left
 								cv2.rectangle(freeze_img
@@ -232,112 +235,112 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 									, (x-pivot_img_size,y)
 									, (x, y+h)
 									, (64,64,64),cv2.FILLED)
-								
+
 								cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
-							
+
 							for index, instance in emotion_df.iterrows():
 								emotion_label = "%s " % (instance['emotion'])
 								emotion_score = instance['score']/100
-								
+
 								bar_x = 35 #this is the size if an emotion is 100%
 								bar_x = int(bar_x * emotion_score)
 
 								if x+w+pivot_img_size < resolution_x:
-									
+
 									text_location_y = y + 20 + (index+1) * 20
 									text_location_x = x+w
-									
+
 									if text_location_y < y + h:
 										cv2.putText(freeze_img, emotion_label, (text_location_x, text_location_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
-										
+
 										cv2.rectangle(freeze_img
 											, (x+w+70, y + 13 + (index+1) * 20)
 											, (x+w+70+bar_x, y + 13 + (index+1) * 20 + 5)
 											, (255,255,255), cv2.FILLED)
-								
+
 								elif x-pivot_img_size > 0:
-									
+
 									text_location_y = y + 20 + (index+1) * 20
 									text_location_x = x-pivot_img_size
-									
+
 									if text_location_y <= y+h:
 										cv2.putText(freeze_img, emotion_label, (text_location_x, text_location_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
-										
+
 										cv2.rectangle(freeze_img
 											, (x-pivot_img_size+70, y + 13 + (index+1) * 20)
 											, (x-pivot_img_size+70+bar_x, y + 13 + (index+1) * 20 + 5)
 											, (255,255,255), cv2.FILLED)
-							
+
 							#-------------------------------
-							
+
 							face_224 = functions.preprocess_face(img = custom_face, target_size = (224, 224), grayscale = False, enforce_detection = False)
-							
+
 							age_predictions = age_model.predict(face_224)[0,:]
 							apparent_age = Age.findApparentAge(age_predictions)
-						
+
 							#-------------------------------
-							
+
 							gender_prediction = gender_model.predict(face_224)[0,:]
-							
+
 							if np.argmax(gender_prediction) == 0:
 								gender = "W"
 							elif np.argmax(gender_prediction) == 1:
 								gender = "M"
-							
+
 							#print(str(int(apparent_age))," years old ", dominant_emotion, " ", gender)
-							
+
 							analysis_report = str(int(apparent_age))+" "+gender
-							
+
 							#-------------------------------
-							
+
 							info_box_color = (46,200,255)
-							
+
 							#top
 							if y - pivot_img_size + int(pivot_img_size/5) > 0:
-								
+
 								triangle_coordinates = np.array( [
 									(x+int(w/2), y)
 									, (x+int(w/2)-int(w/10), y-int(pivot_img_size/3))
 									, (x+int(w/2)+int(w/10), y-int(pivot_img_size/3))
 								] )
-								
+
 								cv2.drawContours(freeze_img, [triangle_coordinates], 0, info_box_color, -1)
-								
+
 								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)
-								
+
 								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)
-							
+
 							#bottom
 							elif y + h + pivot_img_size - int(pivot_img_size/5) < resolution_y:
-							
+
 								triangle_coordinates = np.array( [
 									(x+int(w/2), y+h)
 									, (x+int(w/2)-int(w/10), y+h+int(pivot_img_size/3))
 									, (x+int(w/2)+int(w/10), y+h+int(pivot_img_size/3))
 								] )
-								
+
 								cv2.drawContours(freeze_img, [triangle_coordinates], 0, info_box_color, -1)
-								
+
 								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)
-								
+
 								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)
-								
+
 						#-------------------------------
 						#face recognition
-						
+
 						custom_face = functions.preprocess_face(img = custom_face, target_size = (input_shape_y, input_shape_x), enforce_detection = False)
-						
+
 						#check preprocess_face function handled
 						if custom_face.shape[1:3] == input_shape:
 							if df.shape[0] > 0: #if there are images to verify, apply face recognition
 								img1_representation = model.predict(custom_face)[0,:]
-								
+
 								#print(freezed_frame," - ",img1_representation[0:5])
-								
+
 								def findDistance(row):
 									distance_metric = row['distance_metric']
 									img2_representation = row['embedding']
-									
+
 									distance = 1000 #initialize very large value
 									if distance_metric == 'cosine':
 										distance = dst.findCosineDistance(img1_representation, img2_representation)
@@ -345,111 +348,111 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True
 										distance = dst.findEuclideanDistance(img1_representation, img2_representation)
 									elif distance_metric == 'euclidean_l2':
 										distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
-										
+
 									return distance
-								
+
 								df['distance'] = df.apply(findDistance, axis = 1)
 								df = df.sort_values(by = ["distance"])
-								
+
 								candidate = df.iloc[0]
 								employee_name = candidate['employee']
 								best_distance = candidate['distance']
-								
+
 								#print(candidate[['employee', 'distance']].values)
-								
+
 								#if True:
 								if best_distance <= threshold:
 									#print(employee_name)
 									display_img = cv2.imread(employee_name)
-									
+
 									display_img = cv2.resize(display_img, (pivot_img_size, pivot_img_size))
-																		
+
 									label = employee_name.split("/")[-1].replace(".jpg", "")
 									label = re.sub('[0-9]', '', label)
-									
+
 									try:
 										if y - pivot_img_size > 0 and x + w + pivot_img_size < resolution_x:
 											#top right
 											freeze_img[y - pivot_img_size:y, x+w:x+w+pivot_img_size] = display_img
-											
+
 											overlay = freeze_img.copy(); opacity = 0.4
 											cv2.rectangle(freeze_img,(x+w,y),(x+w+pivot_img_size, y+20),(46,200,255),cv2.FILLED)
 											cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
-											
+
 											cv2.putText(freeze_img, label, (x+w, y+10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1)
-											
+
 											#connect face and text
 											cv2.line(freeze_img,(x+int(w/2), y), (x+3*int(w/4), y-int(pivot_img_size/2)),(67,67,67),1)
 											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)
-											
+
 										elif y + h + pivot_img_size < resolution_y and x - pivot_img_size > 0:
 											#bottom left
 											freeze_img[y+h:y+h+pivot_img_size, x-pivot_img_size:x] = display_img
-											
+
 											overlay = freeze_img.copy(); opacity = 0.4
 											cv2.rectangle(freeze_img,(x-pivot_img_size,y+h-20),(x, y+h),(46,200,255),cv2.FILLED)
 											cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
-											
+
 											cv2.putText(freeze_img, label, (x - pivot_img_size, y+h-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1)
-											
+
 											#connect face and text
 											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)
 											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)
-											
+
 										elif y - pivot_img_size > 0 and x - pivot_img_size > 0:
 											#top left
 											freeze_img[y-pivot_img_size:y, x-pivot_img_size:x] = display_img
-											
+
 											overlay = freeze_img.copy(); opacity = 0.4
 											cv2.rectangle(freeze_img,(x- pivot_img_size,y),(x, y+20),(46,200,255),cv2.FILLED)
 											cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
-											
+
 											cv2.putText(freeze_img, label, (x - pivot_img_size, y+10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1)
-											
+
 											#connect face and text
 											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)
 											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)
-											
+
 										elif x+w+pivot_img_size < resolution_x and y + h + pivot_img_size < resolution_y:
 											#bottom righ
 											freeze_img[y+h:y+h+pivot_img_size, x+w:x+w+pivot_img_size] = display_img
-											
+
 											overlay = freeze_img.copy(); opacity = 0.4
 											cv2.rectangle(freeze_img,(x+w,y+h-20),(x+w+pivot_img_size, y+h),(46,200,255),cv2.FILLED)
 											cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
-											
+
 											cv2.putText(freeze_img, label, (x+w, y+h-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1)
-											
+
 											#connect face and text
 											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)
 											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		
+	#kill open cv things
 	cap.release()
 	cv2.destroyAllWindows()