Delete video test

-import dlib, cv2
-import numpy as np
-
-detector = dlib.get_frontal_face_detector()
-sp = dlib.shape_predictor('models/shape_predictor_68_face_landmarks.dat')
-facerec = dlib.face_recognition_model_v1('models/dlib_face_recognition_resnet_model_v1.dat')
-
-descs = np.load('img/descs.npy', allow_pickle=True)[()]
-
-def encode_face(img):
-  dets = detector(img, 1)
-
-  if len(dets) == 0:
-    return np.empty(0)
-
-  for k, d in enumerate(dets):
-    shape = sp(img, d)
-    face_descriptor = facerec.compute_face_descriptor(img, shape)
-
-    return np.array(face_descriptor)
-
-video_path = './data/record0.mp4'
-cap = cv2.VideoCapture(video_path)
-
-if not cap.isOpened():
-  exit()
-
-_, img_bgr = cap.read() # (800, 1920, 3)
-padding_size = 0
-resized_width = 1920
-video_size = (resized_width, int(img_bgr.shape[0] * resized_width // img_bgr.shape[1]))
-output_size = (resized_width, int(img_bgr.shape[0] * resized_width // img_bgr.shape[1] + padding_size * 2))
-
-fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
-writer = cv2.VideoWriter('%s_output.mp4' % (video_path.split('.')[0]), fourcc, cap.get(cv2.CAP_PROP_FPS), output_size)
-
-while True:
-  ret, img_bgr = cap.read()
-  if not ret:
-    break
-
-  img_bgr = cv2.resize(img_bgr, video_size)
-  img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
-
-  # img_bgr = cv2.copyMakeBorder(img_bgr, top=padding_size, bottom=padding_size, left=0, right=0, borderType=cv2.BORDER_CONSTANT, value=(0,0,0))
-  
-  dets = detector(img_bgr, 1)
-
-  for k, d in enumerate(dets):
-    shape = sp(img_rgb, d)
-    face_descriptor = facerec.compute_face_descriptor(img_rgb, shape)
-
-    last_found = {'name': 'unknown', 'dist': 0.6, 'color': (0,0,255)}
-
-    for name, saved_desc in descs.items():
-      dist = np.linalg.norm([face_descriptor] - saved_desc, axis=1)
-
-      if dist < last_found['dist']:
-        last_found = {'name': name, 'dist': dist, 'color': (255,255,255)}
-
-    cv2.rectangle(img_bgr, pt1=(d.left(), d.top()), pt2=(d.right(), d.bottom()), color=last_found['color'], thickness=2)
-    cv2.putText(img_bgr, last_found['name'], org=(d.left(), d.top()), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=last_found['color'], thickness=2)
-
-  writer.write(img_bgr)
-
-  cv2.imshow('img', img_bgr)
-  
-  if cv2.waitKey(1) == ord('q'):
-    break
-
-cap.release()
-writer.release()

-import dlib
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-import tensorflow as tf
-import math
-import os
-import pathlib
-import time
-import pandas as pd
-import tensorflow as tf
-from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img
-from tensorflow.keras.models import load_model
-from tensorflow.keras import regularizers
-from tensorflow import keras
-from imutils import face_utils
-import time
-
- 
-start = time.time()
-detector = dlib.get_frontal_face_detector()
-predictor = dlib.shape_predictor("./models/shape_predictor_68_face_landmarks.dat")
-facerec = dlib.face_recognition_model_v1('models/dlib_face_recognition_resnet_model_v1.dat')
-model = load_model('../checkpoint/er-best-mobilenet1-bt32-model-classweight-adam.h5')
-# model = load_model('../checkpoint/er-best-mobilenet2-bt32-model-adam.h5')
-# model = load_model('../checkpoint/er-best-efficientNet1-bt32-model-SGD.h5')
-    
-    
-descs = np.load('img/descs2.npy', allow_pickle=True)[()]
- 
-video_path = './data/zoom_1.mp4'
-cap=cv2.VideoCapture(video_path)
- 
-
-
-labels_dict_ = {0 : 'angry', 1 : 'fear' ,  2: 'happy', 3: 'neutral', 4:  'sad', 5: 'surprise'}
-# labels_dict_ = {'angry' : 0,'fear' : 1 ,'happy' : 2, 'neutral' : 3,  'sad' : 4, 'surprise' : 5}
-time_dict = {'angry': [], 'fear': [], 'happy': [], 'neutral': [], 'sad': [], 'surprise': []}
-def get_key(val):
-    for key, value in labels_dict_.items():
-        if(value == val):
-            return key
-
-
-def convertMillis(millis):
-    seconds=(millis/1000)%60 
-    minutes=(millis/(1000*60))%60
-    hours=(millis/(1000*60*60))%24
-    return seconds, int(minutes), int(hours)
-
-#cap = cv2.VideoCapture(0) # 0번 카메라
- 
-# 동영상 크기(frame정보)를 읽어옴
-frameWidth = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-frameHeight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-frame_size = (frameWidth, frameHeight)
-fps = cap.get((cv2.CAP_PROP_FPS))
-
-
-_, img_bgr = cap.read() # (800, 1920, 3)
-padding_size = 0
-resized_width = 1920
-video_size = (resized_width, int(img_bgr.shape[0] * resized_width // img_bgr.shape[1]))
-timestamps = [cap.get(cv2.CAP_PROP_POS_MSEC)]
-prev_time = 0
-
-fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
-# out1 = cv2.VideoWriter('./data/record0.mp4',fourcc, fps, frame_size)
-
-while True:
-    retval, frameBGR = cap.read()	# 영상을 한 frame씩 읽어오기
-    current_time = time.time() - prev_time
-
-    if(type(frameBGR) == type(None)):
-        pass
-    else:
-        frameBGR = cv2.resize(frameBGR, video_size)
-        frame = cv2.cvtColor(frameBGR, cv2.COLOR_BGR2RGB)
-        
-        if (retval is True) and (current_time > 1.5) :
-            prev_time = time.time()
-            faces = detector(frame, 1)
-            
-            for (i, face) in enumerate(faces):
-                shape = predictor(frame, face)
-                face_descriptor = facerec.compute_face_descriptor(frame, shape)
-                
-                img = cv2.resize(frame[face.top():face.bottom(), face.left():face.right()], dsize=(224, 224), interpolation = cv2.INTER_CUBIC)
-                imgarr = np.array(img).reshape(1, 224, 224, 3) /255
-                emotion = labels_dict_[model.predict(imgarr).argmax(axis=-1)[0]]
-                # emotion = get_key(model.predict_classes(imgarr))
-
-                
-                last_found = {'name': 'unknown', 'dist': 0.6, 'color': (0,0,255)}
-                
-                for name, saved_desc in descs.items():
-                    dist = np.linalg.norm([face_descriptor] - saved_desc, axis=1)
-                    if dist < last_found['dist']:
-                        last_found = {'name': name, 'dist': dist, 'color': (255,255,255)}
-                
-                cv2.rectangle(frameBGR, pt1=(face.left(), face.top()), pt2=(face.right(), face.bottom()), color=last_found['color'], thickness=2)
-                cv2.putText(frameBGR, last_found['name'] + ',' + emotion , org=(face.left(), face.top()), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=last_found['color'], thickness=2)
-                # cv2.putText(frameBGR, last_found['name'] + ',' , org=(face.left(), face.top()), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=last_found['color'], thickness=2)
-            
-                con_sec, con_min, con_hour = convertMillis(cap.get(cv2.CAP_PROP_POS_MSEC))
-                time_dict[emotion].append("{0}:{1}:{2}".format(con_hour, con_min, round(con_sec, 3)))
-                print("{0}:{1}:{2} {3}".format(con_hour, con_min, round(con_sec, 3), emotion))
-                # print("{0}:{1}:{2} {3}".format(con_hour, con_min, con_sec))
-
-        cv2.imshow('frame', frameBGR)
-
-    key = cv2.waitKey(25)
-    if key == 27 :
-        break
-
-print(time_dict)       
-print("총 시간 : ", time.time() - start)
-if cap.isOpened():
-    cap.release()
-
-for i in range(1,5):
-    cv2.destroyAllWindows()
-    cv2.waitKey(1)

-import dlib
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-import tensorflow as tf
-import math
-import os
-import pathlib
-import time
-import pandas as pd
-import tensorflow as tf
-from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img
-from tensorflow.keras.models import load_model
-from tensorflow.keras import regularizers
-from tensorflow import keras
-from imutils import face_utils
-import time
-
- 
-start = time.time()
-detector = dlib.get_frontal_face_detector()
-predictor = dlib.shape_predictor("./models/shape_predictor_68_face_landmarks.dat")
-facerec = dlib.face_recognition_model_v1('models/dlib_face_recognition_resnet_model_v1.dat')
-# model = load_model('../checkpoint/er-best-mobilenet1-bt32-model-adam.h5')
-model = load_model('../checkpoint/er-best-efficientNet1-bt32-model-SGD.h5')
-    
-    
-descs = np.load('img/descs2.npy', allow_pickle=True)[()]
- 
-video_path = './data/zoom_1.mp4'
-cap=cv2.VideoCapture(video_path)
- 
-
-
-# labels_dict_ = {0 : 'angry', 1 : 'fear' ,  2: 'happy', 3: 'neutral', 4:  'sad', 5: 'surprise'}
-labels_dict_ = {'angry' : 0,'fear' : 1 ,'happy' : 2, 'neutral' : 3,  'sad' : 4, 'surprise' : 5}
-time_dict = {'angry': [], 'fear': [], 'happy': [], 'neutral': [], 'sad': [], 'surprise': []}
-def get_key(val):
-    for key, value in labels_dict_.items():
-        if(value == val):
-            return key
-
-
-def convertMillis(millis):
-    seconds=(millis/1000)%60 
-    minutes=(millis/(1000*60))%60
-    hours=(millis/(1000*60*60))%24
-    return seconds, int(minutes), int(hours)
-
-#cap = cv2.VideoCapture(0) # 0번 카메라
- 
-# 동영상 크기(frame정보)를 읽어옴
-frameWidth = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-frameHeight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-frame_size = (frameWidth, frameHeight)
-fps = cap.get((cv2.CAP_PROP_FPS))
-
-
-_, img_bgr = cap.read() # (800, 1920, 3)
-padding_size = 0
-resized_width = 1920
-video_size = (resized_width, int(img_bgr.shape[0] * resized_width // img_bgr.shape[1]))
-timestamps = [cap.get(cv2.CAP_PROP_POS_MSEC)]
-prev_time = 0
-
-fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
-# out1 = cv2.VideoWriter('./data/record0.mp4',fourcc, fps, frame_size)
-
-while True:
-    retval, frameBGR = cap.read()	# 영상을 한 frame씩 읽어오기
-    current_time = time.time() - prev_time
-
-    if(type(frameBGR) == type(None)):
-        pass
-    else:
-        frameBGR = cv2.resize(frameBGR, video_size)
-        frame = cv2.cvtColor(frameBGR, cv2.COLOR_BGR2RGB)
-        
-        if (retval is True) and (current_time > 1.5) :
-            prev_time = time.time()
-            faces = detector(frame, 1)
-            
-            for (i, face) in enumerate(faces):
-                shape = predictor(frame, face)
-                face_descriptor = facerec.compute_face_descriptor(frame, shape)
-                
-                img = cv2.resize(frame[face.top():face.bottom(), face.left():face.right()], dsize=(224, 224), interpolation = cv2.INTER_CUBIC)
-                imgarr = np.array(img).reshape(1, 224, 224, 3) /255
-                # emotion = labels_dict_[model.predict(imgarr).argmax(axis=-1)[0]]
-                emotion = get_key(model.predict_classes(imgarr))
-
-                
-                last_found = {'name': 'unknown', 'dist': 0.6, 'color': (0,0,255)}
-                
-                for name, saved_desc in descs.items():
-                    dist = np.linalg.norm([face_descriptor] - saved_desc, axis=1)
-                    if dist < last_found['dist']:
-                        last_found = {'name': name, 'dist': dist, 'color': (255,255,255)}
-                
-                cv2.rectangle(frameBGR, pt1=(face.left(), face.top()), pt2=(face.right(), face.bottom()), color=last_found['color'], thickness=2)
-                cv2.putText(frameBGR, last_found['name'] + ',' + emotion , org=(face.left(), face.top()), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=last_found['color'], thickness=2)
-                # cv2.putText(frameBGR, last_found['name'] + ',' , org=(face.left(), face.top()), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=last_found['color'], thickness=2)
-            
-                con_sec, con_min, con_hour = convertMillis(cap.get(cv2.CAP_PROP_POS_MSEC))
-                time_dict[emotion].append("{0}:{1}:{2}".format(con_hour, con_min, round(con_sec, 3)))
-                print("{0}:{1}:{2} {3}".format(con_hour, con_min, round(con_sec, 3), emotion))
-                # print("{0}:{1}:{2} {3}".format(con_hour, con_min, con_sec))
-
-        cv2.imshow('frame', frameBGR)
-
-    key = cv2.waitKey(25)
-    if key == 27 :
-        break
-
-print(time_dict)       
-print("총 시간 : ", time.time() - start)
-if cap.isOpened():
-    cap.release()
-
-for i in range(1,5):
-    cv2.destroyAllWindows()
-    cv2.waitKey(1)