嘗試將視頻導入到我的 cv2 面部情緒識別代碼時，獲取名稱未定義錯誤

Question

我正在嘗試使用 opencv 制作面部情感識別項目。 我可以通過我的網絡攝像頭使用和預測情緒（使用此代碼：cv2.VideoCapture(0)）。 但出於演示目的，我想將網絡攝像頭更改為特定的 mp4 視頻。 但我得到一個錯誤。 這是我的代碼：

path = "haarcascade_frontalface_default.xml"
font_scale = 1.5
font = cv2.FONT_HERSHEY_PLAIN

#set the rectangle background to white
rectangle_bgr = (255, 255, 255)
# make a black image
img = np.zeros((500, 500))
#set some text
text = "VİDGA Projesi"
#get the width and height of the text box
(text_width, text_height) = cv2.getTextSize(text, font, fontScale=font_scale, thickness=1)[0]
#set the text start position
text_offset_x = 10
text_offset_y = img.shape[0] - 25
#make the coords of the box with a small padding of two pixels
box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height -2))
cv2.rectangle(img, box_coords[0], box_coords[1], rectangle_bgr, cv2.FILLED)
cv2.putText(img, text, (text_offset_x, text_offset_y), font, fontScale=font_scale, color= (0,0,0), thickness=1)

cap = cv2.VideoCapture("sample.mp4")
#Check if the webcam is opened correctly
#if not cap.isOpened():
    #cap = cv2.VideoCapture(0)
if not cap.isOpened():
    raise IOError("Cannot open video")
    
while True:
    ret, frame = cap.read()
    #eye_Cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
    faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    
    if ret == True:
    
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        #print(faceCascade.empty())
        faces = faceCascade.detectMultiScale(gray,1.1,4)
        for x,y,w,h in faces:
            print("Alındı.")
            roi_gray = gray[y:y+h, x:x+w]
            roi_color = frame[y:y+h, x:x+w]
            cv2.rectangle(frame, (x,y), (x+w, y+h), (255, 0, 0), 2)
            facess = faceCascade.detectMultiScale(roi_gray)
            if len(facess) == 0:
                print("Face not detected")
            else: 
                for(ex,ey,ew,eh) in facess:
                    face_roi = roi_color[ey: ey+eh, ex: ex+ew] ##cropping the face
                
    graytemp = cv2.cvtColor(face_roi, cv2.COLOR_BGR2GRAY)
    final_image = cv2.resize(graytemp, (48,48))
    final_image = np.expand_dims(final_image, axis =0) #add third dimension
    final_image = np.expand_dims(final_image, axis =0) #add fourth dimension
    final_image = final_image/255.0 # normalization
    dataa = torch.from_numpy(final_image)
    dataa = dataa.type(torch.FloatTensor)
    dataa = dataa.to(device)
    outputs = net(dataa)
    Pred = F.softmax(outputs, dim=1)
    Predictions = torch.argmax(Pred).item()
    print(Predictions)

這是我得到的錯誤：

    ---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-5-d57dc2ffd8f1> in <module>
     48                     face_roi = roi_color[ey: ey+eh, ex: ex+ew] ##cropping the face
     49 
---> 50     graytemp = cv2.cvtColor(face_roi, cv2.COLOR_BGR2GRAY)
     51     final_image = cv2.resize(graytemp, (48,48))
     52     final_image = np.expand_dims(final_image, axis =0) #add third dimension

NameError: name 'face_roi' is not defined

當我將 cv2.VideoCapture("sample.mp4") 更改為 cv2.VideoCapture(0) 時，一切都像魅力一樣。 請幫我：）

Answer 1

您只在if ret == True塊內的else塊內定義了face_roi變量。 如果face_roi成功檢測到一次，那么一切都很好，因為即使在未能重新定義變量的循環中，它也會有之前定義的變量。

只需將while循環的最后一部分縮進為else塊（以及else塊內的for循環）的一部分：

            else: 
                for(ex,ey,ew,eh) in facess:
                    face_roi = roi_color[ey: ey+eh, ex: ex+ew] ##cropping the face
                    graytemp = cv2.cvtColor(face_roi, cv2.COLOR_BGR2GRAY)
                    final_image = cv2.resize(graytemp, (48,48))
                    final_image = np.expand_dims(final_image, axis =0) #add third dimension
                    final_image = np.expand_dims(final_image, axis =0) #add fourth dimension
                    final_image = final_image/255.0 # normalization
                    dataa = torch.from_numpy(final_image)
                    dataa = dataa.type(torch.FloatTensor)
                    dataa = dataa.to(device)
                    outputs = net(dataa)
                    Pred = F.softmax(outputs, dim=1)
                    Predictions = torch.argmax(Pred).item()
                    print(Predictions)

Answer 2

為什么不用deepface？ 運行以下代碼片段，它會處理所有事情。

#!pip install deepface
from deepface import DeepFace
DeepFace.stream()