参数'img'的预期Ptr <cv :: UMat>可通过TF和OpenCV读取

如何解决参数'img'的预期Ptr <cv :: UMat>可通过TF和OpenCV读取

我有here的这段代码，并对here进行了一些修改。

from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2


# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()

ap.add_argument("-c","--confidence",type=float,default=0.8,help="minimum probability to filter weak detections")
args = vars(ap.parse_args())


classes_90 = [ "person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","unknown","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush" ] 
# Leemos las clases disponibles en openImages
CLASSES = classes_90  #New list of classess with 90 classess.
print(CLASSES)

# Le damos colores a las cajas para cada clase
COLORS = np.random.uniform(0,255,size=(len(CLASSES),3)) 

# Importamos el modelo de red
cvNet = cv2.dnn.readNetFromTensorflow('faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb','faster_rcnn_inception_v2_coco_2018_01_28/resnet.pbtxt')

# Leemos una imagen
img = cv2.VideoCapture('people.mp4')  


while img.isOpened():
    ret,frame = img.read()
    
    if not ret:
        
   

 break


#img = cv2.imread(args["image"])

# Obtenemos las dimensiones de la imagen
h = frame.shape[0] # Alto
w = frame.shape[1] # Ancho
img = np.array(img)
cvNet.setInput(cv2.dnn.blobFromImage(img,size=(h,w),swapRB=True,crop=False))
detections = cvNet.forward()


# loop over the detections
for i in np.arange(0,detections.shape[2]):
    # extract the confidence (i.e.,probability) associated with
    # the prediction
    confidence = detections[0,i,2]

    # filter out weak detections by ensuring the `confidence` is
    # greater than the minimum confidence
    if confidence > args["confidence"]:
        # extract the index of the class label from the
        # `detections`,then compute the (x,y)-coordinates of
        # the bounding box for the object
        idx = int(detections[0,1])
        print(idx   )
        box = detections[0,3:7] * np.array([w,h,w,h])
        (startX,startY,endX,endY) = box.astype("int")

        # draw the prediction on the frame
        label = "{}: {:.2f}%".format(CLASSES[idx],confidence * 100)
        cv2.rectangle(img,(startX,startY),(endX,endY),COLORS[idx],2)
        y = startY - 15 if startY - 15 > 15 else startY + 15
        cv2.putText(img,label,y),cv2.FONT_HERSHEY_SIMPLEX,0.5,2)

        print(label)


out_img = cv2.resize(img,(640,480))
out.write(out_img)
cv2.imshow('img',img)
#cv2.waitKey()
if cv2.waitKey(25) & 0xFF == ord('q'):

    cv2.destroyAllWindows()
    cap.release()
    out.release()

并得到此错误 Expected Ptr<cv::UMat> for argument 'img'，在查找了该问题的大多数可用解决方案之后，似乎一开始输入不是一个数组，所以更改为np.array但不起作用，打印该图像表明：存在图像，这是来自视频的一帧，因此图像在那里。

因此，我无法确定到底是什么导致了此问题。如果仅使用cv2.imread()传递单个图像，则添加此代码也可以正常工作。

解决方法

我不正确地将框架传递给数组，我的意思是我正在创建一个包含一些不同变量的空数组，而不是从cv2.VideoCapture()方法获得的框架。

以下是更新的代码：

from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2


# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()

ap.add_argument("-c","--confidence",type=float,default=0.8,help="minimum probability to filter weak detections")
args = vars(ap.parse_args())


classes_90 = [ "person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","unknown","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush" ] 
# Leemos las clases disponibles en openImages
CLASSES = classes_90  #New list of classess with 90 classess.
print(CLASSES)

# Le damos colores a las cajas para cada clase
COLORS = np.random.uniform(0,255,size=(len(CLASSES),3)) 

# Importamos el modelo de red
cvNet = cv2.dnn.readNetFromTensorflow('faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb','faster_rcnn_inception_v2_coco_2018_01_28/resnet.pbtxt')

# Leemos una imagen
img = cv2.VideoCapture('people.mp4')  


while img.isOpened():
    ret,frame = img.read()
    
    if not ret:
        
   

 break


#img = cv2.imread(args["image"])

# Obtenemos las dimensiones de la imagen
h = frame.shape[0] # Alto
w = frame.shape[1] # Ancho
img = np.array(frame)
cvNet.setInput(cv2.dnn.blobFromImage(img,size=(h,w),swapRB=True,crop=False))
detections = cvNet.forward()


# loop over the detections
for i in np.arange(0,detections.shape[2]):
    # extract the confidence (i.e.,probability) associated with
    # the prediction
    confidence = detections[0,i,2]

    # filter out weak detections by ensuring the `confidence` is
    # greater than the minimum confidence
    if confidence > args["confidence"]:
        # extract the index of the class label from the
        # `detections`,then compute the (x,y)-coordinates of
        # the bounding box for the object
        idx = int(detections[0,1])
        print(idx   )
        box = detections[0,3:7] * np.array([w,h,w,h])
        (startX,startY,endX,endY) = box.astype("int")

        # draw the prediction on the frame
        label = "{}: {:.2f}%".format(CLASSES[idx],confidence * 100)
        cv2.rectangle(img,(startX,startY),(endX,endY),COLORS[idx],2)
        y = startY - 15 if startY - 15 > 15 else startY + 15
        cv2.putText(img,label,y),cv2.FONT_HERSHEY_SIMPLEX,0.5,2)

        print(label)


out_img = cv2.resize(img,(640,480))
out.write(out_img)
cv2.imshow('img',img)
#cv2.waitKey()
if cv2.waitKey(25) & 0xFF == ord('q'):

    cv2.destroyAllWindows()
    cap.release()
    out.release()