Expected Ptr<cv::UMat> for argument 'img' for reading with TF and OpenCV

Question

I have this code from here with some modifications from here .

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", "unknown", "backpack",
            "umbrella", "unknown", "unknown", "handbag", "tie", "suitcase", "frisbee", "skis",
            "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard",
            "surfboard", "tennis racket", "bottle", "unknown", "wine glass", "cup", "fork", "knife",
            "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog",
            "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "unknown", "dining table",
            "unknown", "unknown", "toilet", "unknown", "tv", "laptop", "mouse", "remote", "keyboard",
            "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "unknown",
            "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, 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, 0, i, 1])
        print(idx   )
        box = detections[0, 0, i, 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, (startX, y),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 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()

and getting this error Expected Ptr<cv::UMat> for argument 'img' , after loking at most of the available solutions to this problem it seemed that at first input was not an array so changed to np.array but did not work, printing the image shows that the image which here is a frame from the video is present so the image is there.

Hence I am unable to figure out what is exactly causing this problem. Also to add this code works correctly if you pass just a single image using cv2.imread() .

Answer 1

I was incorrectly passing the frame to the array, I mean I was creating an empty array of some different variable instead of the frame obtained from the cv2.VideoCapture() method.

Following is the updated code:

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", "unknown", "backpack",
            "umbrella", "unknown", "unknown", "handbag", "tie", "suitcase", "frisbee", "skis",
            "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard",
            "surfboard", "tennis racket", "bottle", "unknown", "wine glass", "cup", "fork", "knife",
            "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog",
            "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "unknown", "dining table",
            "unknown", "unknown", "toilet", "unknown", "tv", "laptop", "mouse", "remote", "keyboard",
            "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "unknown",
            "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, 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, 0, i, 1])
        print(idx   )
        box = detections[0, 0, i, 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, (startX, y),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 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()