Program:
# python test_imagenet.py --image images/dog_beagle.png # import the necessary packages from keras.preprocessing import image as image_utils from imagenet_utils import decode_predictions from imagenet_utils import preprocess_input from vgg16 import VGG16 import numpy as np import argparse import cv2 # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-i", "--image", required=True, help="path to the input image") args = vars(ap.parse_args()) # load the original image via OpenCV so we can draw on it and display # it to our screen later orig = cv2.imread(args["image"]) # load the input image using the Keras helper utility while ensuring # that the image is resized to 224x224 pxiels, the required input # dimensions for the network -- then convert the PIL image to a # NumPy array print("[INFO] loading and preprocessing image...") image = image_utils.load_img(args["image"], target_size=(224, 224)) image = image_utils.img_to_array(image) # our image is now represented by a NumPy array of shape (3, 224, 224), # but we need to expand the dimensions to be (1, 3, 224, 224) so we can # pass it through the network -- we'll also preprocess the image by # subtracting the mean RGB pixel intensity from the ImageNet dataset image = np.expand_dims(image, axis=0) image = preprocess_input(image) # load the VGG16 network print("[INFO] loading network...") model = VGG16(weights="imagenet") # classify the image print("[INFO] classifying image...") preds = model.predict(image) (inID, label) = decode_predictions(preds)[0] # display the predictions to our screen print("ImageNet ID: {}, Label: {}".format(inID, label)) cv2.putText(orig, "Label: {}".format(label), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2) cv2.imshow("Classification", orig) cv2.waitKey(0) When performing an import cv2 error KeyError: 'cv2' .
Windows 7 32 bit Python 3.5 Visual Studio 2010 opencv 2.4.10
import cv2, then remove any other code from the question. The minimum reproducible example is jfs