如何正确使用经过预先训练的CNN对图像文件夹进行图像预测

Question

我试图建立一个CNN模型，并使用它在2833张图片上，看看它是否可以预测一个选择(我自己选择)三个功能和流行程度的分数从一个表格数据集。到目前为止，我的代码如下所示：

import os

import cv2
import argparse
import numpy as np
from keras.applications.vgg16 import VGG16
from keras.preprocessing import image as image_utils
from keras.applications.imagenet_utils import preprocess_input, decode_predictions

# Construct argument parser and parse the arguments
argument_parser = argparse.ArgumentParser()

# First two arguments specifies our only argument "image" with both short-/longhand versions where either 
# can be used
# This is a required argument, noted by required=True, the help gives additional info in the terminal
# if needed
argument_parser.add_argument("-i", "--image", required=True, help="path to the input image")

# Set path to files
img_path = "images/"
files = os.listdir(img_path)
print("[INFO] loading and processing images...")

# Loop through images
for filename in files:
    # Load original via OpenCV, so we can draw on it and display it on our screen
    original = cv2.imread(filename)

    # Load image while resizing to 224x224 pixels, then convert to a NumPy array because load_img returns 
    # Pillow format
    image = image_utils.load_img(filename, target_size=(224, 224))
    image = image_utils.img_to_array(image)

    """
    PRE-PROCESS
    The image is now a NumPy array of shape (224, 224, 3). 224 pixels tall, 224 pixels wide, 3 channels = 
    Red, Green, Blue. We need to expand to (1, 3, 224, 224) because when classifying images using Deep
    Learning and Convolutional Neural Networks, we often send several images (instead of one) through
    the network in “batches” for efficiency. We also subtract the mean RGB pixel intensity from the
    ImageNet dataset.
    """
    image = np.expand_dims(image, axis=0)
    image = preprocess_input(image)

    # Load Keras and classify the image
    print("[INFO] loading network...")
    model = VGG16(weights="imagenet")    # Load the VGG16 network pre-trained on the ImageNet dataset

    print("[INFO] classifying image...")
    predictions = model.predict(image)   # Classify the image (NumPy array with 1000 entries)
    P = decode_predictions(predictions)  # Get the ImageNet Unique ID of the label, along with human-readable label
    print(P)

    # Loop over the predictions and display the rank-5 (5 epochs) predictions + probabilities to our terminal
    for (i, (imagenetID, label, prob)) in enumerate(P[0]):
        print("{}. {}: {:.2f}%".format(i + 1, label, prob * 100))

    # Load the image via OpenCV, draw the top prediction on the image, and display the 
    image to our screen
    original = cv2.imread(filename)
    (imagenetID, label, prob) = P[0][0]
    cv2.putText(original, "Label: {}, {:.2f}%".format(label, prob * 100), (10, 30), 
                cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
    cv2.imshow("Classification", original)
    cv2.waitKey(0)

我遵循了这关于如何实现它的文章，并且它在一个图像上工作。但是，当我试图将代码放入循环中时，我会得到以下错误消息：

[ WARN:0@44.040] global D:\a\opencv-python\opencv-python\opencv\modules\imgcodecs\src\loadsave.cpp (239) cv::findDecoder imread_('100.png'): can't open/read file: check file path/integrity
Traceback (most recent call last):
  File "C:\PATH\test_imagenet.py", line 28, in <module>
    image = image_utils.load_img(filename, target_size=(224, 224))
  File "C:\PATH\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\preprocessing\image.py", line 313, in load_img
    return image.load_img(path, grayscale=grayscale, color_mode=color_mode,
  File "C:\PATH\AppData\Local\Programs\Python\Python39\lib\site-packages\keras_preprocessing\image\utils.py", line 113, in load_img
    with open(path, 'rb') as f:
FileNotFoundError: [Errno 2] No such file or directory: '100.png'

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正如您所看到的，我在项目中有这个文件，所以我不知道为什么它找不到它。如何正确地处理图像文件，而不仅仅是一个图像？

Answer 1

请查找下面的工作代码；

import os

import cv2
import argparse
import numpy as np
from keras.applications.vgg16 import VGG16
from keras.preprocessing import image as image_utils
from keras.applications.imagenet_utils import preprocess_input, decode_predictions

# Construct argument parser and parse the arguments
argument_parser = argparse.ArgumentParser()

# First two arguments specifies our only argument "image" with both short-/longhand versions where either 
# can be used
# This is a required argument, noted by required=True, the help gives additional info in the terminal
# if needed
argument_parser.add_argument("-i", "--image", required=True, help="path to the input image")

# Set path to files
img_path = "/content/train/"
files = os.listdir(img_path)
print("[INFO] loading and processing images...")

for filename in files:
    
    # Passing the entire path of the image file
    file= os.path.join(img_path, filename)
    
    # Load original via OpenCV, so we can draw on it and display it on our screen
    original = cv2.imread(file)

    image = image_utils.load_img(file, target_size=(224, 224))
    image = image_utils.img_to_array(image)

    image = np.expand_dims(image, axis=0)
    image = preprocess_input(image)


    print("[INFO] loading network...")
    model = VGG16(weights="imagenet")    # Load the VGG16 network pre-trained on the ImageNet dataset

    print("[INFO] classifying image...")
    predictions = model.predict(image)   # Classify the image (NumPy array with 1000 entries)
    P = decode_predictions(predictions)  # Get the ImageNet Unique ID of the label, along with human-readable label
    print(P)    

    # Loop over the predictions and display the rank-5 (5 epochs) predictions + probabilities to our terminal
    for (i, (imagenetID, label, prob)) in enumerate(P[0]):
        print("{}. {}: {:.2f}%".format(i + 1, label, prob * 100))

    original = cv2.imread(file)
    (imagenetID, label, prob) = P[0][0]
    cv2.putText(original, "Label: {}, {:.2f}%".format(label, prob * 100), (10, 30), 
                cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
    cv2.imshow(original)  
    cv2.waitKey(0) 

产出如下：

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如果这个问题仍然存在，请告诉我们。谢谢!