【PYTHON OPENCV】Image classification OpenCV CNN module using GoogLeNet and caffe pre trained models

 """

Image classification using OpenCV CNN module using GoogLeNet and caffe pre-trained models bvlc_googlenet.prototxt: https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/bvlc_googlenet.prototxtbvlc_googlenet.caffemodel: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel""" # Import required packages: import cv2 import numpy as np from matplotlib import pyplot as plt def show_img_with_matplotlib(color_img, title, pos): """Shows an image using matplotlib capabilities""" img_RGB = color_img[:, :, ::-1] ax = plt.subplot(1, 1, pos) plt.imshow(img_RGB) plt.title(title) plt.axis('off') # Load the names of the classes: rows = open('synset_words.txt').read().strip().split('\n') classes = [r[r.find(' ') + 1:].split(',')[0] for r in rows] # Load the serialized caffe model from disk: net = cv2.dnn.readNetFromCaffe("bvlc_googlenet.prototxt", "bvlc_googlenet.caffemodel") # Load input image: image = cv2.imread("church.jpg") # Create the blob with a size of (224,224), mean subtraction values (104, 117, 123) blob = cv2.dnn.blobFromImage(image, 1, (224, 224), (104, 117, 123)) print(blob.shape) # Feed the input blob to the network, perform inference and ghe the output: net.setInput(blob) preds = net.forward() # Get inference time: t, _ = net.getPerfProfile() print('Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency())) # Get the 10 indexes with the highest probability (in descending order) # This way, the index with the highest prob (top prediction) will be the first: indexes = np.argsort(preds[0])[::-1][:10] # We draw on the image the class and probability associated with the top prediction: text = "label: {}\nprobability: {:.2f}%".format(classes[indexes[0]], preds[0][indexes[0]] * 100) y0, dy = 30, 30 for i, line in enumerate(text.split('\n')): y = y0 + i * dy cv2.putText(image, line, (5, y), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2) # Print top 10 prediction: for (index, idx) in enumerate(indexes): print("{}. label: {}, probability: {:.10}".format(index + 1, classes[idx], preds[0][idx])) # Create the dimensions of the figure and set title: fig = plt.figure(figsize=(10, 6)) plt.suptitle("Image classification with OpenCV using GoogLeNet and caffe pre-trained models", fontsize=14, fontweight='bold') fig.patch.set_facecolor('silver') # Show the output image: show_img_with_matplotlib(image, "GoogLeNet and caffe pre-trained models", 1) # Show the Figure: plt.show()