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

""" Image classification using OpenCV CNN module using SqueezeNet and caffe pre-trained models (SqueezeNet v1.1 has 2.4x less computation than v1.0, without sacrificing accuracy.) deploy.prototxt: https://github.com/DeepScale/SqueezeNet/blob/master/SqueezeNet_v1.1/deploy.prototxtsqueezenet_v1.1.caffemodel: https://github.com/DeepScale/SqueezeNet/blob/master/SqueezeNet_v1.1/squeezenet_v1.1.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("squeezenet_v1.1_deploy.prototxt", "squeezenet_v1.1.caffemodel") # Load input image: image = cv2.imread("37351100481_451dc054aa.jpg") # Create the blob with a size of (227,227), mean subtraction values (104, 117, 123) blob = cv2.dnn.blobFromImage(image, 1, (227, 227), (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: preds = preds.reshape((1, len(classes))) 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) print(text) 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 SqueezeNet (v1.1) and caffe pre-trained models", fontsize=14, fontweight='bold') fig.patch.set_facecolor('silver') # Show the output image: show_img_with_matplotlib(image, "SqueezeNet (v1.1) and caffe pre-trained models", 1) # Show the Figure: plt.show()