深度学习

Author: makelove

Notebook/Blog/deep-learning-opencv深度学习dnn/bvlc_googlenet.caffemodel

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+# USAGE
+'''
+python deep_learning_with_opencv.py --image images/jemma.png \
+	--prototxt bvlc_googlenet.prototxt \
+	--model bvlc_googlenet.caffemodel --labels synset_words.txt
+
+python deep_learning_with_opencv.py --image images/traffic_light.png \
+	--prototxt bvlc_googlenet.prototxt \
+	--model bvlc_googlenet.caffemodel --labels synset_words.txt
+
+
+python deep_learning_with_opencv.py --image images/eagle.png \
+	--prototxt bvlc_googlenet.prototxt \
+	--model bvlc_googlenet.caffemodel --labels synset_words.txt
+'''
+
+# import the necessary packages
+import numpy as np
+import argparse
+import time
+import cv2
+
+# construct the argument parse and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--image", required=True,
+                help="path to input image")
+ap.add_argument("-p", "--prototxt", required=True,
+                help="path to Caffe 'deploy' prototxt file")
+ap.add_argument("-m", "--model", required=True,
+                help="path to Caffe pre-trained model")
+ap.add_argument("-l", "--labels", required=True,
+                help="path to ImageNet labels (i.e., syn-sets)")
+args = vars(ap.parse_args())
+
+# load the input image from disk
+image = cv2.imread(args["image"])
+
+# load the class labels from disk
+rows = open(args["labels"]).read().strip().split("\n")
+classes = [r[r.find(" ") + 1:].split(",")[0] for r in rows]
+
+# our CNN requires fixed spatial dimensions for our input image(s)
+# so we need to ensure it is resized to 224x224 pixels while
+# performing mean subtraction (104, 117, 123) to normalize the input;
+# after executing this command our "blob" now has the shape:
+# (1, 3, 224, 224)
+blob = cv2.dnn.blobFromImage(image, 1, (224, 224), (104, 117, 123))
+
+# load our serialized model from disk
+print("[INFO] loading model...")
+net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
+
+# set the blob as input to the network and perform a forward-pass to
+# obtain our output classification
+net.setInput(blob)
+start = time.time()
+preds = net.forward()
+end = time.time()
+print("[INFO] classification took {:.5} seconds".format(end - start))
+
+# sort the indexes of the probabilities in descending order (higher
+# probabilitiy first) and grab the top-5 predictions
+idxs = np.argsort(preds[0])[::-1][:5]
+
+# loop over the top-5 predictions and display them
+for (i, idx) in enumerate(idxs):
+    # draw the top prediction on the input image
+    if i == 0:
+        text = "Label: {}, {:.2f}%".format(classes[idx], preds[0][idx] * 100)
+        cv2.putText(image, text, (5, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
+
+    # display the predicted label + associated probability to the
+    # console
+    print("[INFO] {}. label: {}, probability: {:.5}".format(i + 1, classes[idx], preds[0][idx]))
+
+# display the output image
+cv2.imshow("Image", image)
+cv2.waitKey(0)