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Author: yashk2000

deep_learning_with_opencv.py

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+import numpy as np
+import argparse
+import time
+import cv2
+
+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())
+
+image = cv2.imread(args["image"])
+rows = open(args["labels"]).read().strip().split("\n")
+classes = [r[r.find(" ") + 1:].split(",")[0] for r in rows]
+
+blob = cv2.dnn.blobFromImage(image, 1, (224, 224), (104, 117, 123))
+print("[INFO] loading model...")
+net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
+
+net.setInput(blob)
+start = time.time()
+preds = net.forward()
+end = time.time()
+print("[INFO] classification took {:.5} seconds".format(end - start))
+
+idxs = np.argsort(preds[0])[::-1][:5]
+
+for (i, idx) in enumerate(idxs):
+	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)
+ 
+	print("[INFO] {}. label: {}, probability: {:.5}".format(i + 1,
+		classes[idx], preds[0][idx]))
+ 
+cv2.imshow("Image", image)
+cv2.waitKey(0)