Reduced time complexity using Deep Learning

C++/Algorithms/sorting.cpp

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+#include<iostream>
+using namespace std;
+void selectionSort(int a[], int n) {
+   int i, j, min, temp;
+   for (i = 0; i < n - 1; i++) {
+      min = i;
+      for (j = i + 1; j < n; j++)
+      if (a[j] < a[min])
+      min = j;
+      temp = a[i];
+      a[i] = a[min];
+      a[min] = temp;
+   }
+}
+int main() {
+   int a[] = { 22, 91, 35, 78, 10, 8, 75, 99, 1, 67 };
+   int n = sizeof(a)/ sizeof(a[0]);
+   int i;
+   cout<<"Given array is:"<<endl;
+   for (i = 0; i < n; i++)
+   cout<< a[i] <<" ";
+   cout<<endl;
+   selectionSort(a, n);
+   printf("\nSorted array is: \n");
+   for (i = 0; i < n; i++)
+   cout<< a[i] <<" ";
+   return 0;
+}

Java/Algorithms/Divide-and-Conquer/Intergrity.java

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+// Java program to calculate SHA-1 hash value
+
+import java.math.BigInteger;
+import java.security.MessageDigest;
+import java.security.NoSuchAlgorithmException;
+
+public class GFG {
+	public static String encryptThisString(String input)
+	{
+		try {
+			// getInstance() method is called with algorithm SHA-1
+			MessageDigest md = MessageDigest.getInstance("SHA-1");
+
+			// digest() method is called
+			// to calculate message digest of the input string
+			// returned as array of byte
+			byte[] messageDigest = md.digest(input.getBytes());
+
+			// Convert byte array into signum representation
+			BigInteger no = new BigInteger(1, messageDigest);
+
+			// Convert message digest into hex value
+			String hashtext = no.toString(16);
+
+			// Add preceding 0s to make it 32 bit
+			while (hashtext.length() < 32) {
+				hashtext = "0" + hashtext;
+			}
+
+			// return the HashText
+			return hashtext;
+		}
+
+		// For specifying wrong message digest algorithms
+		catch (NoSuchAlgorithmException e) {
+			throw new RuntimeException(e);
+		}
+	}
+
+	// Driver code
+	public static void main(String args[]) throws
+									NoSuchAlgorithmException
+	{
+
+		System.out.println("HashCode Generated by SHA-1 for: ");
+
+		String s1 = "GeeksForGeeks";
+		System.out.println("\n" + s1 + " : " + encryptThisString(s1));
+
+		String s2 = "hello world";
+		System.out.println("\n" + s2 + " : " + encryptThisString(s2));
+	}
+}

Python/Algorithms/Dynamic-Programming/faster_cnn.py

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+import os.path
+
+import cv2
+import numpy as np
+import requests
+import torchvision
+import torchvision.transforms as transforms
+
+print("Faster R-CNN object detection")
+
+# COCO dataset class names
+classes = [
+    'background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'street sign',
+    'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
+    'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'hat', 'backpack',
+    'umbrella', 'shoe', 'eye glasses', 'handbag', 'tie', 'suitcase', 'frisbee',
+    'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
+    'skateboard', 'surfboard', 'tennis racket', 'bottle', 'plate', 'wine glass',
+    'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
+    'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
+    'couch', 'potted plant', 'bed', 'mirror', 'dining table', 'window', 'desk',
+    'toilet', 'door', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'blender', 'book',
+    'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'hair brush']
+
+# Download object detection image
+image_file = 'source_2.png'
+if not os.path.isfile(image_file):
+    url = "https://github.com/ivan-vasilev/advanced-deep-learning-with-python/blob/master/chapter04-detection-segmentation/source_2.png"
+    r = requests.get(url)
+    with open(image_file, 'wb') as f:
+        f.write(r.content)
+
+# load the pytorch model
+model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
+
+# set the model in evaluation mode
+model.eval()
+
+# read the image file
+img = cv2.imread(image_file)
+
+# transform the input to tensor
+transform = transforms.Compose([transforms.ToPILImage(), transforms.ToTensor()])
+nn_input = transform(img)
+output = model([nn_input])
+
+# random color for each class
+colors = np.random.uniform(0, 255, size=(len(classes), 3))
+
+# iterate over the network output for all boxes
+for box, box_class, score in zip(output[0]['boxes'].detach().numpy(),
+                                 output[0]['labels'].detach().numpy(),
+                                 output[0]['scores'].detach().numpy()):
+
+    # filter the boxes by score
+    if score > 0.5:
+        # transform bounding box format
+        box = [(box[0], box[1]), (box[2], box[3])]
+
+        # select class color
+        color = colors[box_class]
+
+        # extract class name
+        class_name = classes[box_class]
+
+        # draw the bounding box
+        cv2.rectangle(img=img,
+                      pt1=box[0],
+                      pt2=box[1],
+                      color=color,
+                      thickness=2)
+
+        # display the box class label
+        cv2.putText(img=img,
+                    text=class_name,
+                    org=box[0],
+                    fontFace=cv2.FONT_HERSHEY_SIMPLEX,
+                    fontScale=1,
+                    color=color,
+                    thickness=2)
+
+cv2.imshow("Object detection", img)
+cv2.waitKey()