ivbhatt

import os

def create_code_context(directory, output_file):
    code_extensions = {
        '.py', '.java', '.cpp', '.c', '.h', '.hpp', '.js', '.ts',
        '.jsx', '.tsx', '.go', '.rs', '.rb', '.php', '.cs', '.md'
    }

    with open(output_file, 'w', encoding='utf-8') as outfile:
        for root, _, files in os.walk(directory):

ivbhatt

ivbhatt

# Output blank canvas
output = np.zeros_like(image)

# Making the blank canvas usable with Pillow
pillow_output = Image.fromarray(output)
pillow_drawer = ImageDraw.Draw(pillow_output)

for i in range(int(image_height / font_height)):
  for j in range(int(image_width / font_width)):
    # finding the bounding box for the next character to be inserted

ivbhatt

/* Our plan is to simply check if accessing the candidate key throws an exception.
 If an exception is not thrown, then key is present
 Otherwise, the key is not present in our HashMap
*/

// including everything
#include <bits/stdc++.h>
using namespace std;

bool checkIfKeyPresent (unordered_map<int, int> hashMap, int candidate_key){

ivbhatt

import sys
import cv2

OUTFILE = "output.mp4"
FRAME_FPS = 10.0    
FRAME_WIDTH = 640
FRAME_HEIGHT = 480 

WINDOW_NAME = "Output Window"

ivbhatt

# Now let's test out the model on our selected images
# We will save all our results in a directory called "results"

# First we create "results" directory if it does not exist
if not (os.path.exists("results")):
  os.mkdir("results")

# We traverse through all the files in "images"
for image_name in os.listdir("images"):
  # We process only files that have one of our ALLOWED_EXTENSIONS

ivbhatt

# Since we're using Convolutional Feature Extractors available in keras.applications; 
# We need to import them as modules
# Only un-comment ONE of the following code-block

# Uncomment below lines if you want to try out VGG-16
from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
model = VGG16(weights='imagenet', include_top = False, input_shape=(256, 256, 3))

# # Uncomment below lines if you want to try out MobileNetV2
# from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input