binary_convert_time_analysis_image_resize.py

# -*- coding: utf-8 -*-
"""Untitled7.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/121-mUurRjfF7bxXLmznJ3I35CX_WP49p
"""

import torch
import numpy as np
from PIL import Image
from torchvision import transforms

# Load the image
img = Image.open('/content/image4.jpg').convert('L')  # Convert to grayscale

# Define transformations
preprocess = transforms.Compose([
    transforms.Resize((5, 5)),  # Resize to 5x5
    transforms.ToTensor(),
])

# Apply transformations
img_tensor = preprocess(img)

# Convert to binary (assuming threshold of 0.5)
threshold = 0.5
binary_img = (img_tensor > threshold).float().byte()

# Convert binary_img to a format suitable for saving (e.g., list of lists)
binary_data = binary_img.tolist()

# Define the file path
file_path = '/content/binary value.txt'

# Write binary_data to a text file
with open(file_path, 'w') as file:
    for row in binary_data:
        file.write(' '.join(map(str, row)) + '\n')

print(f"Binary data saved to {file_path}")

from PIL import Image
import matplotlib.pyplot as plt
plt.imshow(img_tensor.squeeze().numpy(), cmap='gray')
plt.title('Resized 5x5 Image')
plt.axis('off')
plt.show()

# Step 1: Upload the text file
from google.colab import files

uploaded = files.upload()

# Step 2: Read the data from the text file
import numpy as np

# Assuming the uploaded file is named 'output.txt'
file_name = list(uploaded.keys())[0]

# Read the data from the file
with open(file_name, 'r') as file:
    lines = file.readlines()

# Extract numerical data from the lines (adjust depending on your file format)
data = []
for line in lines:
    if line.startswith("conv_out"):
        value = int(line.split('=')[1].strip())
        data.append(value)

# Convert the list to a numpy array and reshape to 3x3 matrix
matrix = np.array(data).reshape(3, 3)
print("Matrix:\n", matrix)

# Step 3: Visualize the data in grayscale
import matplotlib.pyplot as plt

# Create a heatmap to visualize the matrix in grayscale
plt.imshow(matrix, cmap='gray', interpolation='none')
plt.colorbar()
plt.title('Convolution Output (Grayscale)')
plt.show()

# Python code to read the log file and perform analysis

def read_time_log(file_path):
    with open(file_path, 'r') as file:
        lines = file.readlines()
    times = [int(line.split(":")[1].strip().split()[0]) for line in lines]
    return times

def analyze_times(times):
    total_time = sum(times)
    avg_time = total_time / len(times)
    max_time = max(times)
    min_time = min(times)
    return {
        'total_time': total_time,
        'avg_time': avg_time,
        'max_time': max_time,
        'min_time': min_time
    }

# Read the log file
times = read_time_log('time_log.txt')

# Perform analysis
analysis_results = analyze_times(times)

# Print results
print(f"Total Time: {analysis_results['total_time']} time units")
print(f"Average Time: {analysis_results['avg_time']} time units")
print(f"Max Time: {analysis_results['max_time']} time units")
print(f"Min Time: {analysis_results['min_time']} time units")

import numpy as np
import matplotlib.pyplot as plt

def read_output_matrix(file_path):
    with open(file_path, 'r') as file:
        lines = file.readlines()
    # Extract the 8-bit values and convert to integers
    values = [int(line.strip(), 2) for line in lines]
    return values

def visualize_matrix(matrix, title="Downsampled Grayscale Image"):
    plt.imshow(matrix, cmap='gray', vmin=0, vmax=255)
    plt.colorbar()
    plt.title(title)
    plt.show()

# Read the 8-bit values from the output file
values = read_output_matrix('output_matrix.txt')

# Convert the flat list to a 2x2 numpy array (assuming the downsampled matrix is 2x2)
matrix = np.array(values).reshape((2, 2))

# Visualize the 2x2 matrix as a grayscale image
visualize_matrix(matrix)