gsoykan

//
//  ListScrollingProxy.swift
//
//  Created by gurkan soykan on 20.05.2022.
//

// source: https://stackoverflow.com/questions/60855852/how-to-scroll-list-programmatically-in-swiftui/60855853#60855853

import Foundation
import UIKit

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import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image

# source: https://stackoverflow.com/a/37437395/8265079

x_0, y_0, x_1, y_1 = annotation_item['bounding_box']

im = Image.open('my_img.png')
# Create figure and axes

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def get_iou(a, b, epsilon=1e-5):
    """ Given two boxes `a` and `b` defined as a list of four numbers:
            [x1,y1,x2,y2]
        where:
            x1,y1 represent the upper left corner
            x2,y2 represent the lower right corner
        It returns the Intersect of Union score for these two boxes.
        source: http://ronny.rest/tutorials/module/localization_001/iou/

    Args:

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# Importing Image class from PIL module
from PIL import Image
 
# Opens a image in RGB mode
im = Image.open(r"C:\Users\Admin\Pictures\network.png")
 
# Setting the points for cropped image
left = 155
top = 65
right = 360

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ssh -C [email protected] ncdu -o- /kuacc/users/gsoykan20 | ncdu -f-

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import cv2
import numpy as np
import torch

# Create a random torch tensor
tensor = torch.randn(3, 256, 256)

# Convert the tensor to a numpy array
numpy_image = tensor.numpy()

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import pytorch_lightning as pl
from torch.optim import Adam
from torch.optim.lr_scheduler import StepLR

class MyModel(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.layer1 = nn.Linear(10, 5)
        self.layer2 = nn.Linear(5, 1)
        self.loss_fn = nn.MSELoss()

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def draw_line_in_mask(mask, start_point, end_point):
  # Extract x and y coordinates of start and end points
  x0, y0 = start_point[0], start_point[1]
  x1, y1 = end_point[0], end_point[1]

  # Compute differences between start and end points
  dx = abs(x1 - x0)
  dy = abs(y1 - y0)

  # Determine direction of the line

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def draw_line_in_mask_batched(self, mask, start_point, end_point, light_neighboring=True):
    """
    Draws a line in the given batch of masks.

    Args:
        mask (torch.Tensor): Batch of masks with shape (B, H, W)
        start_point (List of Tuples): Batch of start points with shape (B, 2)
        end_point (List of Tuples): Batch of end points with shape (B, 2)

    Returns:

gsoykan

find . -name "*.py" -exec python {} \;