LeetCode | Python solutions of LeetCode questions | Computer Vision library
kandi X-RAY | LeetCode Summary
kandi X-RAY | LeetCode Summary
| # | problem | difficulty | solution | |---|---------|------------|----------| 1 | two sum | easy | python 2 | add two numbers | medium | python 3 | longest substring without repeating characters | medium |python 4 | median of two sorted arrays | hard | python 5 | longest palindromic substring | medium | python 6 | zigzag conversion | medium | python 7 | reverse integer | easy | python 8 | string to integer (atoi) | medium | python 9 | palindrome number | easy | python 11 | container with most water | medium | python 12 | integer to roman | medium | python 13 | roman to integer | easy | python 14 | longest common prefix | easy | python 15 | 3sum | medium | python 16 | 3sum closest | medium | python 17 | letter combinations of a phone number | medium | python 18 | 4sum | medium | python 19 | remove nth node from end of list | medium | python 20 | valid parentheses | easy | python 21 | merge two sorted lists | easy | python 22 | generate parentheses | medium | python 23 | merge k sorted lists | hard | python 24 | swap nodes in pairs | medium | python 26 | remove duplicates from sorted array | easy | python 27 | remove element | easy | python 28 | implement strstr() | easy | python 29 | divide two integers | medium | python 31 | next permutation | medium | python 34 | find first and last position of element in sorted array | medium | python 35 | search insert position | easy | python 36 | valid sudoku | medium | python 37 | sudoku solver | hard | python 38 | count and say | easy | python 39 | combination sum | medium | python 40 | combination sum ii | medium | python 42 | trapping rain water | hard | python 46 | permutations | medium | python 47 | permutations ii | medium | python 49 | group anagrams | medium | python 50 | pow(x, n) | medium | python 51 | n-queens | hard | python 52 | n-queens ii | hard | python 53 | maximum subarray | easy | python 54 | spiral matrix | medium | python 56 | merge intervals | medium | python 58 | length of last word | easy | python 62 | unique paths | medium | python 63 | unique paths ii | medium | python 64 | minimum path sum | medium | python 66 | plus one | easy | python 67 | add binary | easy | python 69 | sqrt(x) | easy | python 70 | climbing stairs | easy | python 73 | set matrix zeroes | medium | python 75 | sort colors | medium | python 76 | minimum window substring | hard | python 78 | subsets | medium | python 79 | word search | medium | python 83 | remove duplicates from sorted list | easy | python 88 | merge sorted array | easy | python 90 | subsets ii | medium | python 98 | validate binary search tree | medium | python 100 | same tree | easy | python 101 | symmetric tree | easy | python 104 | maximum depth of binary tree | easy | python 107 | binary tree level order traversal ii | easy | python 108 | convert sorted array to binary search tree | easy | python 110 | balanced binary tree | easy | python 111 | minimum depth of binary tree | easy | python 112 | path sum | easy | python 113 | path sum ii | medium | python 118 | pascal's triangle | easy | python 119 | pascal's triangle ii | easy | python 121 | best time to buy and sell stock | easy | python 122 | best time to buy and sell stock ii | easy | python 123 | best time to buy and sell stock iii | hard | python 124 | binary tree maximum path sum | hard | python 125 | valid palindrome | easy | python 136 | single number | easy | python 137 | single number ii | medium | python 138 | copy list with random pointer | medium | python 139 | word break | medium | python 140 | word break ii | hard | python 141 | linked list cycle | easy | python 142 | linked list cycle ii | medium | python 146 | lru cache | hard | python 149 | max points on a line | hard | python 151 | reverse words in a string | medium | python 155 | min stack | easy | python 157 | read n characters given read4 | easy | python 159 | longest substring with at most two distinct characters | hard | python 163 | missing ranges | medium | python 167 | two sum ii - input array is sorted | easy | python 169 | majority element | easy | python 171 | excel sheet column number | easy | python 173 | binary search tree iterator | medium | python 174 | dungeon game | hard | python 186 | reverse words in a string ii | medium | python 189 | rotate array | easy | python 191 | number of 1 bits | easy | python 198 | house robber | easy | python 200 | number of islands | medium | python 203 | remove linked list elements | easy | python 204 | count primes | easy | python 205 | isomorphic strings | easy | python 206 | reverse linked list | easy | python 208 | implement trie (prefix tree) | medium | python 212 | word search ii | hard | python 213 | house robber ii | medium | python 215 | kth largest element in an array | medium | python 216 | combination sum iii | medium | python 217 | contains duplicate | easy | python 218 | the skyline problem | hard | python 219 | contains duplicate ii | easy | python 220 | contains duplicate iii | medium | python 222 | count complete tree nodes | medium | python 226 | invert binary tree | easy | python 230 | kth smallest element in a bst | medium | python 232 | implement queue using stacks | easy | python 234 | palindrome linked list | easy | python 237 | delete node in a linked list | easy | python 239 | sliding window maximum | hard | python 242 | valid anagram | easy | python 243 | shortest word distance | easy | python 246 | strobogrammatic number | easy | python 247 | strobogrammatic number ii | medium | python 250 | count univalue subtrees | medium | python 256 | paint house | easy | python 258 | add digits | easy | python 259 | 3sum smaller | medium | python 263 | ugly number | easy | python 265 | paint house ii | hard | python 266 | palindrome permutation | easy | python 268 | missing number | easy | python 269 | alien dictionary | hard | python 270 | closest binary search tree value | easy | python 271 | encode and decode strings | medium | python 276 | paint fence | easy | python 278 | first bad version | easy | python 283 | move zeroes | easy | python 284 | peeking iterator | medium | python 285 | inorder successor in bst | medium | python 288 | unique word abbreviation | medium | python 289 | game of life | medium | python 292 | nim game | easy | python 293 | flip game | easy | python 295 | find median from data stream | hard | python 297 | serialize and deserialize binary tree | easy | python 298 | binary tree longest consecutive sequence | medium | python 299 | bulls and cows | medium | python 303 | range sum query - immutable | easy | python 304 | range sum query 2d - immutable | medium | python 305 | number of islands ii | hard | python 307 | range sum query - mutable | medium | python 318 | maximum product of word lengths | medium | python 322 | coin change | medium | python 339 | nested list weight sum | easy | python 340 | longest substring with at most k distinct characters | hard | python 344 | reverse string | easy | python 345 | reverse vowels of a string | easy | python 346 | moving average from data stream | easy | python 348 | design tic-tac-toe | medium | python 349 | intersection of two arrays | easy | python 350 |
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Find the words in the board .
- Move the player .
- Return the leader for t .
- Deserialize a tree node .
- Return a list of frequent sentences .
- Find the most frequent sentences in a node .
- Insert a value into this set .
- Determine if a message should be printed .
- Compute the sum of the region between two cells .
- Return the next value .
LeetCode Key Features
LeetCode Examples and Code Snippets
Community Discussions
Trending Discussions on Computer Vision
QUESTION
The swift vision similarity feature is able to assign a number to the variance between 2 images. Where 0 variance between the images, means the images are the same. As the number increases this that there is more and more variance between the images.
What I am trying to do is turn this into a percentage of similarity. So one image is for example 80% similar to the other image. Any ideas how I could arrange the logic to accomplish this:
...ANSWER
Answered 2022-Mar-25 at 10:26It depends on how you want to scale it. If you just want the percentage you could just use Float.greatestFiniteMagnitude as the maximum value.
QUESTION
import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
...ANSWER
Answered 2022-Mar-22 at 13:26It appears that the 'visions.application' module was available in v0.7.1
https://github.com/dylan-profiler/visions/tree/v0.7.1/src/visions
But it's no longer available in v0.7.2
https://github.com/dylan-profiler/visions/tree/v0.7.2/src/visions
It also appears that the pandas_profiling project has been updated, the file summary.py no longer tries to do this import.
In summary: use visions version v0.7.1 or upgrade pandas_profiling.
QUESTION
I'm exploring Google Cloud Vision to detect handwriting in text. I see that the model is quite accurate in read handwritten text.
I'm following this guide: https://cloud.google.com/vision/docs/handwriting
Here is my question: is there a way to discover in the responses if the text is handwritten or typed?
A parameter or something in the response useful to classify images?
Here is the request:
...ANSWER
Answered 2022-Mar-01 at 00:36It seems that there's already an open discussion with the Google team to get this Feature Request addressed:
https://issuetracker.google.com/154156890
I would recommend you to comment on the Public issue tracker and indicate that "you are affected to this issue" to gain visibility and push for get this change done.
Other that that I'm unsure is that can be implemented locally.
QUESTION
I want to try out this tutorial and therefore used the code from here in order to calibrate my camera. I use this image:
The only thing I adapted was chessboard_size = (14,9)
so that it matches the corners of my image.
I don't know what I do wrong. I tried multiple chessboard pattern and cameras but still cv2.findChessboardCorners always fails detecting corners.
Any help would be highly appreciated.
ANSWER
Answered 2022-Jan-29 at 23:59Finally I could do it. I had to set chessboard_size = (12,7)
then it worked. I had to count the internal number of horizontal and vertical corners.
QUESTION
I am trying to get the RGB average inside of a non-rectangular multi-edge (closed) contour generated over a face landmark region in the frame (think of it as a face contour) from AVCaptureVideoDataOutput. I currently have the following code,
...ANSWER
Answered 2022-Jan-26 at 02:12If you could make all pixels outside of the contour transparent then you could use CIKmeans
filter with inputCount
equal 1
and the inputExtent
set to the extent of the frame to get the average color of the area inside the contour (the output of the filter will contain 1-pixel image and the color of the pixel is what you are looking for).
Now, to make all pixels transparent outside of the contour, you could do something like this:
- Create a mask image but setting all pixels inside the contour white and black outside (set background to black and fill the path with white).
- Use
CIBlendWithMask
filter where:inputBackgroundImage
is a fully transparent (clear) imageinputImage
is the original frameinputMaskImage
is the mask you created above
The output of that filter will give you the image with all pixels outside the contour fully transparent. And now you can use the CIKMeans
filter with it as described at the beginning.
BTW, if you want to play with every single of the 230 filters out there check this app out: https://apps.apple.com/us/app/filter-magic/id1594986951
UPDATE:CIFilters can only work with CIImages. So the mask image has to be a CIImage as well. One way to do that is to create a CGImage from CAShapeLayer containing the mask and then create CIImage out of it. Here is how the code could look like:
QUESTION
I am actually experimenting with the Vision Framework. I have simply an UIImageView in my Storyboard and my class is from type UIViewController. But when I try to override viewDidAppear(_ animated: Bool) I get the error message: Method does not override any method from its superclass Do anyone know what the issue is? Couldn't find anything that works for me...
...ANSWER
Answered 2022-Jan-21 at 19:37This is my complete code:
QUESTION
I'm using Vision Framework to detecting faces with iPhone's front camera. My code looks like
...ANSWER
Answered 2021-Dec-23 at 14:33For some reason, remove
QUESTION
I would like to read Japanese characters from a scanned image using swift's Vision framework. However, when I attempt to set the recognition language of VNRecognizeTextRequest
to Japanese using
request.recognitionLanguages = ["ja", "en"]
the output of my program becomes nonsensical roman letters. For each image of japanese text there is unexpected recognized text output. However, when set to other languages such as Chinese or German the text output is as expected. What could be causing the unexpected output seemingly peculiar to Japanese?
I am building from the github project here.
...ANSWER
Answered 2021-Oct-12 at 23:37As they said in WWDC 2019 video, Text Recognition in Vision Framework:
First, a prerequisite, you need to check the languages that are supported by language-based correction...
Look at supportedRecognitionLanguages
for VNRecognizeTextRequestRevision2
for “accurate” recognition, and it would appear that the supported languages are:
QUESTION
For my research project I'm trying to distinguish between hydra plant (the larger amoeba looking oranges things) and their brine shrimp feed (the smaller orange specks) so that we can automate the cleaning of petri dishes using a pipetting machine. An example of a snap image from the machine of the petri dish looks like so:
I have so far applied a circle mask and an orange color space mask to create a cleaned up image so that it's mostly just the shrimp and hydra.
There is some residual light artifacts left in the filtered image, but I have to bite the cost or else I lose the resolution of the very thin hydra such as in the top left of the original image.
I was hoping to box and label the larger hydra plants but couldn't find much applicable literature for differentiating between large and small objects of similar attributes in an image, to achieve my goal.
I don't want to approach this using ML because I don't have the manpower or a large enough dataset to make a good training set, so I would truly appreciate some easier vision processing tools. I can afford to lose out on the skinny hydra, just if I can know of a simpler way to identify the more turgid, healthy hydra from the already cleaned up image that would be great.
I have seen some content about using openCV findCountours
? Am I on the right track?
Attached is the code I have so you know what datatypes I'm working with.
...ANSWER
Answered 2021-Oct-12 at 10:58You are on the right track, but I have to be honest. Without DeepLearning you will get good results but not perfect.
That's what I managed to get using contours:
Code:
QUESTION
Assume you have a binary buffer or file which represents a 2-dimensional image.
How can you convert the binary data into a IMAQ image for further processing using LabVIEW?
...ANSWER
Answered 2021-Sep-30 at 13:54For LabVIEW users who have the NI vision library installed, there are VIs that allow for the image data of an IMAQ image to be copied from a 2D array.
For single-channel images (U8
, U16
, I16
, float
) the VI is
Vision and Motion >> Vision Utilites >> Pixel Manipulation >> IMAQ ArrayToImage.vi
For multichannel images (RGB
etc) the VI is
Vision and Motion >> Vision Utilites >> Color Utilities >> IMAQ ArrayColorToImage.vi
Example 1
An example of using the IMAQ ArrayToImage.vi
is shown in the snippet below where U16
data is read from a binary file and written to a Greyscale U16
type IMAQ image
. Please note, if the file has been created by other software than LabVIEW then it is likely that it will have to be read in little-endian format which is specified for the Read From Binary File.vi
Example 2
A similar process can be used when some driver DLL call is used to get the image data as a buffer. For example, if the driver has a function capture(unsigned short * buffer)
then the following technique could be employed where a correctly sized array is initialized before the function call using the initialize array
primitive.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install LeetCode
You can use LeetCode like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page