diff --git a/.gitignore b/.gitignore index 064fd1fb..736d455c 100644 --- a/.gitignore +++ b/.gitignore @@ -141,3 +141,4 @@ cython_debug/ !/PYTHON APPS/Alarm_Clock/media/image/chose_timer.png !/PYTHON APPS/Alarm_Clock/media/image/start_alarm.png +.DS_Store diff --git a/Data Structures and Algorithms/Sorting Algorithms/Cycle_Sort.py b/Data Structures and Algorithms/Sorting Algorithms/Cycle_Sort.py new file mode 100644 index 00000000..1afd985f --- /dev/null +++ b/Data Structures and Algorithms/Sorting Algorithms/Cycle_Sort.py @@ -0,0 +1,50 @@ +from typing import List + +def cycle_sort(nums: List[int]) -> int: + + writes = 0 + + for cycle_start in range(len(nums) - 1): + current = nums[cycle_start] + + # Find the target position for the current item. + target_position = cycle_start + for i in range(cycle_start + 1, len(nums)): + if nums[i] < current: + target_position += 1 + + # Skip if the item is already in the correct position. + if target_position == cycle_start: + continue + + # Handle duplicates by finding the next available position. + while current == nums[target_position]: + target_position += 1 + + nums[target_position], current = current, nums[target_position] + writes += 1 + + # Rotate the rest of the cycle. + while target_position != cycle_start: + target_position = cycle_start + for i in range(cycle_start + 1, len(nums)): + if nums[i] < current: + target_position += 1 + + while current == nums[target_position]: + target_position += 1 + + nums[target_position], current = current, nums[target_position] + writes += 1 + + return writes + + +if __name__ == "__main__": + arr = [1, 8, 3, 9, 10, 10, 2, 4] + print("Before sort:", arr) + + writes = cycle_sort(arr) + + print("After sort:", arr) + print(f"Number of writes: {writes}") diff --git a/Data Structures and Algorithms/Sorting Algorithms/Pigeonhole_Sort.py b/Data Structures and Algorithms/Sorting Algorithms/Pigeonhole_Sort.py new file mode 100644 index 00000000..11a60934 --- /dev/null +++ b/Data Structures and Algorithms/Sorting Algorithms/Pigeonhole_Sort.py @@ -0,0 +1,33 @@ +# Python program to implement Pigeonhole Sort + +def pigeonhole_sort(a): + # size of range of values in the list + # (ie, number of pigeonholes we need) + my_min = min(a) + my_max = max(a) + size = my_max - my_min + 1 + + # our list of pigeonholes + holes = [0] * size + + # Populate the pigeonholes. + for x in a: + assert type(x) is int, "integers only" + holes[x - my_min] += 1 + + # Put the elements back into the array in order. + i = 0 + for count in range(size): + while holes[count] > 0: + holes[count] -= 1 + a[i] = count + my_min + i += 1 + + +a = [8, 1, 2, 7, 4, 5, 8] +print("Sorted order is : ", end = ' ') + +pigeonhole_sort(a) + +for i in range(0, len(a)): + print(a[i], end = ' ') diff --git a/Data Structures and Algorithms/Sorting Algorithms/README.md b/Data Structures and Algorithms/Sorting Algorithms/README.md new file mode 100644 index 00000000..7ddfda4b --- /dev/null +++ b/Data Structures and Algorithms/Sorting Algorithms/README.md @@ -0,0 +1,49 @@ +# Cycle Sort Algorithm + +## Overview +Cycle Sort is a comparison-based sorting algorithm that is efficient when minimizing memory writes is important. It is an in-place sorting algorithm that rearranges the elements by identifying cycles in the permutation of elements. + +## Algorithm Explanation +The algorithm works by: +1. Identifying the correct position of each element in the array. +2. Placing the element in its correct position and replacing the element already there in the cycle. +3. Repeating the process for the remaining unsorted elements. + +## Complexity +- **Time Complexity**: + - Best, Worst, and Average Case: O(n²) (due to nested cycles). +- **Space Complexity**: O(1) (in-place sorting). + +## Usage Example +```python +from Cycle_Sort import cycle_sort + +arr = [4, 5, 3, 2, 1] +print("Original array:", arr) +writes = cycle_sort(arr) +print("Sorted array:", arr) +print("Number of writes performed:", writes) +``` +# Pigeonhole Sort Algorithm + +## Overview +Pigeonhole Sort is a sorting algorithm that works well for sorting lists where the range of values (i.e., the difference between the maximum and minimum values) is not significantly larger than the number of elements in the list. It is a non-comparison-based sorting algorithm. + +The algorithm works by placing each element into its corresponding "pigeonhole" (a slot or bucket) and then iterating through the pigeonholes in order to reconstruct the sorted list. + +## Complexity +- **Time Complexity**: + - The time complexity of Pigeonhole Sort is O(n + range), where n is the number of elements in the list and range is the difference between the maximum and minimum values. + + - This makes it efficient for lists with a small range of values. +- **Space Complexity**: The space complexity is O(range), as it requires additional space for the holes list. +- **Limitations**: Pigeonhole Sort is not suitable for lists with a large range of values, as it would require a lot of memory for the holes list. + +## Usage Example +```python +from PigeonHole_Sort import pigeonhole_sort + +arr = [4, 5, 3, 2, 1] +print("Original array:", arr) +writes = pigeonhole_sort(arr) +print("Sorted array:", arr) diff --git a/Data Structures and Algorithms/queues.py b/Data Structures and Algorithms/queues.py new file mode 100644 index 00000000..95f67510 --- /dev/null +++ b/Data Structures and Algorithms/queues.py @@ -0,0 +1,27 @@ +class Queue: + def __init__(self): + self.queue = [] + + def enqueue(self, item): + self.queue.append(item) + + def dequeue(self): + if not self.is_empty(): + return self.queue.pop(0) + return "Queue is empty" + + def is_empty(self): + return len(self.queue) == 0 + + def peek(self): + return self.queue[0] if not self.is_empty() else None + + def size(self): + return len(self.queue) + +# Example Usage +q = Queue() +q.enqueue(10) +q.enqueue(20) +print(q.dequeue()) # Output: 10 +print(q.peek()) # Output: 20 diff --git a/Data Structures and Algorithms/union_find/README.md b/Data Structures and Algorithms/union_find/README.md new file mode 100644 index 00000000..6f39cf37 --- /dev/null +++ b/Data Structures and Algorithms/union_find/README.md @@ -0,0 +1,84 @@ +# Union Find (Disjoint Set Union) - Implementation and Use + +## Table of Contents +- [Why Union Find?](#why-union-find) +- [Functions and Examples](#functions-and-examples) +- [Setup](#setup) +- [Additional Resources](#additional-resources) +- [Leetcode Questions](#leetcode-questions) + +## Why Union Find? +Union Find is a popular data structure that allows us to solve many different types of graph +problems. It works best with undirected graphs, and it allows us to figure out whether a node +is connected to another node. + +Some problems it can be used to solve: +- Find the minimum spanning tree in a graph (Kruskal's) +- Check if there is a path between two nodes +- Finding redundant edges +- Representing networks + + +## Functions and Examples +Union Find seems complex at first, but it is actually a lot easier when you understand that there are +only two functions. +- Find(n) : returns the parent of a node n +- Union(n1, n2) : connects n1 and n2 if they are not previously connected + +Let's look at an example! +```python +u = UnionFind(7) # create a UnionFind object with 7 nodes (numbered 0 to 6) + +u.union(0, 1) # connects 0 and 1 together +u.union(5, 6) # connects 5 and 6 together + +u.find(1) # returns 0, since 0 is parent of 1 +u.find(5) # returns 5, since 5 is its own parent + +u.union(1, 2) # connects 2 to the component 0-1 +u.find(2) # 2s parent is now 0 + +# Now our structure looks like this + +# 0-1-2 3 4 5-6 + +u.union(1, 6) # first we find the parents of 1 and 6 + # parents are 0, and 5 + # connect the smaller component to the bigger + # now 5's parent is 0 + +u.find(6) # now this goes: + # 6 parent is 5 -> 5 parent is 0 -> 0 is its own parent +``` + +And that's it! You can use the sample code to test different examples with Union Find. +In the code, par keeps track of the parent of each node and rank keeps track of the size of +each component. + +## Setup + +First clone the repo + > `cd union_find` to get into this folder. + > call the verify function anywhere, consider adding ``` if __name__ == '__main__'``` + > `python union_find.py` to run the demo + + You can modify the structure in the verify function and play around with it. + + ## Additional Resources + + Here are some resources I found useful when learning: + - Neetcode Graph Videos on YouTube + - William Fiset - Union Find Video on YouTube + - Union Find Medium Article by Claire Lee + - Union Find Visualizer - Visualgo + + ## Leetcode Questions + - 200 - Number of Islands + - 684 - Redundant Connection + - 695 - Max Area of an Island + - 827 - Making a Large Island + - 2316 - Count Unreachable Pairs of Nodes in an Undirected Graph + - 2421 - Maximum Score of a Good Path + - 2709 - Greatest Common Divisor Traversal + + I hope this was helpful. If there are any mistakes or issues or if you want to contribute to union find, feel free to contact me at rawateshaan0 [at] gmail [dot] com \ No newline at end of file diff --git a/Data Structures and Algorithms/union_find/union_find.py b/Data Structures and Algorithms/union_find/union_find.py new file mode 100644 index 00000000..b01f53ca --- /dev/null +++ b/Data Structures and Algorithms/union_find/union_find.py @@ -0,0 +1,88 @@ +# Basic implementation of the Union Find data structure +# Assume we have n nodes labeled from 0 to n - 1 + +class UnionFind: + def __init__(self, n): + # every node is originally its own parent + self.par = [i for i in range(n)] + # self.par = list(range(n)) -- also valid + + # every node originally is in its own + # component of size 1 - this changes during + # the union operation + self.rank = [1] * n + + def find(self, n) -> int: + ''' + Finds the parent node of n + ''' + + # can be optimized with path compression + while n != self.par[n]: + n = self.par[n] + return n + + + def union(self, n1, n2) -> bool: + ''' + Connects two nodes together if not + already connected + ''' + + # find the parent of node 1 and 2 + p1 = self.find(n1) + p2 = self.find(n2) + + # nodes are already connected + # cannot union together + if p1 == p2: + return False + + # for efficiency, make bigger component + # parent of smaller component - reduces + # number of steps we have to take in find() + + if self.rank[p1] >= self.rank[p2]: + # p2 is smaller, so when union it has a + # new parent, p1 + self.par[p2] = p1 + + # p1 gets all the nodes of p2, increasing + # its rank, or size + self.rank[p1] += self.rank[p2] + else: + self.par[p1] = p2 + self.rank[p2] += self.rank[p1] + + return True + + def nodes_connected(self, n1, n2) -> bool: + ''' + Returns if two nodes are connected + ''' + + # connected if parent is the same + return self.find(n1) == self.find(n2) + + + +def verify(): + n = 7 + u = UnionFind(n) + + # False, nodes not connected + print(u.nodes_connected(0, 1)) + + # True, just connected 0 and 1 + u.union(0, 1) + print(u.nodes_connected(0, 1)) + + # Rank is 2, includes 0 and 1 + print(u.rank[0]) + + u.union(4, 5) + u.union(1, 4) + + # True, 0 - 1 and 4 - 5 are connected + # 1 to 4 connects both components + print(u.nodes_connected(0, 5)) \ No newline at end of file diff --git a/FLASK PROJECTS/E-commerce/templates/cart.html b/FLASK PROJECTS/E-commerce/templates/cart.html index 5bcd681b..4df31577 100644 --- a/FLASK PROJECTS/E-commerce/templates/cart.html +++ b/FLASK PROJECTS/E-commerce/templates/cart.html @@ -34,6 +34,9 @@

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{{ row.name }} {{ row.quantity }} {{ row.price }} + + Pay with Stripe + {% endfor %} diff --git a/INVESTMENT_RULES/README.md b/INVESTMENT_RULES/README.md new file mode 100644 index 00000000..99198579 --- /dev/null +++ b/INVESTMENT_RULES/README.md @@ -0,0 +1,28 @@ +# List of python scripts that auomates calculation of really handful of Investment Rules. + +## 1. What Is the Rule of 72? + The Rule of 72 is a simple way to determine how long an investment will take to double given a fixed annual rate of interest. Dividing 72 by the annual rate of return gives investors a rough estimate of how many years it will take for the initial investment to duplicate itself. + +### Key takeaways + The Rule of 72 is not precise, but is a quick way to get a useful ballpark figure. + For investments without a fixed rate of return, you can instead divide 72 by the number of years you hope it will take to double your money. This will give you an estimate of the annual rate of return you’ll need to achieve that goal. + The calculation is most accurate for rates of return of about 5% to 10%. + For more precise outcomes, divide 69.3 by the rate of return. While not as easy to do in one’s head, it is more accurate. + +### How the Rule of 72 Works + For example, the Rule of 72 states that $1 invested at an annual fixed interest rate of 10% would take 7.2 years ((72 ÷ 10) = 7.2) to grow to $2. + +For more details refer https://www.investopedia.com/ask/answers/what-is-the-rule-72/ + +## 2. Real Rate of Return adjusted to Inflation + You know that investments have to do more than keep pace with inflation for you to build wealth. As Golden says, + “A dollar today is not worth a dollar in the future.” But how do you determine what your investment return is after inflation? + This equation helps you compute your real return, or your return adjusted for inflation. + + For example, if an investment returns 8 percent, and inflation is 3 percent, this is how you’d set up the problem: + [ ( 1.08 ÷ 1.03 ) - 1 ] x 100 = 4.85 percent real return + + “You’re losing to inflation every year,” says Charles Sachs, a wealth manager at Kaufman Rossin Wealth in Miami. + “Long term, inflation runs about 3 percent. So your money buys half as much in 20 years.” + + Learn more here--> https://finance.yahoo.com/news/6-investment-formulas-financial-success-172744221.html \ No newline at end of file diff --git a/INVESTMENT_RULES/inflation_adjusted_return.py b/INVESTMENT_RULES/inflation_adjusted_return.py new file mode 100644 index 00000000..6a59cd2f --- /dev/null +++ b/INVESTMENT_RULES/inflation_adjusted_return.py @@ -0,0 +1,18 @@ +Inflation_Adjsted_Return_Summary = """ +Learn More about this investment rule in README.md located in INVESTMENT_RULES folder** + """ + +print(Inflation_Adjsted_Return_Summary) + +# Get the Avg Investment Rate of Return and Avg Inflation Rate +invest_rate_return = float(input("What is expected average Rate of Return (don't use % sign): "))/100 +avg_inflration_rate = float(input("What is your avg inflation rate?: "))/100 + + +def inflation_adjusted_return(invest_rate_return, avg_inflration_rate): + # Simple formula is : ((1 + Investment return percentage) / (1 + Inflation rate percentage) - 1) x 100 + inflration_adjusted_return_val = (((1 +invest_rate_return )/(1 +avg_inflration_rate)) - 1) * 100 + return inflration_adjusted_return_val + +real_return = round(inflation_adjusted_return(invest_rate_return, avg_inflration_rate),2) +print(f"Your Actual Rate of Return adjusted to the inflation is {real_return}%. Not {invest_rate_return*100}% ") \ No newline at end of file diff --git a/INVESTMENT_RULES/rule_of_72.py b/INVESTMENT_RULES/rule_of_72.py new file mode 100644 index 00000000..f1b4f1f3 --- /dev/null +++ b/INVESTMENT_RULES/rule_of_72.py @@ -0,0 +1,11 @@ +# Get Aannual fixed interest rate +fixed_interest_rate = input("Please enter the Annual Fixed interest rate (don't use % sign): ") + + +def calculate_time_taken_to_double(fixed_interest_rate): + # A simple formula calulate the time it takes to double an investment. + time_taken = 72/float(fixed_interest_rate) + return time_taken + +time_taken_to_double = round(calculate_time_taken_to_double(fixed_interest_rate),2) +print(f"Your investment will take {time_taken_to_double} year(s) to double!") \ No newline at end of file diff --git a/MachineLearning Projects/Driver-Drowsiness-Detection/README.md b/MachineLearning Projects/Driver-Drowsiness-Detection/README.md new file mode 100644 index 00000000..1c381cbb --- /dev/null +++ b/MachineLearning Projects/Driver-Drowsiness-Detection/README.md @@ -0,0 +1,107 @@ + +# Driver Drowsiness Detection + +A system that alarms the driver as soon as it detects that the driver is becoming drowsy to prevent any accidents. + + + +## Quick Start 🚀 + +### Clone the Repository + +```sh +git clone https://github.com/adityajai25/driver-drowsiness-detection.git +``` +Then + +```sh +cd driver-drowsiness-detection +``` + + +## Dataset + +We used a dataset downloaded from Kaggle. +## Creating Virtual Environment + +Using a virtual environment isolates dependencies, manages library versions, keeps the global Python environment clean, and ensures consistent setups. + +### On Windows + +#### Creating a virtual environment: + +Open Command Prompt and navigate to the project directory + +```sh +cd project/directory/ + +``` +Create a Virtual Environment: +```sh +python -m venv env +``` +To Activate the Virtual Environment: + +```sh +.\env\Scripts\activate +``` + +### On mac/Linux + +#### Creating a virtual environment: +Open terminal and navigate to the project directory + +```sh +cd project/directory/ + +``` +Create a Virtual Environment: +```sh +python -m venv env +``` +To Activate the Virtual Environment: + +```sh +source env/bin/activate +``` + + +## Installing Required Packages + +Once the virtual environment is activated, install the required packages using the following commands: + +#### 1. Install pygame + +```sh +pip install pygame==2.4.0 +``` +#### 2. Install openCV-Python + +```sh +pip install opencv-python==4.6.0.66 +``` +#### 3. Install numpy + +```sh +pip install numpy==1.23.0 +``` +#### 4. Install keras + +```sh +pip install keras==2.12.0 +``` +#### 5. Install tensorflow + +```sh +pip install tensorflow==2.13.0 +``` + + +## Execution +After installing the packages required, the project can be executed using the following command. + +```sh +python main.py +``` + +This will initiate the application, and it may take a few moments to activate the webcam and begin detection. \ No newline at end of file diff --git a/MachineLearning Projects/Driver-Drowsiness-Detection/alarm.wav b/MachineLearning Projects/Driver-Drowsiness-Detection/alarm.wav new file mode 100644 index 00000000..f7bf38c9 Binary files /dev/null and b/MachineLearning Projects/Driver-Drowsiness-Detection/alarm.wav differ diff --git a/MachineLearning Projects/Driver-Drowsiness-Detection/dataset_new/test/Closed/_107.jpg b/MachineLearning Projects/Driver-Drowsiness-Detection/dataset_new/test/Closed/_107.jpg new file mode 100644 index 00000000..95534c11 Binary files /dev/null and b/MachineLearning Projects/Driver-Drowsiness-Detection/dataset_new/test/Closed/_107.jpg differ diff 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1 2. + <_> + 3 10 3 1 2. + <_> + + <_> + 3 2 4 4 -1. + <_> + 4 2 2 4 2. + <_> + + <_> + 18 3 2 3 -1. + <_> + 18 4 2 1 3. + <_> + + <_> + 6 16 3 3 -1. + <_> + 6 17 3 1 3. + <_> + + <_> + 1 16 6 3 -1. + <_> + 1 17 6 1 3. + diff --git a/MachineLearning Projects/Driver-Drowsiness-Detection/main.py b/MachineLearning Projects/Driver-Drowsiness-Detection/main.py new file mode 100644 index 00000000..daa11f1a --- /dev/null +++ b/MachineLearning Projects/Driver-Drowsiness-Detection/main.py @@ -0,0 +1,106 @@ +import cv2 +import os +from keras.models import load_model +import numpy as np +from pygame import mixer +import time + +import os +print(os.path.abspath('haar cascade files/haarcascade_frontalface_alt.xml')) + + +mixer.init() +sound = mixer.Sound('alarm.wav') + + +face = cv2.CascadeClassifier('haar_cascade_files/haarcascade_frontalface_alt.xml') +leye = cv2.CascadeClassifier('haar_cascade_files/haarcascade_lefteye_2splits.xml') +reye = cv2.CascadeClassifier('haar_cascade_files/haarcascade_righteye_2splits.xml') + + +lbl=['Close','Open'] +model = load_model('models/cnnCat2.h5') +path = os.getcwd() +cap = cv2.VideoCapture(0) +font = cv2.FONT_HERSHEY_COMPLEX_SMALL +count=0 +score=0 +thicc=2 +rpred=[99] +lpred=[99] + +while(True): + ret, frame = cap.read() + height,width = frame.shape[:2] + + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + + faces = face.detectMultiScale(gray,minNeighbors=5,scaleFactor=1.1,minSize=(25,25)) + left_eye = leye.detectMultiScale(gray) + right_eye = reye.detectMultiScale(gray) + + cv2.rectangle(frame, (0,height-50) , (200,height) , (0,0,0) , thickness=cv2.FILLED ) + + for (x,y,w,h) in faces: + cv2.rectangle(frame, (x,y) , (x+w,y+h) , (100,100,100) , 1 ) + + for (x,y,w,h) in right_eye: + r_eye=frame[y:y+h,x:x+w] + count=count+1 + r_eye = cv2.cvtColor(r_eye,cv2.COLOR_BGR2GRAY) + r_eye = cv2.resize(r_eye,(24,24)) + r_eye= r_eye/255 + r_eye= r_eye.reshape(24,24,-1) + r_eye = np.expand_dims(r_eye,axis=0) + rpred = np.argmax(model.predict(r_eye), axis=-1) + if(rpred[0]==1): + lbl='Open' + if(rpred[0]==0): + lbl='Closed' + break + + for (x,y,w,h) in left_eye: + l_eye=frame[y:y+h,x:x+w] + count=count+1 + l_eye = cv2.cvtColor(l_eye,cv2.COLOR_BGR2GRAY) + l_eye = cv2.resize(l_eye,(24,24)) + l_eye= l_eye/255 + l_eye=l_eye.reshape(24,24,-1) + l_eye = np.expand_dims(l_eye,axis=0) + lpred = np.argmax(model.predict(l_eye), axis=-1) + if(lpred[0]==1): + lbl='Open' + if(lpred[0]==0): + lbl='Closed' + break + + if(rpred[0]==0 and lpred[0]==0): + score=score+1 + cv2.putText(frame,"Closed",(10,height-20), font, 1,(255,255,255),1,cv2.LINE_AA) + # if(rpred[0]==1 or lpred[0]==1): + else: + score=score-1 + cv2.putText(frame,"Open",(10,height-20), font, 1,(255,255,255),1,cv2.LINE_AA) + + + if(score<0): + score=0 + cv2.putText(frame,'Score:'+str(score),(100,height-20), font, 1,(255,255,255),1,cv2.LINE_AA) + if(score>15): + cv2.imwrite(os.path.join(path,'image.jpg'),frame) + try: + sound.play() + except: + pass + if(thicc<16): + thicc= thicc+2 + else: + thicc=thicc-2 + if(thicc<2): + thicc=2 + cv2.rectangle(frame,(0,0),(width,height),(0,0,255),thicc) + cv2.imshow('frame',frame) + if cv2.waitKey(1) & 0xFF == ord('q'): + break +cap.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git a/MachineLearning Projects/Driver-Drowsiness-Detection/model.py b/MachineLearning Projects/Driver-Drowsiness-Detection/model.py new file mode 100644 index 00000000..7cc62c64 --- /dev/null +++ b/MachineLearning Projects/Driver-Drowsiness-Detection/model.py @@ -0,0 +1,48 @@ +import os +from keras.preprocessing import image +import matplotlib.pyplot as plt +import numpy as np +from keras.utils.np_utils import to_categorical +import random,shutil +from keras.models import Sequential +from keras.layers import Dropout,Conv2D,Flatten,Dense, MaxPooling2D, BatchNormalization +from keras.models import load_model + + +def generator(dir, gen=image.ImageDataGenerator(rescale=1./255), shuffle=True,batch_size=1,target_size=(24,24),class_mode='categorical' ): + + return gen.flow_from_directory(dir,batch_size=batch_size,shuffle=shuffle,color_mode='grayscale',class_mode=class_mode,target_size=target_size) + +BS= 32 +TS=(24,24) +train_batch= generator('data/train',shuffle=True, batch_size=BS,target_size=TS) +valid_batch= generator('data/valid',shuffle=True, batch_size=BS,target_size=TS) +SPE= len(train_batch.classes)//BS +VS = len(valid_batch.classes)//BS +print(SPE,VS) + + +model = Sequential([ + Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(24,24,1)), + MaxPooling2D(pool_size=(1,1)), + Conv2D(32,(3,3),activation='relu'), + MaxPooling2D(pool_size=(1,1)), + Conv2D(64, (3, 3), activation='relu'), + MaxPooling2D(pool_size=(1,1)), + Dropout(0.25), + Flatten(), + Dense(128, activation='relu'), + Dropout(0.5), + Dense(2, activation='softmax') +]) + +model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy']) + +model.fit_generator(train_batch, validation_data=valid_batch,epochs=15,steps_per_epoch=SPE ,validation_steps=VS) + +<<<<<<< HEAD +model.save('models/cnnCat2.h5', overwrite=True) +======= +model.save('models/cnnCat2.h5', overwrite=True) +model = load_model('model.h5') +>>>>>>> 79e42d9 (Updated) diff --git a/MachineLearning Projects/Driver-Drowsiness-Detection/models/cnnCat2.h5 b/MachineLearning Projects/Driver-Drowsiness-Detection/models/cnnCat2.h5 new file mode 100644 index 00000000..f89cd07d Binary files /dev/null and b/MachineLearning Projects/Driver-Drowsiness-Detection/models/cnnCat2.h5 differ diff --git a/MachineLearning Projects/sudoku_solver/README.md b/MachineLearning Projects/sudoku_solver/README.md new file mode 100644 index 00000000..4829dc59 --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/README.md @@ -0,0 +1,28 @@ +# Sudoku Solver + +* This app was built to allow users to solve their sudokus using a computer. +* There is a Flask based webserver `web_interface.py` which when run gives a web interface to upload an image of a sudoku to be solved. The response is a solved sudoku. +* There is a file `full_stack_http.py` which needs to be run alongside the webserver for the full app to run. This is in charge of opening multiple process channels to process the images that are sent to the webserver. +* The app relies of Pytesseract to identify the characters in the sudoku image. + +# Operation + +* The image is first stripped of color. +* It is then cropped to select the section of the sudoku. NOTE: This section is not dependent on the sudoku but has been hardcoded. +* The resulting image is passed to `Pytesseract` to extract the characters and their position. +* Using the characters and their position the grid size is determined. +* The appropriate grid is created and filled with the discovered characters. +* The grid is then solved with an algorithm contained in `sudoku.py`. +* A snapshot of the solved grid is then created and sent back to the user. +* The resultant snapshot is rendered on the browser page. + +# To Run + +* First install `Pytesseract` +* Install `Flask` +* Then run the `full_stack_http.py` file. +* Then run the `web_interface.py` file. +* Go to the browser and load the URL provided in the previous step. +* Click the upload button. +* Select your image and submit the form. +* Wait for the result to be loaded. \ No newline at end of file diff --git a/MachineLearning Projects/sudoku_solver/__pycache__/image.cpython-311.pyc b/MachineLearning Projects/sudoku_solver/__pycache__/image.cpython-311.pyc new file mode 100644 index 00000000..bc46b3c9 Binary files /dev/null and b/MachineLearning Projects/sudoku_solver/__pycache__/image.cpython-311.pyc differ diff --git a/MachineLearning Projects/sudoku_solver/__pycache__/perspective.cpython-312.pyc b/MachineLearning Projects/sudoku_solver/__pycache__/perspective.cpython-312.pyc new file mode 100644 index 00000000..73b55c0f Binary files /dev/null and b/MachineLearning Projects/sudoku_solver/__pycache__/perspective.cpython-312.pyc differ diff --git a/MachineLearning Projects/sudoku_solver/__pycache__/sudoku.cpython-312.pyc b/MachineLearning Projects/sudoku_solver/__pycache__/sudoku.cpython-312.pyc new file mode 100644 index 00000000..d76e94c2 Binary files /dev/null and b/MachineLearning Projects/sudoku_solver/__pycache__/sudoku.cpython-312.pyc differ diff --git a/MachineLearning Projects/sudoku_solver/config.cfg b/MachineLearning Projects/sudoku_solver/config.cfg new file mode 100644 index 00000000..93d8c2b5 --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/config.cfg @@ -0,0 +1,4 @@ +UPLOAD_FOLDER="uploads" +SECRET_KEY="secret" +SOLVER_IP="localhost" +SOLVER_PORT=3535 \ No newline at end of file diff --git a/MachineLearning Projects/sudoku_solver/f1.jpg b/MachineLearning Projects/sudoku_solver/f1.jpg new file mode 100644 index 00000000..12c2702e Binary files /dev/null and b/MachineLearning Projects/sudoku_solver/f1.jpg differ diff --git a/MachineLearning Projects/sudoku_solver/f2.jpg b/MachineLearning Projects/sudoku_solver/f2.jpg new file mode 100644 index 00000000..dc232b54 Binary files /dev/null and b/MachineLearning Projects/sudoku_solver/f2.jpg differ diff --git a/MachineLearning Projects/sudoku_solver/full_stack_http.py b/MachineLearning Projects/sudoku_solver/full_stack_http.py new file mode 100644 index 00000000..d6232b81 --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/full_stack_http.py @@ -0,0 +1,136 @@ +import multiprocessing.util +import socket +from perspective import resolve_image +from sudoku import Grid +import argparse +import multiprocessing +import os + +temp_result_file = "resultfile.png" +temp_input_file = "tempfile.jpg" + +def process_handle_transaction(proc_num:int, sock:socket.socket): + print(f"[{proc_num}] Waiting for client...") + sock2, address2 = sock.accept() + print(f"[{proc_num}] Connected to client with address: {address2}") + sock2.settimeout(1) + rec_buf = b'' + split = temp_input_file.split('.') + my_temp_input_file = ".".join(i for i in split[:-1]) + str(proc_num) + "." + split[-1] + split = temp_result_file.split('.') + my_temp_result_file = ".".join(i for i in split[:-1]) + str(proc_num) + "." + split[-1] + try: + while True: + try: + rec = sock2.recv(1) + rec_buf += rec + if len(rec) == 0: + print(f"[{proc_num}] Lost connection") + break + except socket.timeout: + with open(my_temp_input_file, "wb") as f: + f.write(rec_buf) + rec_buf = b'' + grid_size, points = resolve_image(my_temp_input_file) + grid = Grid(rows=grid_size[0], columns=grid_size[1]) + assignment_values = {} + for val,loc in points: + assignment_values[loc] = val + grid.preassign(assignment_values) + grid.solve() + grid.save_grid_image(path=my_temp_result_file, size=(400,400)) + with open(my_temp_result_file, "rb") as f: + sock2.send(f.read()) + os.remove(my_temp_input_file) + os.remove(my_temp_result_file) + sock2.close() + print(f"[{proc_num}] Finished!") + break + finally: + sock2.close() + +class Manager(): + def __init__(self, address:tuple[str,int]): + self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + self.address = address + + def wait_for_connect(self): + print("Waiting for client...") + self.sock2, self.address2 = self.sock.accept() + print(f"Connected to client with address: {self.address2}") + self.sock2.settimeout(1) + + def run(self): + self.sock.bind(self.address) + self.sock.listen() + print(f"Listening from address: {self.address}") + try: + while True: + self.wait_for_connect() + rec_buf = b'' + while True: + try: + rec = self.sock2.recv(1) + rec_buf += rec + if len(rec) == 0: + print("Lost connection") + break + except socket.timeout: + with open(temp_input_file, "wb") as f: + f.write(rec_buf) + rec_buf = b'' + grid_size, points = resolve_image(temp_input_file) + grid = Grid(rows=grid_size[0], columns=grid_size[1]) + assignment_values = {} + for val,loc in points: + assignment_values[loc] = val + grid.preassign(assignment_values) + grid.solve() + grid.save_grid_image(path=temp_result_file, size=(400,400)) + with open(temp_result_file, "rb") as f: + self.sock2.send(f.read()) + os.remove(temp_input_file) + os.remove(temp_result_file) + self.sock2.close() + break + finally: + try: + self.sock2.close() + except socket.error: + pass + except AttributeError: + pass + self.sock.close() + + def run_multiprocessing(self, max_clients:int=8): + self.sock.bind(self.address) + self.sock.listen() + print(f"Listening from address: {self.address}") + processes:dict[int,multiprocessing.Process]= {} + proc_num = 0 + try: + while True: + if len(processes) <= max_clients: + proc = multiprocessing.Process(target=process_handle_transaction, args=(proc_num, self.sock)) + proc.start() + processes[proc_num] = proc + proc_num += 1 + proc_num%=(max_clients*2) + keys = list(processes.keys()) + for proc_n in keys: + if not processes[proc_n].is_alive(): + processes.pop(proc_n) + finally: + if len(processes): + for proc in processes.values(): + proc.kill() + self.sock.close() + +if "__main__" == __name__: + parser = argparse.ArgumentParser() + parser.add_argument("--port", type=int, default=3535, help="The port to host the server.") + parser.add_argument("--host", type=str, default="localhost", help="The host or ip-address to host the server.") + args = parser.parse_args() + address = (args.host, args.port) + manager = Manager(address) + manager.run_multiprocessing(max_clients=multiprocessing.cpu_count()) \ No newline at end of file diff --git a/MachineLearning Projects/sudoku_solver/image.py b/MachineLearning Projects/sudoku_solver/image.py new file mode 100644 index 00000000..24ca83d4 --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/image.py @@ -0,0 +1,141 @@ +import torch +from torch.utils.data import Dataset, DataLoader +import PIL.Image as Image +import pandas as pd +from tqdm import tqdm +import numpy as np + + +class SudokuDataset(Dataset): + def __init__(self, grid_locations_file:str, input_shape:tuple[int, int]) -> None: + super().__init__() + self.grid_locations = [] + self.image_filenames = [] + self.input_shape = input_shape + self.all_data = pd.read_csv(grid_locations_file, header=0) + self.image_filenames = list(self.all_data['filepath'].to_numpy()) + self.grid_locations = [list(a[1:]) for a in self.all_data.values] + to_pop = [] + for i,file in enumerate(self.image_filenames): + try: + Image.open(file) + except FileNotFoundError: + to_pop.append(i) + print(f"{file} not found.") + for i in reversed(to_pop): + self.image_filenames.pop(i) + self.grid_locations.pop(i) + # print(self.all_data.columns) + # print(self.grid_locations) + + def __len__(self) -> int: + return len(self.image_filenames) + + def __getitem__(self, index) -> dict[str, torch.Tensor]: + image = Image.open(self.image_filenames[index]).convert("L") + size = image.size + image = image.resize(self.input_shape) + image = np.array(image) + image = image.reshape((1,*image.shape)) + location = self.grid_locations[index] + for i in range(len(location)): + if i%2: + location[i] /= size[1] + else: + location[i] /= size[0] + return { + "image": torch.tensor(image, dtype=torch.float32)/255., + "grid": torch.tensor(location, dtype=torch.float32) + } + +class Model(torch.nn.Module): + def __init__(self, input_shape:tuple[int,int], number_of_layers:int, dims:int, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.input_shape = input_shape + self.conv_layers:list = [] + self.conv_layers.append(torch.nn.Conv2d(1, dims, (3,3), padding='same')) + for _ in range(number_of_layers-1): + self.conv_layers.append(torch.nn.Conv2d(dims, dims, (3,3), padding='same')) + self.conv_layers.append(torch.nn.LeakyReLU(negative_slope=0.01)) + self.conv_layers.append(torch.nn.MaxPool2d((2,2))) + self.conv_layers.append(torch.nn.BatchNorm2d(dims)) + self.flatten = torch.nn.Flatten() + self.location = [ + torch.nn.Linear(4107, 8), + torch.nn.Sigmoid() + ] + self.conv_layers = torch.nn.ModuleList(self.conv_layers) + self.location = torch.nn.ModuleList(self.location) + + def forward(self, x:torch.Tensor) -> torch.Tensor: + for layer in self.conv_layers: + x = layer(x) + x = self.flatten(x) + location = x + for layer in self.location: + location = layer(location) + return location + +def create_model(input_shape:tuple[int,int], number_of_layers:int, dims:int): + model = Model(input_shape, number_of_layers, dims) + for p in model.parameters(): + if p.dim() > 1: + torch.nn.init.xavier_uniform_(p) + return model + +def get_dataset(filename:str, input_shape:tuple[int,int], batch_size:int) -> DataLoader: + train_dataset = SudokuDataset(filename, input_shape) + train_dataloader = DataLoader(train_dataset, batch_size, shuffle=True) + return train_dataloader + +def train(epochs:int, config:dict, model:None|Model = None) -> Model: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + if not model: + print("========== Using new model =========") + model = create_model(config['input_shape'], config['number_of_layers'], config['dims']).to(device) + optimizer = torch.optim.Adam(model.parameters(), lr=config['lr']) + loss = torch.nn.MSELoss().to(device) + dataset = get_dataset(config['filename'], config['input_shape'], config['batch_size']) + prev_error = 0 + try: + for epoch in range(1, epochs+1): + batch_iterator = tqdm(dataset, f"Epoch {epoch}/{epochs}:") + for batch in batch_iterator: + x = batch['image'].to(device) + y_true = batch['grid'].to(device) + # print(batch['grid']) + # return + y_pred = model(x) + error = loss(y_true, y_pred) + batch_iterator.set_postfix({"loss":f"Loss: {error.item():6.6f}"}) + error.backward() + optimizer.step() + # optimizer.zero_grad() + if abs(error-0.5) < 0.05:# or (prev_error-error)<0.000001: + del(model) + model = create_model(config['input_shape'], config['number_of_layers'], config['dims']).to(device) + print("New model created") + prev_error = error + except KeyboardInterrupt: + torch.save(model, "model.pt") + return model + +def test(config:dict, model_filename:str): + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + model = torch.load("model.pt").to(device) + loss = torch.nn.MSELoss().to(device) + dataset = get_dataset(config['filename'], config['input_shape'], config['batch_size']) + + +if __name__ == '__main__': + config = { + "input_shape": (300,300), + "filename": "archive/outlines_sorted.csv", + "number_of_layers": 4, + "dims": 3, + "batch_size": 8, + "lr": 1e-5 + } + # model = train(50, config) + model = torch.load("model.pt") + test(config, model) \ No newline at end of file diff --git a/MachineLearning Projects/sudoku_solver/model.pt b/MachineLearning Projects/sudoku_solver/model.pt new file mode 100644 index 00000000..ba421a06 Binary files /dev/null and b/MachineLearning Projects/sudoku_solver/model.pt differ diff --git a/MachineLearning Projects/sudoku_solver/perspective.py b/MachineLearning Projects/sudoku_solver/perspective.py new file mode 100644 index 00000000..3c79c78d --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/perspective.py @@ -0,0 +1,98 @@ +import cv2 +import numpy as np +from pytesseract import pytesseract as pt + +def resolve_perspective(source_image:np.ndarray, points:np.ndarray, target_shape:tuple[int,int]) -> np.ndarray: + """Takes an source image and transforms takes the region demarkated by points and creates a rectangular image of target. + + Args: + source_image (np.ndarray): the source image. + points (np.ndarray): a numpy array of 4 points that will demarkate the vertices of the region to be transformed.\n + \tShould be in the form of points from the point that would be transformed to the top left of the rectangle, clockwise + target_shape (tuple[int,int]): the target shape of the rectangular output image. Format [height, width]. + + Returns: + np.ndarray: the output image transformed + """ + output_points:np.ndarray = np.array([ + [0,0], + [target_shape[0]-1, 0], + [target_shape[0]-1, target_shape[1]-1], + [0,target_shape[1]-1] + ], dtype=np.float32) + transformation_matrix:cv2.typing.MatLike = cv2.getPerspectiveTransform(points.astype(np.float32), output_points) + output:cv2.typing.MatLike = cv2.warpPerspective(source_image, transformation_matrix, (target_shape[1], target_shape[0]), flags=cv2.INTER_LINEAR) + return output + +def get_grid_size(image:np.ndarray, boxes:list[list[int]], allowed_sizes:list[tuple[int,int]]=[(2,3),(3,3),(4,4)]) -> tuple[int,int]: + h,w = image.shape + for size in allowed_sizes: + s1 = float(w)/float(size[0]) + s2 = float(h)/float(size[1]) + for box in boxes: + _,x1,y1,x2,y2 = box + if (abs(int(x1/s1) - int(x2/s1)) + abs(int((h - y1)/s2) - int((h - y2)/s2))) > 0: + break + else: + return size + +def get_points(image:np.ndarray, boxes:list[list[int]], grid_size:tuple[int,int]) -> list[tuple[int,tuple]]: + h,w = image.shape + size = grid_size[0] * grid_size[1] + s1 = float(w)/float(size) + s2 = float(h)/float(size) + results = [] + for box in boxes: + val,x1,y1,x2,y2 = box + center_x = int((x1+x2)/2) + center_y = int((y1+y2)/2) + results.append((val, (int((h-center_y)/s2), int(center_x/s1)))) + return results + +def resolve_image(path:str) -> tuple[tuple,list[tuple[int,tuple]]]: + # img = cv2.imread("images/image210.jpg") + img = cv2.imread(path) + numbers = [str(i) for i in range(10)] + max_size = 500 + min_area = 150 + *img_shape,_ = img.shape + max_ind = np.argmax(img_shape) + min_ind = np.argmin(img_shape) + next_shape = [0,0] + if max_ind != min_ind: + next_shape[max_ind] = max_size + next_shape[min_ind] = int(img_shape[min_ind]*max_size/img_shape[max_ind]) + else: + next_shape = [max_size, max_size] + img = cv2.resize(img, tuple(reversed(next_shape))) + points = np.array([6,97,219,99,216,309,7,310]) + points = points.reshape((4,2)) + target_shape = (400,400) + output = resolve_perspective(img, points, target_shape) + output = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY) + norm_img = np.zeros((output.shape[0], output.shape[1])) + output = cv2.normalize(output, norm_img, 0, 255, cv2.NORM_MINMAX) + output1 = cv2.threshold(output, 140, 255, cv2.THRESH_BINARY_INV)[1] + if np.average(output1.flatten()) > 128: + output = cv2.threshold(output, 140, 255, cv2.THRESH_BINARY)[1] + else: + output = output1 + output = cv2.GaussianBlur(output, (1,1), 0) + boxes = pt.image_to_boxes(output, "eng", config=r'-c tessedit_char_whitelist=0123456789 --psm 13 --oem 3') + print(boxes) + h,w = output.shape + new_boxes_str = "" + new_boxes = [] + for bt in boxes.splitlines(): + b = bt.split(' ') + area = (int(b[1]) - int(b[3]))*(int(b[2]) - int(b[4])) + if b[0] in numbers and area > min_area: + output = cv2.rectangle(output, (int(b[1]), h - int(b[2])), (int(b[3]), h - int(b[4])), (255, 255, 255), 2) + new_boxes_str += bt + "\n" + new_boxes.append(list(int(i) for i in b[:5])) + grid_size = get_grid_size(output, new_boxes) + final_points = get_points(output, new_boxes, grid_size) + return grid_size,final_points + +if "__main__" == __name__: + print(resolve_image("f2.jpg")) \ No newline at end of file diff --git a/MachineLearning Projects/sudoku_solver/resultfile2_server.png b/MachineLearning Projects/sudoku_solver/resultfile2_server.png new file mode 100644 index 00000000..d6af2f3c Binary files /dev/null and b/MachineLearning Projects/sudoku_solver/resultfile2_server.png differ diff --git a/MachineLearning Projects/sudoku_solver/sudoku.py b/MachineLearning Projects/sudoku_solver/sudoku.py new file mode 100644 index 00000000..fa5dc225 --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/sudoku.py @@ -0,0 +1,373 @@ +"""This python script contains a class to solve a sudoku. +""" + +from copy import deepcopy +import pygame as pg + +VISITED_COLOR = (50,50,50) +AGENT_COLOR = (255,0,0) +BOARD_COLOR = (0,0,0) +WALL_COLOR = (255,255,255) +SOLUTION_COLOR = (0,255,0) +START_CELL_COLOR = (200,0,200) +END_CELL_COLOR = (0,128,128) + +SIZE = (600,600) + +class Cell(): + """Cell element of sudoku. + """ + def __init__(self, name:int|str, domain:list[int]) -> None: + """Initialise a cell of the sudoku. + + Args: + name (int): the actual cell position. + domain (list[int]): list of all the possible values the cell can take. + """ + self.name = name + self.value:int|str = None + self.domain:list[int] = deepcopy(domain) + +class Grid(): + """The actual sudoku grid. + """ + def __init__(self, rows:int|None = None, columns:int|None = None) -> None: + """Initialise the sudoku grid. + + Args: + rows (int | None, optional): The number of rows in a block eg 3 for a 9x9 sudoku. Defaults to None. + columns (int | None, optional): The number of columns in a block. Defaults to None. + """ + self.rows = rows + self.columns = columns + if not self.rows or not self.columns: + return + self.grid_len = self.rows * self.columns + self.domain:list[int] = [i for i in range(1, min(10, self.grid_len+1))] + if self.grid_len >= 10: + self.domain.extend(chr(ord('A') + i - 10) for i in range(10, self.grid_len+1)) + self.cells:list[Cell] = [Cell(i, self.domain) for i in range(self.grid_len * self.grid_len)] + self.unsolved_cells:list[int] = [i for i in range(self.grid_len * self.grid_len)] + self.solved_cells:list[int] = [] + self.initial_solved:list[int] = [] + self.initial_unsolved:list[int] = [i for i in range(self.grid_len * self.grid_len)] + + def preassign(self, values:dict[tuple, int]) -> None: + """Preassigns particular value to the cells already given in the problem. + + Args: + values (dict[tuple, int]): a dictionary with keys of the (row,column) and value of the actual value of the cell. + """ + for i, value in values.items(): + number = int(i[0]*self.grid_len + i[1]) + if number in self.initial_solved: + self.unassign_last(number) + self.cells[number].value = value + self.cells[number].domain = [] + self.unsolved_cells.remove(number) + self.initial_unsolved.remove(number) + self.initial_solved.append(number) + self.solved_cells.append(number) + + def unassign_last(self, number:int|None = None): + """Unassigns either the last value assigned to a cell or a particular cell given by number. + + Args: + number (int | None, optional): The number of the cell in the grid, starting from 0 at the top right and moving left. Defaults to None. + """ + if not number: + number = self.solved_cells.pop() + self.initial_solved.pop() + else: + self.solved_cells.remove(number) + self.initial_solved.remove(number) + self.unsolved_cells.append(number) + self.initial_unsolved.append(number) + self.cells[number].domain = deepcopy(self.domain) + self.cells[number].value = None + + def solve(self) -> None: + """Tries to solve the sudoku. + """ + while len(self.unsolved_cells) > 0: + changed = False + i = 0 + # first update domains based on known cells + while i < len(self.solved_cells): + val = self.cells[self.solved_cells[i]].value + r,c = int(self.solved_cells[i]/self.grid_len), int(self.solved_cells[i]%self.grid_len) + # first check cells on the same row + for j in range(r*self.grid_len, (r+1)*self.grid_len): + try: + self.cells[j].domain.remove(val) + if len(self.cells[j].domain) == 1: + self.cells[j].value = self.cells[j].domain[0] + self.cells[j].domain = [] + self.unsolved_cells.remove(j) + self.solved_cells.append(j) + changed = True + i = -1 + except ValueError: + pass + # next check cells on the same column + for k in range(self.grid_len): + j = k*self.grid_len + c + try: + self.cells[j].domain.remove(val) + if len(self.cells[j].domain) == 1: + self.cells[j].value = self.cells[j].domain[0] + self.cells[j].domain = [] + self.unsolved_cells.remove(j) + self.solved_cells.append(j) + changed = True + i = -1 + except ValueError: + pass + # next check cells on the same block + br = int(r/self.rows) + bc = int(c/self.columns) + for k in range(self.grid_len): + cr = br*self.rows + int(k/self.columns) + cc = bc*self.columns + int(k%self.columns) + j = cr*self.grid_len + cc + try: + self.cells[j].domain.remove(val) + if len(self.cells[j].domain) == 1: + self.cells[j].value = self.cells[j].domain[0] + self.cells[j].domain = [] + self.unsolved_cells.remove(j) + self.solved_cells.append(j) + changed = True + i = -1 + except ValueError: + pass + i += 1 + # next check for unique value in domains of cells in row column or block + # first check rows + to_break = False + for k in range(self.grid_len): + values:dict[int|str, list[int]] = {val:[] for val in self.domain} + for m in range(self.grid_len): + j = k*self.grid_len + m + for v in self.cells[j].domain: + values[v].append(j) + for val,ls in values.items(): + if len(ls) == 1: + self.cells[ls[0]].value = val + self.cells[ls[0]].domain = [] + self.unsolved_cells.remove(ls[0]) + self.solved_cells.append(ls[0]) + to_break = True + break + if to_break: + break + if to_break: + continue + # first check columns + to_break = False + for k in range(self.grid_len): + values:dict[int|str, list[int]] = {val:[] for val in self.domain} + for m in range(self.grid_len): + j = m*self.grid_len + k + for v in self.cells[j].domain: + values[v].append(j) + for val,ls in values.items(): + if len(ls) == 1: + self.cells[ls[0]].value = val + self.cells[ls[0]].domain = [] + self.unsolved_cells.remove(ls[0]) + self.solved_cells.append(ls[0]) + to_break = True + break + if to_break: + break + if to_break: + continue + if not changed: + return + + def render_cells(self, window:pg.Surface) -> None: + """Draws the grid and populates it with the value of the cells. + + Args: + window (pg.Surface): a pygame window to be used to populate the grid and cells. + """ + size = window.get_size() + py = int(size[1] / self.grid_len) + px = int(size[0] / self.grid_len) + ball = pg.Rect(0, 0, size[0], size[1]) + pg.draw.rect(window, BOARD_COLOR, ball) + for i in range(self.grid_len+1): + if i%self.columns: + pg.draw.line(window, VISITED_COLOR, (i*px, 0), (i*px, size[1])) + else: + pg.draw.line(window, WALL_COLOR, (i*px, 0), (i*px, size[1])) + if i%self.rows: + pg.draw.line(window, VISITED_COLOR, (0, i*py), (size[0], i*py)) + else: + pg.draw.line(window, WALL_COLOR, (0, i*py), (size[0], i*py)) + font = pg.font.SysFont(None, min(py, px)) + for i in self.initial_solved: + text = font.render(str(self.cells[i].value), True, AGENT_COLOR, BOARD_COLOR) + textRect = text.get_rect() + y = int(i/self.grid_len) + x = int(i%self.grid_len) + textRect.center = (int((x+0.5)*px),int((y+0.5)*py)) + window.blit(text, textRect) + for i in self.initial_unsolved: + if val:=self.cells[i].value: + text = font.render(str(val), True, SOLUTION_COLOR, BOARD_COLOR) + textRect = text.get_rect() + y = int(i/self.grid_len) + x = int(i%self.grid_len) + textRect.center = (int((x+0.5)*px),int((y+0.5)*py)) + window.blit(text, textRect) + # else: + # for dv in self.cells[i].domain: + # text = font.render(str(val), True, SOLUTION_COLOR, BOARD_COLOR) + # textRect = text.get_rect() + # y = int(i/self.grid_len) + # x = int(i%self.grid_len) + # textRect.center = (int((x+0.5)*px),int((y+0.5)*py)) + # window.blit(text, textRect) + + def render_grid(self, size:tuple[int, int]=SIZE) -> None: + """Creates the grid window and renders it. + + Args: + size (tuple[int, int], optional): The size of the window to be used. Defaults to (600,600). + """ + pg.init() + window = pg.display.set_mode(size) + window.fill(BOARD_COLOR) + while True: + for event in pg.event.get(): + if event.type == pg.QUIT: + pg.display.quit() + return + self.render_cells(window) + pg.display.update() + + def input_to_grid(self, size:tuple[int, int]=SIZE) -> None: + """Allows for input of the value of the grid cells by clicking on a cell and typing the value. + + Args: + size (tuple[int, int], optional): The size of the window to which the grid will be rendered. Defaults to (600,600). + """ + pg.init() + window = pg.display.set_mode(size) + window.fill(BOARD_COLOR) + size = window.get_size() + py = int(size[1] / self.grid_len) + px = int(size[0] / self.grid_len) + clicked_cell = None + while True: + for event in pg.event.get(): + if event.type == pg.QUIT: + pg.display.quit() + return + if event.type == pg.MOUSEBUTTONUP: + clicked_cell = event.dict['pos'] + if event.type == pg.KEYDOWN: + key = event.dict['unicode'] + if key >= '0' and key <= '9': + if clicked_cell: + pos = (int(clicked_cell[1] / py), int(clicked_cell[0] / px)) + if int(key) <= self.grid_len: + self.preassign({pos:int(key)}) + elif key >= 'A' and key <= 'Z': + if clicked_cell: + pos = (int(clicked_cell[1] / py), int(clicked_cell[0] / px)) + if (ord(key) - ord('A') + 10) <= self.grid_len: + self.preassign({pos:key}) + elif key == ' ': + self.unassign_last() + self.render_cells(window) + pg.display.update() + + def save(self, filename:str) -> None: + """Saves the current state of the grid in a file.\n + Save format is:\n + rows,columns\n + (cell_number,cell_value)|(cell_number,cell_value)|...|(cell_number,cell_value)\n + (cell_number,cell_value)|(cell_number,cell_value)|...|(cell_number,cell_value)\n + \n + where the second line is the initial cell values before trying to solve\n + \t the third line is the initially unsolved cell values after solving if Grid.solve() has been run\n + + Args: + filename (str): The path of the file to be saved to. + """ + s = f"{self.rows},{self.columns}\n" + s += "|".join(f"({a},{self.cells[a].value})" for a in self.initial_solved) + s += "\n" + s += "|".join(f"({a},{self.cells[a].value})" for a in self.initial_unsolved) + with open(filename, 'w') as f: + f.write(s) + f.close() + + def load(self, filename:str): + """Loads the grid from a saved state file created by calling Grid.save(filename) + + Args: + filename (str): The path to the file containing the grid status to be loaded. + """ + with open(filename, 'r') as f: + for i,line in enumerate(f): + line = line.replace("\n","") + if i == 0: + rows, columns = line.replace("(","").replace(")","").split(",") + self.rows = int(rows) + self.columns = int(columns) + elif i == 1: + initial_solved_pairs = [tuple(int(i) for i in a.split(",")) for a in line.replace("(","").replace(")","").split("|")] + elif i == 2: + initial_unsolved_pairs = [tuple(eval(i) for i in a.split(",")) for a in line.replace("(","").replace(")","").split("|")] + f.close() + self.grid_len = self.rows * self.columns + self.domain:list[int] = [i for i in range(1, min(10, self.grid_len+1))] + if self.grid_len >= 10: + self.domain.extend(chr(ord('A') + i - 10) for i in range(10, self.grid_len+1)) + self.cells:list[Cell] = [Cell(i, self.domain) for i in range(self.grid_len * self.grid_len)] + self.unsolved_cells:list[int] = [i for i in range(self.grid_len * self.grid_len)] + self.solved_cells:list[int] = [] + self.initial_solved:list[int] = [] + self.initial_unsolved:list[int] = [i for i in range(self.grid_len * self.grid_len)] + for (number,value) in initial_solved_pairs: + self.initial_solved.append(number) + self.solved_cells.append(number) + self.cells[number].value = value + self.cells[number].domain = [] + self.initial_unsolved.remove(number) + self.unsolved_cells.remove(number) + for (number,value) in initial_unsolved_pairs: + if value: + self.solved_cells.append(number) + self.cells[number].value = value + self.cells[number].domain = [] + self.unsolved_cells.remove(number) + + def save_grid_image(self, path:str, size:tuple[int, int]=SIZE) -> None: + pg.init() + window = pg.display.set_mode(size) + window.fill(BOARD_COLOR) + self.render_cells(window) + pg.image.save(window, path) + pg.quit() + +def main(): + r = int(input("Enter number of rows in a block: ")) + c = int(input("Enter number of columns in a block: ")) + grid = Grid(r,c) + # grid = Grid() + # grid.load("s2.txt") + grid.input_to_grid() + grid.save("s3.txt") + grid.solve() + grid.save("s3.txt") + # grid = Grid() + # grid.load("s1.txt") + grid.render_grid() + +if __name__ == "__main__": + main() diff --git a/MachineLearning Projects/sudoku_solver/temp.ipynb b/MachineLearning Projects/sudoku_solver/temp.ipynb new file mode 100644 index 00000000..0737eb93 --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/temp.ipynb @@ -0,0 +1,230 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "class Node:\n", + " def __init__(self,val):\n", + " self.val = val\n", + " self.to = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [], + "source": [ + "class Node:\n", + " def __init__(self,val):\n", + " self.val:int = val\n", + " self.to:dict[Node,tuple[int,int]] = {} # destinationNode:(steps,price)\n", + " \n", + " def __str__(self) -> str:\n", + " children = ','.join(str(i.val) for i in self.to.keys())\n", + " return f\"Node({self.val})\"\n", + " \n", + " def __repr__(self) -> str:\n", + " children = ','.join(str(i.val) for i in self.to.keys())\n", + " return f\"Node({self.val})\"\n", + " \n", + " def full(self) -> str:\n", + " children = ','.join(str(i.val) for i in self.to.keys())\n", + " return f\"Node({self.val})->[{children}]\"\n", + "\n", + "def update(node:Node, start:list[int]):\n", + " # print(\"iter\", node, start)\n", + " if node.val in start:\n", + " # print(\"found: \", node, \" => \", start)\n", + " return {}\n", + " ret:dict[Node,set[tuple[int,int]]] = {\n", + " i:set([tuple(node.to[i]),]) for i in node.to.keys()\n", + " } # destinationNode:[(steps1,price1), (steps2,price2), ...]\n", + " for destinationNode,(steps,price) in node.to.items():\n", + " # print(f\"step {node} to {destinationNode}\")\n", + " returned = update(destinationNode, [*start,node.val])\n", + " # print(f\"{node.val} going to {destinationNode.val} got {returned}\")\n", + " if returned == {}:\n", + " # print(f\"here on\")\n", + " ret[destinationNode].add((steps,price))\n", + " continue\n", + " for v,mylist in returned.items():\n", + " # v is the a possible destination from our destination node\n", + " # my list is a list of the steps and prices to that possible destination\n", + " for (stp,prc) in mylist:\n", + " newTuple = (stp+steps,prc+price)\n", + " if ret.get(v):\n", + " ret[v].add(newTuple)\n", + " else:\n", + " ret[v] = set([newTuple,])\n", + " return ret" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [], + "source": [ + "from cmath import inf\n", + "\n", + "def findCheapestPrice(n: int, flights: list[list[int]], src: int, dst: int, k: int) -> int:\n", + " nodes:dict[int,Node] = {}\n", + " for s,d,p in flights:\n", + " dnode = nodes.get(d)\n", + " if dnode:\n", + " snode = nodes.get(s)\n", + " if snode:\n", + " snode.to[dnode] = (1,p)\n", + " else:\n", + " nd = Node(s)\n", + " nd.to[dnode] = (1,p)\n", + " nodes[s] = nd\n", + " else:\n", + " snode = nodes.get(s)\n", + " if snode:\n", + " nd = Node(d)\n", + " snode.to[nd] = (1,p)\n", + " nodes[d] = nd\n", + " else:\n", + " nd1 = Node(s)\n", + " nd2 = Node(d)\n", + " nd1.to[nd2] = (1,p)\n", + " nodes[s] = nd1\n", + " nodes[d] = nd2\n", + " for _,node in nodes.items():\n", + " print(node.full())\n", + " return method2(nodes, src, dst, k)\n", + "\n", + "def method1(nodes:dict[int,Node], src:int, dst:int, k:int) -> int:\n", + " results = {}\n", + " for val,node in nodes.items():\n", + " ret = update(node, [])\n", + " results[val] = ret\n", + " desired = results[src].get(nodes[dst])\n", + " if not desired:\n", + " return -1\n", + " filtered = []\n", + " k = k + 1\n", + " for d in desired:\n", + " if d[0] <= k:\n", + " filtered.append(d)\n", + " return min(filtered, key=lambda x:x[1])\n", + "\n", + "def method2(nodes:dict[int,Node], src:int, dst:int, k:int) -> int:\n", + " def recurse(node:Node, dst:int, k:int, visited:list[int]):\n", + " results = []\n", + " if k == 1:\n", + " for nd in node.to.keys():\n", + " if nd.val == dst:\n", + " return node.to[nd][1]\n", + " return inf\n", + " if node.val in visited:\n", + " return inf\n", + " for nd in node.to.keys():\n", + " if nd.val == dst:\n", + " results.append(node.to[nd][1])\n", + " else:\n", + " temp = recurse(nd, dst, k-1, [*visited, node.val]) + node.to[nd][1]\n", + " results.append(temp)\n", + " if len(results):\n", + " return min(results)\n", + " return inf\n", + " \n", + " k = k+1\n", + " node = nodes[src]\n", + " result = recurse(node, dst, k, [])\n", + " if result == inf:\n", + " return -1\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "100" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "findCheapestPrice(n = 3, flights = [[0,1,100],[1,2,100],[0,2,500]], src = 0, dst = 2, k = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Node(0)->[12,8,15,10]\n", + "Node(12)->[4,3,14,13,9,0,16,6]\n", + "Node(5)->[6,14,13,16,10,9,7]\n", + "Node(6)->[14,10,2,12]\n", + "Node(8)->[6,10,11,9,2,13,3]\n", + "Node(13)->[15,12,6,16,0,5,11,7,8]\n", + "Node(15)->[3,0,6,13,12,11,14,2]\n", + "Node(10)->[12,2,15,11,5,4,9,0,7]\n", + "Node(3)->[4,12,5,6,7,10]\n", + "Node(7)->[11,3,1,14,0,12,2]\n", + "Node(11)->[16,1,0,2,6,9]\n", + "Node(9)->[4,6,1,12,7,10,15,5]\n", + "Node(4)->[7,9,8,5,11,10]\n", + "Node(2)->[12,0,11,5,13,10,7]\n", + "Node(14)->[15,1,9,7,11,6]\n", + "Node(16)->[4,12,1,3,8,11,9,14]\n", + "Node(1)->[11,4,3,7]\n" + ] + }, + { + "data": { + "text/plain": [ + "47" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "findCheapestPrice(n = 4, flights = [[0,12,28],[5,6,39],[8,6,59],[13,15,7],[13,12,38],[10,12,35],[15,3,23],[7,11,26],[9,4,65],[10,2,38],[4,7,7],[14,15,31],[2,12,44],[8,10,34],[13,6,29],[5,14,89],[11,16,13],[7,3,46],[10,15,19],[12,4,58],[13,16,11],[16,4,76],[2,0,12],[15,0,22],[16,12,13],[7,1,29],[7,14,100],[16,1,14],[9,6,74],[11,1,73],[2,11,60],[10,11,85],[2,5,49],[3,4,17],[4,9,77],[16,3,47],[15,6,78],[14,1,90],[10,5,95],[1,11,30],[11,0,37],[10,4,86],[0,8,57],[6,14,68],[16,8,3],[13,0,65],[2,13,6],[5,13,5],[8,11,31],[6,10,20],[6,2,33],[9,1,3],[14,9,58],[12,3,19],[11,2,74],[12,14,48],[16,11,100],[3,12,38],[12,13,77],[10,9,99],[15,13,98],[15,12,71],[1,4,28],[7,0,83],[3,5,100],[8,9,14],[15,11,57],[3,6,65],[1,3,45],[14,7,74],[2,10,39],[4,8,73],[13,5,77],[10,0,43],[12,9,92],[8,2,26],[1,7,7],[9,12,10],[13,11,64],[8,13,80],[6,12,74],[9,7,35],[0,15,48],[3,7,87],[16,9,42],[5,16,64],[4,5,65],[15,14,70],[12,0,13],[16,14,52],[3,10,80],[14,11,85],[15,2,77],[4,11,19],[2,7,49],[10,7,78],[14,6,84],[13,7,50],[11,6,75],[5,10,46],[13,8,43],[9,10,49],[7,12,64],[0,10,76],[5,9,77],[8,3,28],[11,9,28],[12,16,87],[12,6,24],[9,15,94],[5,7,77],[4,10,18],[7,2,11],[9,5,41]], src = 13, dst = 4, k = 13)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/MachineLearning Projects/sudoku_solver/temp.py b/MachineLearning Projects/sudoku_solver/temp.py new file mode 100644 index 00000000..553f0531 --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/temp.py @@ -0,0 +1,2 @@ +while True: + pass \ No newline at end of file diff --git a/MachineLearning Projects/sudoku_solver/tempfile1.jpg b/MachineLearning Projects/sudoku_solver/tempfile1.jpg new file mode 100644 index 00000000..7e398e54 Binary files /dev/null and b/MachineLearning Projects/sudoku_solver/tempfile1.jpg differ diff --git a/MachineLearning Projects/sudoku_solver/tempfile2.jpg b/MachineLearning Projects/sudoku_solver/tempfile2.jpg new file mode 100644 index 00000000..b0069382 Binary files /dev/null and b/MachineLearning Projects/sudoku_solver/tempfile2.jpg differ diff --git a/MachineLearning Projects/sudoku_solver/templates/index.html b/MachineLearning Projects/sudoku_solver/templates/index.html new file mode 100644 index 00000000..8a812ec4 --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/templates/index.html @@ -0,0 +1,20 @@ + + + + + + Sudoku Solver + + +

Sudoku Solver

+
+

To solve a sudoku select the image of the sudoku and upload it to the page then hit submit.

+

The solution will be returned as an image on the next page.

+
+
+ + +
+
+ + \ No newline at end of file diff --git a/MachineLearning Projects/sudoku_solver/templates/result.html b/MachineLearning Projects/sudoku_solver/templates/result.html new file mode 100644 index 00000000..94fbedb1 --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/templates/result.html @@ -0,0 +1,19 @@ + + + + + + Solution + + +
+ Back to Main Page +
+
+

Solution

+
+
+ img +
+ + \ No newline at end of file diff --git a/MachineLearning Projects/sudoku_solver/test_full_stack.py b/MachineLearning Projects/sudoku_solver/test_full_stack.py new file mode 100644 index 00000000..a24ab068 --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/test_full_stack.py @@ -0,0 +1,31 @@ +import socket + +result_file = "resultfile2_server.png" +input_file = "f1.jpg" + +def main(address:tuple[str,int]): + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + sock.connect(address) + sock.settimeout(10) + with open(input_file, "rb") as f: + sock.send(f.read()) + res_buf = b'' + try: + while True: + try: + res = sock.recv(1) + res_buf += res + if 0 == len(res): + sock.close() + with open(result_file, "wb") as f: + f.write(res_buf) + break + except socket.timeout: + with open(result_file, "wb") as f: + f.write(res_buf) + break + finally: + sock.close() + +if "__main__" == __name__: + main(("localhost", 3535)) \ No newline at end of file diff --git a/MachineLearning Projects/sudoku_solver/verify_image.py b/MachineLearning Projects/sudoku_solver/verify_image.py new file mode 100644 index 00000000..1ad57615 --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/verify_image.py @@ -0,0 +1,110 @@ +"""This code is to verify the image dataset and check that all the labels of the grid location are in the correct place. +""" + +import PIL.Image as Image +from matplotlib import pyplot as plt +import numpy as np +from image import SudokuDataset, get_dataset, tqdm, Model +import torch + +img_size = (300,300) + +def mark(positions, image, color_value): + print(positions) + print(image.shape) + x0,y0,x1,y1,x2,y2,x3,y3 = positions + image = image.transpose() + grad = (y1 - y0)/(x1 - x0) + if x1 > x0: + for i in range(x1 - x0): + image[x0 + i, int(y0 + i * grad)] = color_value + else: + for i in range(x0 - x1): + image[x0 - i, int(y0 - i * grad)] = color_value + + grad = (y2 - y1)/(x2 - x1) + if x2 > x1: + for i in range(x2 - x1): + image[x1 + i, int(y1 + i * grad)] = color_value + else: + for i in range(x1 - x2): + image[x1 - i, int(y1 - i * grad)] = color_value + + grad = (y3 - y2)/(x3 - x2) + if x3 > x2: + for i in range(x3 - x2): + image[x2 + i, int(y2 + i * grad)] = color_value + else: + for i in range(x2 - x3): + image[x2 - i, int(y2 - i * grad)] = color_value + + grad = (y0 - y3)/(x0 - x3) + if x0 > x3: + for i in range(x0 - x3): + image[x3 + i, int(y3 + i * grad)] = color_value + else: + for i in range(x3 - x0): + image[x3 - i, int(y3 - i * grad)] = color_value + return image.transpose() + +# dataset = SudokuDataset("./archive/outlines_sorted.csv", img_size) +# for item in dataset: +# try: +# image = item['image'] +# grid = item['grid'] +# x0,y0,x1,y1,x2,y2,x3,y3 = list(grid.numpy()) +# x0 = int(x0 * img_size[0]) +# x1 = int(x1 * img_size[0]) +# x2 = int(x2 * img_size[0]) +# x3 = int(x3 * img_size[0]) +# y0 = int(y0 * img_size[1]) +# y1 = int(y1 * img_size[1]) +# y2 = int(y2 * img_size[1]) +# y3 = int(y3 * img_size[1]) +# image = mark((x0,y0,x1,y1,x2,y2,x3,y3), image.numpy()[0], 0.7) +# plt.imshow(image) +# plt.colorbar() +# plt.show() +# except KeyboardInterrupt: +# break + +def test(config:dict, model_filename:str): + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + model = torch.load(model_filename).to(device) + model.eval() + loss = torch.nn.MSELoss().to(device) + dataset = get_dataset(config['filename'], config['input_shape'], config['batch_size']) + batch_iterator = tqdm(dataset) + for batch in batch_iterator: + x = batch['image'].to(device) + y_true = batch['grid'].to(device) + # print(batch['grid']) + # return + y_pred = model(x) + error = loss(y_true, y_pred) + batch_iterator.set_postfix({"loss":f"Loss: {error.item():6.6f}"}) + x0,y0,x1,y1,x2,y2,x3,y3 = list(y_pred.detach().numpy()[1]) + print(x0,y0,x1,y1,x2,y2,x3,y3) + x0 = int(x0 * img_size[0]) + x1 = int(x1 * img_size[0]) + x2 = int(x2 * img_size[0]) + x3 = int(x3 * img_size[0]) + y0 = int(y0 * img_size[1]) + y1 = int(y1 * img_size[1]) + y2 = int(y2 * img_size[1]) + y3 = int(y3 * img_size[1]) + image = mark((x0,y0,x1,y1,x2,y2,x3,y3), x.detach().numpy()[0][0], 0.7) + plt.imshow(image) + plt.colorbar() + plt.show() + +config = { + "input_shape": (300,300), + "filename": "archive/outlines_sorted.csv", + "number_of_layers": 4, + "dims": 3, + "batch_size": 8, + "lr": 1e-5 +} +# model = train(50, config) +test(config, "model.pt") \ No newline at end of file diff --git a/MachineLearning Projects/sudoku_solver/web_interface.py b/MachineLearning Projects/sudoku_solver/web_interface.py new file mode 100644 index 00000000..6bc14604 --- /dev/null +++ b/MachineLearning Projects/sudoku_solver/web_interface.py @@ -0,0 +1,129 @@ +from flask import Flask, render_template, redirect, url_for, request, flash, session +from werkzeug.utils import secure_filename +import os +from random import choices, choice +from string import ascii_letters, digits +from time import sleep +from datetime import datetime +import socket + +app = Flask(__name__) + +app.config.from_pyfile("config.cfg") + +def manage_solution(input_file, result_file) -> int: + def send(input_file:str, sock:socket.socket) -> int: + try: + with open(input_file, "rb") as f: + sock.send(f.read()) + return 1 + except FileNotFoundError: + return -2 + except socket.error: + return -1 + + def connect() -> socket.socket: + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + sock.connect((app.config['SOLVER_IP'], int(app.config['SOLVER_PORT']))) + sock.settimeout(10) + return sock + + def manage_full_send(input_file:str, sock:socket.socket): + tries = 0 + while tries < 5: + send_state = send(input_file, sock) + if send_state == 1: + break + elif send_state == -2: + return -2 + elif send_state == -1: + sock = connect() + tries += 1 + return send_state + + sock = connect() + send_state = manage_full_send(input_file, sock) + if send_state == -1: + return -1 + elif send_state == -2: + return -2 + res_buf = b'' + try: + while True: + try: + res = sock.recv(1) + res_buf += res + if 0 == len(res): + sock.close() + with open(result_file, "wb") as f: + f.write(res_buf) + break + except socket.timeout: + with open(result_file, "wb") as f: + f.write(res_buf) + break + finally: + sock.close() + return 0 + +@app.route('/', methods=['POST', 'GET']) +def index(): + if "POST" == request.method: + print(request) + if 'image' not in request.files: + flash('No file part.', "danger") + else: + file = request.files['image'] + if '' == file.filename: + flash("No file selected.", "danger") + else: + ext = "." + file.filename.split('.')[-1] + filename = datetime.now().strftime("%d%m%y%H%M%S") + "_" + "".join(i for i in choices(ascii_letters+digits, k=3)) + ext + filename = os.path.join(app.config['UPLOAD_FOLDER'], filename) + print(filename) + file.save(filename) + session['filename'] = filename + return redirect(url_for('result')) + else: + if session.get('solved'): + session.pop('solved') + if session.get('filename'): + try: + os.remove(session['filename']) + session.pop('filename') + except FileNotFoundError: + pass + return render_template('index.html', request=request) + +@app.route('/result', methods=['GET']) +def result(): + if not session.get('solved'): + filename = session.get('filename') + if not filename: + return redirect(url_for('/')) + solution = "" + result_file = ".".join(i for i in filename.split(".")[:-1]) + "_sol.png" + result_file = result_file.split("/")[-1] + full_result_file = "static/" + result_file + result_file = f"../static/{result_file}" + result = manage_solution(filename, full_result_file) + os.remove(session['filename']) + if result == 0: + session['filename'] = full_result_file + print("solved") + solution = result_file + session['solved'] = solution + else: + session.pop('filename') + flash(f"There was an issue, Error {result}", "danger") + redirect(url_for('/')) + else: + solution = session['solved'] + return render_template('result.html', img=solution) + +if "__main__" == __name__: + app.run( + host="192.168.1.88", + port=5000, + debug=True + ) \ No newline at end of file