Merge pull request larymak#398 from EugeneMMF/main

sudoku solver script.

Author: larymak

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.

MachineLearning Projects/sudoku_solver/__pycache__/image.cpython-311.pyc

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+UPLOAD_FOLDER="uploads"
+SECRET_KEY="secret"
+SOLVER_IP="localhost"
+SOLVER_PORT=3535

MachineLearning Projects/sudoku_solver/__pycache__/perspective.cpython-312.pyc

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+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())

MachineLearning Projects/sudoku_solver/__pycache__/sudoku.cpython-312.pyc

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+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)

MachineLearning Projects/sudoku_solver/config.cfg

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+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"))

MachineLearning Projects/sudoku_solver/f1.jpg

@@ -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
+}

MachineLearning Projects/sudoku_solver/f2.jpg

@@ -0,0 +1,2 @@
+while True:
+    pass

MachineLearning Projects/sudoku_solver/full_stack_http.py

@@ -0,0 +1,20 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Sudoku Solver</title>
+</head>
+<body>
+    <h1>Sudoku Solver</h1>
+    <hr>
+    <h3>To solve a sudoku select the image of the sudoku and upload it to the page then hit submit.</h3>
+    <h3>The solution will be returned as an image on the next page.</h3>
+    <div>
+        <form action="" method="post" enctype="multipart/form-data">
+            <input type="file" name="image" id="image" value={{request.files.image}}>
+            <input type="submit" value="Submit" >
+        </form>
+    </div>
+</body>
+</html>

MachineLearning Projects/sudoku_solver/image.py

@@ -0,0 +1,19 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Solution</title>
+</head>
+<body>
+    <hr>
+    <a href="/">Back to Main Page</a>
+    <hr>
+    <div>
+        <h1>Solution</h1>
+    </div>
+    <div>
+        <img src="{{img}}" alt="img" style="height: max-content; width: max-content;">
+    </div>
+</body>
+</html>

MachineLearning Projects/sudoku_solver/model.pt

@@ -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))

MachineLearning Projects/sudoku_solver/perspective.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")

MachineLearning Projects/sudoku_solver/resultfile2_server.png

@@ -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
+    )