diff --git a/data_validator.py b/data_validator.py
index 01adbd4..6803bf9 100644
--- a/data_validator.py
+++ b/data_validator.py
@@ -3,20 +3,24 @@
 
 
 def read_data_file(file_path):
-    with open(file_path, 'r') as file:
+    with open(file_path, "r") as file:
         lines = file.readlines()
     data = []
     for line in lines:
         parts = line.strip().split()
+        if len(parts) < 8:
+            print("Bad data")
+            continue
         coords = list(map(int, parts[:-1]))
-     #   print(parts)
-        image_name = parts[-1]
+        try:
+            image_name = parts[-1]
+        except IndexError:
+            print("Bad data")
+            continue
         data.append((coords, image_name))
     return data
 
 
-import cv2
-
 def draw_points(image, points):
     for i in range(0, len(points), 2):
         x = points[i]
@@ -27,14 +31,16 @@ def draw_points(image, points):
     return image
 
 
-
 def main():
     # Get the input file path from the user
-    file_path = "points.txt"  #input("Enter the path to the .txt file: ")
+    file_path = "points.txt"  # input("Enter the path to the .txt file: ")
     data = read_data_file(file_path)
+    if len(data) == 0:
+        print("No data to review")
+        quit()
 
     # Set the path to the folder containing images
-    image_folder = 'output_frames'
+    image_folder = "output_frames"
 
     index = 0
     while True:
@@ -49,10 +55,10 @@ def main():
 
         image_with_points = draw_points(image, coords)
 
-        cv2.imshow('Image with Points', image_with_points)
+        cv2.imshow("Image with Points", image_with_points)
         key = cv2.waitKey(0)
 
-        if key == ord('q'):
+        if key == ord("q"):
             break
         else:
             index = (index + 1) % len(data)
diff --git a/imganno.py b/imganno.py
index 2805578..e4f8ad8 100644
--- a/imganno.py
+++ b/imganno.py
@@ -1,27 +1,57 @@
 import cv2
+import numpy as np
 import glob
 import os
-import time
 import sys
+import collections
+import re
 
 # Constants
 MAX_POINTS = 7
 KEYPOINT_COLOR = (255, 255, 0)
-KEYPOINT_ALPHA = 0.30 # Transparency value (0.0 - fully transparent, 1.0 - fully opaque)
+KEYPOINT_ALPHA = 0.30  # Transparency value (0.0 - fully transparent, 1.0 - fully opaque)
+SIMILARITY_LOOKBACK_COUNT = 10  # Number of previous good frames to check similarity to
+SIMILARITY_THRESHOLD = 0.99
+POINT_HELP_TEXT = [
+    "left eyelid corner",
+    "upper left eyelid",
+    "upper right eyelid",
+    "right eyelid corner",
+    "lower right eyelid",
+    "lower left eyelid",
+    "pupil center",
+]
 last_annotated_index_path = "last_annotated_index.txt"
-# Variables
+points_path = "points.txt"  # Replace with path of existing file to save keypoint data to
+
+# General variables
+original_points = []
 image_points = []
 image_index = 0
 keypoints_history = []
 point_id = 0
 
+# Rotation-related variable
+scale_factor = 2  # Set the desired scale factor
+rotation_degrees = 0
+rotation_increment = 15
+rotation_matrix = np.asarray([[1, 0, 0], [0, 1, 0]])
+rotation_matrix_inv = np.asarray([[1, 0, 0], [0, 1, 0]])
+
 
 def mouse_callback(event, x, y, flags, param):
-    global image_points, point_id
+    global original_points, image_points, point_id, rotation_matrix_inv
 
     if event == cv2.EVENT_LBUTTONDOWN:
         if len(image_points) < MAX_POINTS:
             image_points.append((point_id, x, y))
+
+            # Points in its non-rotated coordinates
+            original_point = np.asarray([x, y])
+            new_point = cv2.transform(original_point.reshape((-1, 1, 2)), rotation_matrix_inv)
+            new_point = np.uint16(new_point[0][0])
+            original_points.append((point_id, new_point[0], new_point[1]))
+
             point_id += 1
 
     elif event == cv2.EVENT_RBUTTONDOWN:
@@ -30,6 +60,13 @@ def mouse_callback(event, x, y, flags, param):
             closest_point_index = distances.index(min(distances))
             image_points[closest_point_index] = (image_points[closest_point_index][0], x, y)
 
+            # Points in its non-rotated coordinates
+            original_point = np.asarray([x, y])
+            new_point = cv2.transform(original_point.reshape((-1, 1, 2)), rotation_matrix_inv)
+            new_point = np.uint16(new_point[0][0])
+            original_points[closest_point_index] = (image_points[closest_point_index][0], new_point[0], new_point[1])
+
+
 def load_last_annotated_index(file_path):
     try:
         with open(file_path, "r") as file:
@@ -37,22 +74,19 @@ def load_last_annotated_index(file_path):
     except FileNotFoundError:
         return 0
 
+
 # Function to save the last annotated frame index to a file
 def save_last_annotated_index(file_path, index):
     with open(file_path, "w") as file:
         file.write(str(index))
 
 
-import os
-import cv2
-
-
 def extract_frames(video_path, output_folder):
-    # Get the base name of the video file without the extension
-    base_name = os.path.splitext(os.path.basename(video_path))[0]
+    # # Get the base name of the video file without the extension
+    # base_name = os.path.splitext(os.path.basename(video_path))[0]
 
     # Create the output folder if it doesn't exist
-   # output_folder = base_name + '_frames'
+    # output_folder = base_name + '_frames'
     os.makedirs(output_folder, exist_ok=True)
 
     # Open the video file
@@ -69,18 +103,31 @@ def extract_frames(video_path, output_folder):
     print(f"Extracting {frame_count} frames from {video_path} at {fps} FPS")
 
     frame_number = 0
+    frame_good = 0
+    frame_deque = collections.deque(maxlen=SIMILARITY_LOOKBACK_COUNT)
     while True:
         try:
             ret, frame = cap.read()
             if not ret:
-                print(f"Finished extracting frames. Total frames extracted: {frame_number}")
+                print(f"Finished extracting frames. Total frames extracted: {frame_number}, out of which {frame_good} are saved.")
                 break
 
+            is_frame_good = True
+            filename = f"{video_path}_frame_{frame_number:04d}.jpeg"
+            frame_path = os.path.join(output_folder, filename)
             # Save the frame as a JPEG file
-            frame_path = os.path.join(output_folder, f"{video_path}_frame_{frame_number:04d}.jpeg")
             cv2.imwrite(frame_path, frame)
-
             print(f"Saved frame {frame_number}")
+            for frame_number_template, frame_template in frame_deque:
+                if cv2.matchTemplate(frame, frame_template, cv2.TM_CCOEFF_NORMED).max() >= SIMILARITY_THRESHOLD:
+                    print(f"Frame is too similar to {frame_number_template}, excluding to annotate..")
+                    is_frame_good = False
+                    break
+            if is_frame_good or len(frame_deque) == 0:
+                frame_deque.append((frame_number, frame))
+                with open(f"{output_folder}{os.path.sep}good_images_list.txt", "a") as file:
+                    file.write(f"{filename}\n")
+                frame_good += 1
 
             frame_number += 1
 
@@ -95,12 +142,13 @@ def extract_frames(video_path, output_folder):
 
 
 video_path = input("Give the name of the video you would like to annotate :> ")
-output_folder = "output_frames" #input("What would you like to name the output folder, or what is the name of the folder of pre extracted frames :> ")
+output_folder = "output_frames"  # input("What would you like to name the output folder, or what is the name of the folder of pre extracted frames :> ")
 
-resume_status = input("Resume annotations where you left off? Y/N? (if you are doing a new video type N, this will not work properly if new files have been added) :> ")
+resume_status = input(
+    "Resume annotations where you left off? Y/N? (if you are doing a new video type N, this will not work properly if new files have been added) :> "
+)
 resume_status = resume_status.lower()
 if resume_status == "y":
-   
     image_index = load_last_annotated_index(last_annotated_index_path)
     print("resuming")
 
@@ -108,10 +156,13 @@ def extract_frames(video_path, output_folder):
     extract_frames(video_path, output_folder)
 
 
-print('\n \nKeybinds:')
+print("\n \nKeybinds:")
 print("Press 'r' to clear the keypoints.")
 print("Press 'w' to save the keypoints and move to the next image.")
-print("Press 'q' to place the keypoints from the previous image to the current image \n")
+print("Press 's' to save the keypoints and move to the previous image.")
+print("Press 'q' to place the shadow keypoints to the current image \n")
+print("Press 'd' to rotate image clockwise")
+print("Press 'a' to rotate image counter-clockwise\n")
 print("Press 'y' to close the program \n")
 
 print("Mouse Buttons:")
@@ -119,22 +170,66 @@ def extract_frames(video_path, output_folder):
 print("Right click to adjust point nearest to cursor to cursor position. \n \n")
 
 
-
-
-
-image_folder = output_folder # path with .jpg images
-image_files = glob.glob(image_folder + "/*.jpeg")
+image_folder = output_folder  # path with .jpg images
+# image_files = glob.glob(image_folder + "/*.jpeg")
+image_files = []
+try:
+    with open(f"{output_folder}{os.path.sep}good_images_list.txt", "r") as file:
+        lines = file.readlines()
+except FileNotFoundError:
+    print("No good images found! Press enter to exit..")
+    input()
+    exit()
+else:
+    for line in lines:
+        image_file = f"{image_folder}{os.path.sep}{line.strip()}"
+        if len(image_file) < len(image_folder):
+            continue
+        else:
+            image_files.append(image_file)
+if len(image_files) == 0:
+    print("No good images found! Press enter to exit..")
+    input()
+    exit()
 
 cv2.namedWindow("Image")
 cv2.setMouseCallback("Image", mouse_callback)
 
-scale_factor = 2  # Set the desired scale factor
 
-while image_index < len(image_files):
+while True:
+    # Init
+    image_points = []
+    original_points = []
+    point_id = 0
+
     # Load image
-    print(image_index)
+    directory_path = os.path.dirname(image_files[image_index])
+    file_name = image_files[image_index].replace(directory_path + os.path.sep, "")
     image = cv2.imread(image_files[image_index])
+    print(f"Annotating {file_name}")
 
+    # Load image points
+    line = None
+    try:
+        with open(points_path, "r") as file:
+            line = re.search(f"(^|\n).*{file_name}.*", file.read())
+    except FileNotFoundError:
+        open(points_path, "a").close()
+    if line:
+        parts = line[0].strip().split()
+        coords = list(map(int, parts[:-1]))
+        for i in range(0, len(coords), 2):
+            x = coords[i] * scale_factor
+            y = coords[i + 1] * scale_factor
+            original_points.append((point_id, x, y))
+
+            original_point = np.asarray([x, y])
+            new_point = cv2.transform(original_point.reshape((-1, 1, 2)), rotation_matrix)
+            new_point = np.uint16(new_point[0][0])
+            image_points.append((point_id, new_point[0], new_point[1]))
+            point_id += 1
+
+    # Draw keypoints history on original image
     for keypoints in keypoints_history:
         for point in keypoints:
             overlay = image.copy()
@@ -144,41 +239,101 @@ def extract_frames(video_path, output_folder):
             image = cv2.addWeighted(overlay, KEYPOINT_ALPHA, image, 1 - KEYPOINT_ALPHA, 0)
 
     while True:
-        display_image = cv2.resize(image, None, fx=scale_factor, fy=scale_factor)  # Scale the image
-
+        # Scale the image
+        display_image = cv2.resize(image, None, fx=scale_factor, fy=scale_factor)
+
+        # Rotate image
+        image_center = (display_image.shape[1] // 2, display_image.shape[0] // 2)
+        rotation_matrix = cv2.getRotationMatrix2D(image_center, rotation_degrees, 1)
+        rotation_matrix_inv = cv2.getRotationMatrix2D(image_center, -rotation_degrees, 1)
+        display_image = cv2.warpAffine(display_image, rotation_matrix, display_image.shape[0:-1])
+
+        # Point hint when putting down points
+        if point_id < MAX_POINTS:
+            cv2.putText(
+                display_image,
+                f"put down {point_id+1}: {POINT_HELP_TEXT[point_id]}",
+                (5, 14),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.5,
+                (0, 255, 0),
+                1,
+            )
+
+        # Keyboard shortcuts as footer
+        text_footer = np.zeros((np.uint8(0.04 * display_image.shape[1]), display_image.shape[0], 3), np.uint8)
+        cv2.putText(
+            text_footer,
+            "r - clear | w - next | s - prev | q - copy KPs | y - quit",
+            (
+                np.uint8(0.01 * display_image.shape[0]),
+                np.uint8(text_footer.shape[0] / 2 + 0.009 * display_image.shape[1]),
+            ),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            (229, 229, 229),
+            1,
+        )
+        display_image = cv2.vconcat([display_image, text_footer])
+
+        # Draw image annotations
         for i, point in enumerate(image_points):
-            # Scale the points back to the original image size
-            original_point = (int(point[1]), int(point[2]))
-
-            cv2.circle(display_image, original_point, 4, (0, 255, 0), -1)
-            cv2.putText(display_image, str(i + 1), (original_point[0], original_point[1] + 15),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
+            draw_point = (point[1], point[2])
+            cv2.circle(display_image, draw_point, 4, (0, 255, 0) if i < 6 else (0, 0, 255), -1)
+            cv2.putText(
+                display_image,
+                str(i + 1),
+                (draw_point[0], draw_point[1] + 15),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.5,
+                (0, 255, 0),
+                1,
+            )
 
         cv2.imshow("Image", display_image)
+        cv2.setWindowTitle("Image", f"Current image: {file_name}")
         key = cv2.waitKey(1) & 0xFF
 
-        if key == ord("w"):
-            directory_path = os.path.dirname(image_files[image_index])
-            file_name = os.path.basename(image_files[image_index])
-            filen = image_files[image_index].replace(directory_path + os.path.sep, "")
-
+        if key == ord("w") or key == ord("s"):
             if len(image_points) == MAX_POINTS:
-                keypoints_history = [image_points.copy()]
-                sorted_points = sorted(image_points, key=lambda x: x[0])
+                keypoints_history = [original_points.copy()]
+                sorted_points = sorted(original_points, key=lambda x: x[0])
 
                 # Print image points
-                point_string = ",".join([f"{int(point[1] / scale_factor)},{int(point[2] / scale_factor)}" for point in sorted_points])
+                point_string = ",".join(
+                    [
+                        f"{min(int(point[1] / scale_factor), image.shape[1]-1)},{min(int(point[2] / scale_factor), image.shape[0]-1)}"
+                        for point in sorted_points
+                    ]
+                )
                 point_string = point_string.replace(",", " ")
 
-                point_string = f'{point_string} {filen}'
-                print(f'[INFO] SAVED DATA FOR FRAME {filen}')
-                file_path = "points.txt"  # Replace with path of existing file to save keypoint data to
-                with open(file_path, "a") as file:
+                point_string = f"{point_string} {file_name}"
+                with open(points_path, "r") as file:
+                    file_content = file.read()
+                    line = re.search(f"(^|\n).*{file_name}.*", file_content)
+                if line:
+                    with open(points_path, "w") as file:
+                        file.write(re.sub(f"(^|\n).*{file_name}.*", "", file_content))
+                with open(points_path, "a") as file:
                     file.write(point_string + "\n")
+                print(f"[INFO] SAVED DATA FOR FRAME {file_name}")
+
             else:
-                print(f'[INFO] SKIPPED FRAME {filen}')
-            break
+                print(f"[INFO] SKIPPED FRAME {file_name}")
 
+            save_last_annotated_index(last_annotated_index_path, image_index)
+
+            if key == ord("w"):
+                # Move to the next image
+                image_index += 1
+                image_index = image_index if image_index < len(image_files) else 0
+                break
+            elif key == ord("s"):
+                # Move to previous image
+                image_index -= 1
+                image_index = image_index if image_index >= 0 else len(image_files) - 1
+                break
 
         if key == ord("r"):
             print("[INFO] CLEARED KEYPOINTS")
@@ -189,40 +344,38 @@ def extract_frames(video_path, output_folder):
             print("[INFO] CLOSING")
             exit()
 
+        if key == ord("a") or key == ord("d"):
+            if key == ord("a"):
+                rotation_degrees += rotation_increment
+            elif key == ord("d"):
+                rotation_degrees -= rotation_increment
+            # Clamp to 0..359
+            rotation_degrees = rotation_degrees if rotation_degrees >= 0 else 360 + rotation_degrees
+            rotation_degrees = rotation_degrees if rotation_degrees < 360 else rotation_degrees - 360
+            print(f"Rotation: {rotation_degrees}")
+            rotation_matrix = cv2.getRotationMatrix2D(image_center, rotation_degrees, 1)
+            rotation_matrix_inv = cv2.getRotationMatrix2D(image_center, -rotation_degrees, 1)
+            new_points = []
+            for point in original_points:
+                original_point = np.asarray([point[1], point[2]])
+                new_point = cv2.transform(original_point.reshape((-1, 1, 2)), rotation_matrix)
+                new_point = np.uint16(new_point[0][0])
+                new_points.append((point[0], new_point[0], new_point[1]))
+            image_points = new_points
+            new_points = []
+
         if key == ord("q"):
             if len(keypoints_history) > 0:
-                image_points = keypoints_history[-1].copy()  # Copy the last set of keypoints from history
+                original_points = keypoints_history[-1].copy()  # Copy the last set of keypoints from history
+                new_points = []
+                for point in original_points:
+                    original_point = np.asarray([point[1], point[2]])
+                    new_point = cv2.transform(original_point.reshape((-1, 1, 2)), rotation_matrix)
+                    new_point = np.uint16(new_point[0][0])
+                    new_points.append((point[0], new_point[0], new_point[1]))
+                image_points = new_points
+                point_id = MAX_POINTS
                 print("[INFO] Placed keypoints from history")
 
 
-
-
-
-
-    # Reset points for the next image
-    image_points = []
-
-    # Move to the next image
-    image_index += 1
-    save_last_annotated_index(last_annotated_index_path, image_index)
-
 cv2.destroyAllWindows()
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/poetry.lock b/poetry.lock
index 33e2557..3709f1b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "altgraph"
@@ -11,37 +11,6 @@ files = [
     {file = "altgraph-0.17.4.tar.gz", hash = "sha256:1b5afbb98f6c4dcadb2e2ae6ab9fa994bbb8c1d75f4fa96d340f9437ae454406"},
 ]
 
-[[package]]
-name = "annotated-types"
-version = "0.6.0"
-description = "Reusable constraint types to use with typing.Annotated"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
-    {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
-]
-
-[[package]]
-name = "anyio"
-version = "4.3.0"
-description = "High level compatibility layer for multiple asynchronous event loop implementations"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
-    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
-]
-
-[package.dependencies]
-idna = ">=2.8"
-sniffio = ">=1.1"
-
-[package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (>=0.23)"]
-
 [[package]]
 name = "black"
 version = "22.12.0"
@@ -68,6 +37,7 @@ click = ">=8.0.0"
 mypy-extensions = ">=0.4.3"
 pathspec = ">=0.9.0"
 platformdirs = ">=2"
+tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
@@ -101,87 +71,18 @@ files = [
 ]
 
 [[package]]
-name = "coloredlogs"
-version = "15.0.1"
-description = "Colored terminal output for Python's logging module"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-files = [
-    {file = "coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934"},
-    {file = "coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0"},
-]
-
-[package.dependencies]
-humanfriendly = ">=9.1"
-
-[package.extras]
-cron = ["capturer (>=2.4)"]
-
-[[package]]
-name = "fastapi"
-version = "0.110.0"
-description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "fastapi-0.110.0-py3-none-any.whl", hash = "sha256:87a1f6fb632a218222c5984be540055346a8f5d8a68e8f6fb647b1dc9934de4b"},
-    {file = "fastapi-0.110.0.tar.gz", hash = "sha256:266775f0dcc95af9d3ef39bad55cff525329a931d5fd51930aadd4f428bf7ff3"},
-]
-
-[package.dependencies]
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 typing-extensions = ">=4.1.0"
 
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-python-versions = ">=3.6"
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-    {file = "pye3d-0.3.2.tar.gz", hash = "sha256:12793dd0b2926539252d6b2ba9df6b0c1774947a007661b2c1670ef73c905339"},
-]
-
-[package.dependencies]
-msgpack = ">=1.0"
-numpy = "*"
-sortedcontainers = "*"
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)", "sphinx (<4.4)"]
-examples = ["opencv-python", "pupil-detectors"]
-legacy-sklearn-models = ["joblib", "scikit-learn"]
-testing = ["matplotlib", "opencv-python-headless (==4.6.0.66)", "pandas", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.0.1)", "scikit-image"]
-with-opencv = ["opencv-python"]
-
 [[package]]
 name = "pyinstaller"
 version = "5.13.2"
@@ -773,17 +386,6 @@ files = [
 packaging = ">=22.0"
 setuptools = ">=42.0.0"
 
-[[package]]
-name = "pyreadline3"
-version = "3.4.1"
-description = "A python implementation of GNU readline."
-optional = false
-python-versions = "*"
-files = [
-    {file = "pyreadline3-3.4.1-py3-none-any.whl", hash = "sha256:b0efb6516fd4fb07b45949053826a62fa4cb353db5be2bbb4a7aa1fdd1e345fb"},
-    {file = "pyreadline3-3.4.1.tar.gz", hash = "sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae"},
-]
-
 [[package]]
 name = "pyserial"
 version = "3.5"
@@ -811,9 +413,11 @@ files = [
 
 [package.dependencies]
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
+exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
 pluggy = ">=0.12,<2.0"
+tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
 testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
@@ -901,58 +505,16 @@ testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[l
 testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
-name = "sniffio"
-version = "1.3.1"
-description = "Sniff out which async library your code is running under"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
-    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
-]
-
-[[package]]
-name = "sortedcontainers"
-version = "2.4.0"
-description = "Sorted Containers -- Sorted List, Sorted Dict, Sorted Set"
-optional = false
-python-versions = "*"
-files = [
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-    {file = "sortedcontainers-2.4.0.tar.gz", hash = "sha256:25caa5a06cc30b6b83d11423433f65d1f9d76c4c6a0c90e3379eaa43b9bfdb88"},
-]
-
-[[package]]
-name = "starlette"
-version = "0.36.3"
-description = "The little ASGI library that shines."
-optional = false
-python-versions = ">=3.8"
-files = [
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-    {file = "starlette-0.36.3.tar.gz", hash = "sha256:90a671733cfb35771d8cc605e0b679d23b992f8dcfad48cc60b38cb29aeb7080"},
-]
-
-[package.dependencies]
-anyio = ">=3.4.0,<5"
-
-[package.extras]
-full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
-
-[[package]]
-name = "sympy"
-version = "1.12"
-description = "Computer algebra system (CAS) in Python"
+name = "tomli"
+version = "2.1.0"
+description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"},
-    {file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"},
+    {file = "tomli-2.1.0-py3-none-any.whl", hash = "sha256:a5c57c3d1c56f5ccdf89f6523458f60ef716e210fc47c4cfb188c5ba473e0391"},
+    {file = "tomli-2.1.0.tar.gz", hash = "sha256:3f646cae2aec94e17d04973e4249548320197cfabdf130015d023de4b74d8ab8"},
 ]
 
-[package.dependencies]
-mpmath = ">=0.19"
-
 [[package]]
 name = "typing-extensions"
 version = "4.10.0"
@@ -964,24 +526,6 @@ files = [
     {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
 ]
 
-[[package]]
-name = "uvicorn"
-version = "0.20.0"
-description = "The lightning-fast ASGI server."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "uvicorn-0.20.0-py3-none-any.whl", hash = "sha256:c3ed1598a5668208723f2bb49336f4509424ad198d6ab2615b7783db58d919fd"},
-    {file = "uvicorn-0.20.0.tar.gz", hash = "sha256:a4e12017b940247f836bc90b72e725d7dfd0c8ed1c51eb365f5ba30d9f5127d8"},
-]
-
-[package.dependencies]
-click = ">=7.0"
-h11 = ">=0.8"
-
-[package.extras]
-standard = ["colorama (>=0.4)", "httptools (>=0.5.0)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"]
-
 [[package]]
 name = "viztracer"
 version = "0.15.6"
@@ -1074,5 +618,5 @@ watchmedo = ["PyYAML (>=3.10)"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = "~3.11.0"
-content-hash = "3edf4ffa8dd42d4922b72abe8689db9a75459f3381876220ab0511ae816ff6b6"
+python-versions = ">=3.10.0,<3.13"
+content-hash = "8253562970f8adbbd8f3debf919fce05c7e8e3fb71e8f4f9082d197db77f481d"
diff --git a/pyproject.toml b/pyproject.toml
index f6b8aca..42df1b3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,7 @@ license = "GPL v3"
 repository = "https://github.com/RedHawk989/ETVR-Data-Collection"
 
 [tool.poetry.dependencies]
-python = ">=3.10.0"
+python = ">=3.10.0,<3.13"
 python-osc = "^1.8.1"
 opencv-python = "^4.8.0.74"
 numpy = "^1.23.5"
@@ -38,7 +38,18 @@ respect-gitignore = true
 target-version = "py311"
 output-format = "grouped"
 indent-width = 4
-exclude = ["__pycache__", ".ruff_cache", ".vscode", ".idea", ".venv", "build", "dist", ".git", ".env", "venv"]
+exclude = [
+    "__pycache__",
+    ".ruff_cache",
+    ".vscode",
+    ".idea",
+    ".venv",
+    "build",
+    "dist",
+    ".git",
+    ".env",
+    "venv",
+]
 line-length = 135
 
 [tool.ruff.per-file-ignores]