diff --git a/components/lane_follower/src/Follower_Consts.toml b/components/lane_follower/src/Follower_Consts.toml
new file mode 100644
index 000000000..0c60b0047
--- /dev/null
+++ b/components/lane_follower/src/Follower_Consts.toml
@@ -0,0 +1,27 @@
+# Constants for the individual follower module
+
+[Individual_Follower]
+NWINDOWS = 9
+SEARCH_WIDTH = 100
+MINPIXELS = 40
+LANE_POLY_SIZE = 2
+BOX_COLOR = [0, 255, 0]
+LANE_COLOR = [255, 0, 0]
+DRAW_THICKNESS = 2
+
+[Lane_Follower]
+GUI = true
+# TODO: Move these into a conf
+KERNEL = 31
+LANE_TOLERANCE = 10
+MISSING_IMAGE_TOLERANCE = 100
+EMPTY_WINDOWS_THRESHOLD = 3
+OVERFLOW = 1000.0
+FORMAT = [640, 480]
+TOLERANCE = 100
+PIXELS_TO_METERS = 260.8269125
+lower_hsv = [100, 0, 220]
+upper_hsv = [255, 255, 255]
+# Alignment points
+bottom_coordinates = [[12, 472], [499, 475]]
+top_coordinates = [[90, 8], [435, 24]]
diff --git a/components/lane_follower/src/individual_follower.py b/components/lane_follower/src/individual_follower.py
new file mode 100644
index 000000000..69937b864
--- /dev/null
+++ b/components/lane_follower/src/individual_follower.py
@@ -0,0 +1,150 @@
+import math
+
+import cv2
+import numpy as np
+import toml
+from einops import repeat
+
+
+class ifResult:
+    def __init__(self):
+        self.result_img = None
+        self.empty_windows = 0
+        self.heading = 0
+        self.slope = 0
+
+    pass
+
+
+class IndividualFollower:
+
+    def __init__(self, toml_path='Follower_Consts.toml'):
+
+        self.consts = toml.load(toml_path)["Individual_Follower"]
+        # self.fit is set in Plot_Line
+        self.fit = None
+        # These two are set below
+        self._binary_warped = None
+        self._histogram = None
+
+    # Need to determine if these should remain properties
+    # Or if they should be passed as arguments to Plot_Line
+    @property
+    def _binary_warped(self, value: np.ndarray):
+        self._binary_warped = value
+        self._histogram = np.sum(
+            self.binary_warped[self.binary_warped.shape[0] // 2 :, :], axis=0
+        )
+
+    def get_white_pixels(self):
+        # Returns an output image
+        NWINDOWS = self.consts["NWINDOWS"]
+        SEARCH_WIDTH = self.consts['SEARCH_WIDTH']
+
+        window_height = np.int32(self._binary_warped.shape[0] / NWINDOWS)
+        lane_inds = []
+
+        nonzero_pixels = self._binary_warped.nonzero()
+        nonzero_y = np.array(nonzero_pixels[0])
+        nonzero_x = np.array(nonzero_pixels[1])
+
+        empty_windows = 0
+        lane_base = np.argmax(self.histogram[:])
+
+        # Image to visualize output
+        out_img = repeat(self._binary_warped, 'h w -> h w c', repeat=3) * 255
+        ##These outputs need to be confirmed compatible
+        # out_img = (
+        #     np.dstack(
+        #         self._binary_warped, self._binary_warped, self._binary_warped
+        #     )
+        #     * 255
+        # )
+
+        for window in range(NWINDOWS):
+            window_dims = window * window_height
+            win_y_upper = self._binary_warped.shape[0] - window_dims
+            # One window height lower than win_y_higher
+            win_y_lower = win_y_upper - window_height
+            win_x_lower = lane_base - SEARCH_WIDTH
+            win_x_upper = lane_base + SEARCH_WIDTH
+
+            lower_coords = (win_x_lower, win_y_lower)
+            upper_coords = (win_x_upper, win_y_lower)
+            cv2.rectangle(
+                out_img,
+                lower_coords,
+                upper_coords,
+                self.consts["BOX_COLOR"],
+                self.consts["DRAW_THICKNESS"],
+            )
+
+            white_pix_inds = (
+                (nonzero_y >= win_y_lower)
+                & (nonzero_y < win_y_upper)
+                & (nonzero_x >= win_x_lower)
+                & (nonzero_x >= win_x_upper)
+            ).nonzero_pixels()[0]
+
+            # This should likely be moved into the if statement: leaving for now
+            lane_inds.append(white_pix_inds)
+            if len(white_pix_inds) > self.consts["MINPIXELS"]:
+                # np.mean will return a float: We need an exact value
+                lane_base = np.int32(np.mean(nonzero_x[white_pix_inds]))
+            else:
+                empty_windows += 1
+            if len(lane_inds) == 0:
+                return None
+            lane_array = np.concatenate(lane_inds)
+            lane_x_pos = nonzero_x[lane_array]
+            lane_y_pos = nonzero_y[lane_array]
+            # TODO: test if this statement is necessary
+            if lane_x_pos.any() and lane_y_pos.any():
+                self._fit = np.polyfit(
+                    lane_y_pos, lane_x_pos, self.consts["LANE_POLY_SIZE"]
+                )
+
+            out_img[nonzero_y[lane_array], nonzero_x[lane_array]] = (
+                self.consts["LANE_COLOR"]
+            )
+        return out_img
+
+    def plot_line(self) -> ifResult:
+        if not self._binary_warped:
+            raise Exception("no binary warp specified")
+
+        result = ifResult()
+
+        NWINDOWS = self.consts["NWINDOWS"]
+        window_height = np.int32(self._binary_warped.shape[0] / NWINDOWS)
+        out_img = self.get_white_pixels()
+        if out_img is None:
+            return result
+
+        ##Generates the search window area
+        window_img = np.zeros_like(out_img)
+        # Create linearly spaced points at each height, evaluate polynomial, create coordinates
+        x_val = np.arange(0, window_height, step=5).T
+        y_val = np.polyval(self.fit, x_val)
+        coords = np.concatenate((x_val, y_val), axis=0)
+        ##TODO: Test if this approach works
+        cv2.polylines(
+            out_img,
+            coords,
+            isClosed=False,
+            color=self.consts["LANE_COLOR"],
+            thickness=self.consts["DRAW_THICKNESS"],
+        )
+
+        # Calculating heading error by converting lane polynomial into line
+        ##TODO: Make this use the furthest found box. This way, we'll use the most accurate heading
+        y_lower = 0
+        y_upper = (NWINDOWS + 1) * window_height
+
+        result.slope = np.polyval(self.fit, y_lower) - np.polyval(
+            self.fit, y_upper
+        ) / (y_upper - y_lower)
+        result.result_img = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
+        result.heading = math.atan(result.slope)
+
+        return result
diff --git a/components/lane_follower/src/lane_follower.py b/components/lane_follower/src/lane_follower.py
new file mode 100644
index 000000000..429ca8901
--- /dev/null
+++ b/components/lane_follower/src/lane_follower.py
@@ -0,0 +1,20 @@
+import toml
+import cv2
+
+# import numpy as np
+
+
+class LaneFollower:
+
+    def __init__(
+        self, odom_sub, cam_left, cam_right, toml_path='Follower_Consts.toml'
+    ):
+        self.vidcap_left = cv2.VideoCapture(cam_left)
+        self.vidcap_right = cv2.VideoCapture(cam_right)
+        self.const = toml.load(toml_path)["Lane_Follower"]
+        # LOWER = np.array(self.const['lower_hsv'])
+        # UPPER = np.array(self.const['upper_hsv'])
+        # PTS1 = np.float32([tl, bl, tr, br])
+        # PTS2 = np.float32([[0, 0], [0, 480], [640, 0], [640, 480]])
+        # # Matrix to warp the image for birdseye window
+        # UNWARP = cv2.getPerspectiveTransform(pts1, pts2)
diff --git a/components/lane_follower/src/pyproject.toml b/components/lane_follower/src/pyproject.toml
new file mode 100644
index 000000000..15528d718
--- /dev/null
+++ b/components/lane_follower/src/pyproject.toml
@@ -0,0 +1,8 @@
+[project]
+version = '1.0'
+name = "individual-follower"
+authors = [
+  {name = "Vaibhav Hariani"},
+  {name = "Isaiah Rivera"},
+]
+description = "IGVC lane follower module for a single camera"