Changed thresholding to adaptive thresholding

.gitignore

@@ -0,0 +1,4 @@
+build
+**/.vscode
+**/__pycache__
+**/.DS_Store

.gitmodules

@@ -1,3 +1,9 @@
 [submodule "gps"]
 	path = gps
 	url = ../gps/
+[submodule "YOLO"]
+	path = YOLO
+	url = https://github.com/Sooner-Rover-Team/YOLO
+[submodule "src/autonomous/gps"]
+	path = src/autonomous/gps
+	url = https://github.com/Sooner-Rover-Team/gps

.python-version

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+cpython@3.11.3

examples/ARTrackerTest/config.ini

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+[CONFIG]
+SWIFT_IP=10.0.0.222
+SWIFT_PORT=55556
+MBED_IP=10.0.0.101
+MBED_PORT=1001
+[ARTRACKER]
+#dpp is .040625 with logi
+DEGREES_PER_PIXEL=0.09375
+VDEGREES_PER_PIXEL = .125
+#Focal length was 1500 with logi
+FOCAL_LENGTH=435
+FOCAL_LENGTH30H=590
+FOCAL_LENGTH30V=470
+KNOWN_TAG_WIDTH=20
+FORMAT=XVID
+FRAME_WIDTH=1280
+FRAME_HEIGHT=720
+MAIN_CAMERA=2.3
+LEFT_CAMERA=2.4
+RIGHT_CAMERA=2.2
+[YOLO]
+#I am assuming that these are in the darknet folder
+WEIGHTS=soro.weights
+DATA=cfg/soro.data
+CFG=cfg/soro.cfg
+THRESHOLD=.25

examples/ARTrackerTest/newAr.py

@@ -0,0 +1,84 @@
+import cv2
+import cv2.aruco as aruco
+import numpy as np
+import configparser
+import os
+
+
+def preprocess_image(image):
+    # Convert the image to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Enhance contrast using histogram equalization
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    equalized = clahe.apply(gray)
+
+    # Apply Gaussian blur to reduce noise
+    blurred = cv2.GaussianBlur(equalized, (5, 5), 0)
+
+    # Apply adaptive thresholding to segment the image
+    print(type(blurred))
+    thresholded = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 4)
+
+    return thresholded
+
+def configCam(cam, configFile):
+        # Open the config file
+        config = configparser.ConfigParser(allow_no_value=True)
+        if not config.read(configFile):
+            print(f"ERROR OPENING AR CONFIG:", end="")
+            if os.path.isabs(configFile):
+                print(configFile)
+            else:
+                print("{os.getcwd()}/{configFile}")
+            exit(-2)
+
+        # Set variables from the config file
+        degreesPerPixel = float(config['ARTRACKER']['DEGREES_PER_PIXEL'])
+        vDegreesPerPixel = float(config['ARTRACKER']['VDEGREES_PER_PIXEL'])
+        focalLength = float(config['ARTRACKER']['FOCAL_LENGTH'])
+        focalLength30H = float(config['ARTRACKER']['FOCAL_LENGTH30H'])
+        focalLength30V = float(config['ARTRACKER']['FOCAL_LENGTH30V'])
+        knownMarkerWidth = float(config['ARTRACKER']['KNOWN_TAG_WIDTH'])
+        format = config['ARTRACKER']['FORMAT']
+        frameWidth = int(config['ARTRACKER']['FRAME_WIDTH'])
+        frameHeight = int(config['ARTRACKER']['FRAME_HEIGHT'])
+
+        # Set the camera properties
+        cam.set(cv2.CAP_PROP_FRAME_HEIGHT, frameHeight)
+        cam.set(cv2.CAP_PROP_FRAME_WIDTH, frameWidth)
+        cam.set(cv2.CAP_PROP_BUFFERSIZE, 1) # greatly speeds up the program but the writer is a bit wack because of this
+        cam.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(format[0], format[1], format[2], format[3]))
+
+if __name__ == "__main__":
+    # Create the camera object
+    cam = cv2.VideoCapture(1)
+    configCam(cam, "config.ini")
+
+    # Create the aruco dictionary
+    markerDict = aruco.getPredefinedDictionary(aruco.DICT_4X4_50)
+
+    while True:
+        # Read the image from the camera and convert to grayscale
+        ret, frame = cam.read()
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+        # Detect the markers
+        print(type(gray))
+        adapted_thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 3)
+
+        # Display the image
+        cv2.imshow('adapted thresh', adapted_thresh)
+        cv2.waitKey(1)
+        print(adapted_thresh[:][0])
+        (corners, markerIDs, rejected) = aruco.detectMarkers(adapted_thresh, markerDict)  
+        if len(corners) == 1 and markerIDs[0] == 1:
+            print(markerIDs)
+        else:
+            print("No markers found")
+            cv2.imshow('adapted thresh', adapted_thresh)
+            cv2.waitKey(1)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+

examples/ARTrackerTest/testARpic.py

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+import cv2
+import cv2.aruco as aruco
+import argparse
+
+# Parse the command line arguments
+parser = argparse.ArgumentParser(description="Test the ARTracker with a picture")
+parser.add_argument("file", help="The file to test the ARTracker with")
+args = parser.parse_args() # parse the arguments
+
+if __name__ == "__main__":
+    # Read the image from the file
+    if args.file.endswith(".jpg") or args.file.endswith(".png"):
+        image = cv2.imread(args.file)
+    else:
+        print("ERROR: File must be a jpg or png")
+        exit(-1)
+
+    # Show image
+    cv2.namedWindow('unprocessed image', cv2.WINDOW_KEEPRATIO)
+    cv2.imshow('unprocessed image', image)
+    cv2.resizeWindow('unprocessed image', image.shape[1] // 2, image.shape[0] // 2)
+
+    # Process the image
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    adapted_thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 3)
+    cv2.namedWindow('adapted thresh', cv2.WINDOW_KEEPRATIO)
+    cv2.resizeWindow('adapted thresh', image.shape[1] // 2, image.shape[0] // 2)
+    cv2.imshow('adapted thresh', adapted_thresh)
+
+    # Detect the markers
+    markerDict = aruco.getPredefinedDictionary(aruco.DICT_4X4_50)
+    (corners, markerIDs, rejected) = aruco.detectMarkers(adapted_thresh, markerDict)
+    if (markerIDs is not None) :
+        print("Marker found")
+        print("Found IDs:" + markerIDs)
+    else:
+        print("No markers found")
+
+    if cv2.waitKey(0) & 0xFF == ord('q'):
+        exit(0)
+

examples/ARTrackerTest/testARvid.py

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+import cv2
+import cv2.aruco as aruco
+from time import sleep
+import argparse
+
+# Parse the command line arguments
+parser = argparse.ArgumentParser(description="Test the ARTracker with a video or picture")
+parser.add_argument("file", help="The file to test the ARTracker with")
+args = parser.parse_args() # parse the arguments
+
+if __name__ == "__main__":    
+
+    # Import either a video or a picture
+    if args.file.endswith(".mp4") or args.file.endswith(".avi"):
+        file = cv2.VideoCapture(args.file)
+    else:
+        print("file must be a video, either .mp4 or .avi")
+        exit(-1)
+
+    # Play video or display picture
+    index = 0
+    markerTicks = 0
+    soonestFrame = 0
+    while True:
+        # Create the aruco dictionary
+        markerDict = aruco.getPredefinedDictionary(aruco.DICT_4X4_50)
+
+        # Capture and display the unprocessed frame
+        #sleep(1/45) # 30 fps NOTE: This does not accurately represent the actual fps of the video
+
+        ret, frame = file.read()
+        cv2.namedWindow('unprocessed frame', cv2.WINDOW_KEEPRATIO)
+        cv2.imshow('unprocessed frame', frame)
+        cv2.resizeWindow('unprocessed frame', frame.shape[1] // 2, frame.shape[0] // 2)
+
+        # Process the image
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+        # Process image by changing the contrast
+        """for i in range(40, 221, 60):
+            process_image = cv2.threshold(gray,i,255, cv2.THRESH_BINARY)[1]
+            (corners, markerIDs, rejected) = aruco.detectMarkers(process_image, markerDict)
+            if markerIDs is not None:
+                break
+        """
+
+        # Process image using Adaptive Thresholding
+        process_image = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 25, 3)
+
+        # Show the processed image
+        cv2.namedWindow('processed image', cv2.WINDOW_KEEPRATIO)
+        cv2.imshow('processed image', process_image)
+        cv2.resizeWindow('processed image', process_image.shape[1] // 2, process_image.shape[0] // 2)
+
+        # Detect the markers
+        (corners, markerIDs, rejected) = aruco.detectMarkers(process_image, markerDict)
+        if (markerIDs is not None) and (markerIDs[0] == 1):
+            if markerTicks == 0:
+                soonestFrame = index
+            print("Marker found")
+            print("Found IDs:" + str(markerIDs))
+            markerTicks += 1
+
+        print("Frame " + str(index))
+        index += 1
+        # Exit the program if the user presses 'q'
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            print("Number of times marker was found:" + str(markerTicks))
+            print("Soonest frame:" + str(soonestFrame))
+            break
+
+

pyproject.toml

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+[project]
+name = "autonomous"
+version = "0.1.0"
+description = "Add a short description here"
+dependencies = [
+    "opencv-contrib-python==4.6.0.66",
+    "flask>=2.3.2",
+]
+readme = "README.md"
+requires-python = ">= 3.10"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.rye]
+managed = true
+
+[tool.hatch.metadata]
+allow-direct-references = true
+
+[tool.rye.workspace]
+members = ["examples", "gps", "libs", "yolo", "RoverMap"]

runAutonomous.sh → run_autonomous.sh

@@ -1,4 +1,6 @@
 #! /bin/bash
+cd src/autonomous
+
 #Parses the config file below
 main=$(cat config.ini | grep MAIN_CAMERA)
 main=${main: -3}

src/RoverMap/.gitignore

@@ -0,0 +1,2 @@
+tiles/
+tiles.tar

src/RoverMap/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Benton Smith
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

src/RoverMap/README.md

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+# Map Server
+
+This houses both the HTML/CSS/JS frontend to render the map with the rover's coordinates,
+and the backend server that powers it.
+
+## Structure
+
+The frontend provides a leafletjs map which draws the rover's position on it.
+The map renderer uses image files served by `server.py`, and generated by
+the generating program in `RoverMapTileGenerator`.
+
+## Running and Integration
+
+You can run the server in standalone mode by running `python3 server.py`.
+
+To run it from python, import and call `start_map_server`.
+The flask app isn't wrapped nicely in a class (this would be a good refactor),
+so we really need to test this integration and make sure it works.
+
+The server sends the rover's gps coords to the web client,
+so the server will need to be supplied with those coords.
+Call `update_rover_coords` with an array of lat, lng like `[38.4065, -110.79147]`,
+the center of the mars thingy.
+
+There's an example in `example/updater.py` to go off of.
+
+## Accessing
+
+The server should open to port 5000, so you can access by connecting to `10.0.0.2:5000`.
+That port could change somehow, so you may need to check stdout to find the exact address and port.
+
+## Documentation
+
+This SoRo component is a new candidate for documentation! If you know markdown, and have a good idea about what's going on here, please feel free to [make a new page about it in the docs](https://sooner-rover-team.github.io/soro-documentation/html/new-page-guide.html)! :)

src/RoverMap/RoverMapTileGenerator/.gitignore

@@ -0,0 +1,3 @@
+node_modules/
+tiles/*
+*.tif