Idk

rpi/board-detector/debug.py

@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from paths import *
+
+
+def main():
+    model = YOLO(model_path)
+
+    # Load image
+    image = cv2.imread(img_path)
+    if image is None:
+        print(f"Failed to read {img_path}")
+        return
+
+    height, width = image.shape[:2]
+
+    warped = image  # For now assume top-down view
+
+    # Run YOLO detection
+    results = model(warped)
+    res = results[0]
+
+    debug_img = res.plot()  # draws boxes around detected objects
+    cv2.imshow("Detections", debug_img)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    main()

rpi/board-detector/main.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+from paths import *
+
+from ultralytics import YOLO
+import cv2
+
+# Map class names to FEN characters
+class_to_fen = {
+    'w_pawn': 'P',
+    'w_knight': 'N',
+    'w_bishop': 'B',
+    'w_rook': 'R',
+    'w_queen': 'Q',
+    'w_king': 'K',
+    'b_pawn': 'p',
+    'b_knight': 'n',
+    'b_bishop': 'b',
+    'b_rook': 'r',
+    'b_queen': 'q',
+    'b_king': 'k',
+}
+
+def prediction_to_fen(results, width, height):
+
+    # Initialize empty board
+    board = [['' for _ in range(8)] for _ in range(8)]
+
+    # Iterate through predictions
+    for result in results:
+        for box, cls in zip(result.boxes.xyxy, result.boxes.cls):
+            x1, y1, x2, y2 = box.tolist()
+            class_name = model.names[int(cls)]
+            fen_char = class_to_fen.get(class_name)
+
+            if fen_char:
+                # Compute board square
+                col = int((x1 + x2) / 2 / (width / 8))
+                row = 7 - int((y1 + y2) / 2 / (height / 8))
+                board[row][col] = fen_char
+                print(f"[{class_name}] {fen_char} {row} {col}")
+
+    # Convert board to FEN
+    fen_rows = []
+    for row in board:
+        fen_row = ''
+        empty_count = 0
+        for square in row:
+            if square == '':
+                empty_count += 1
+            else:
+                if empty_count > 0:
+                    fen_row += str(empty_count)
+                    empty_count = 0
+                fen_row += square
+        if empty_count > 0:
+            fen_row += str(empty_count)
+        fen_rows.append(fen_row)
+
+    # Join rows into a FEN string (default: white to move, all castling rights, no en passant)
+    fen_string = '/'.join(fen_rows) + ' w KQkq - 0 1'
+    return fen_string
+
+
+if __name__ == "__main__":
+
+    img = cv2.imread(img_path)
+    height, width = img.shape[:2]
+
+    model = YOLO(model_path)
+    results = model.predict(source=img_path, conf=0.5)
+
+    #fen = prediction_to_fen(results, height, width)
+    #print("Predicted FEN:", fen)
+
+    annotated_image = results[0].plot()  # Annotated image as NumPy array
+    cv2.namedWindow("YOLO Predictions", cv2.WINDOW_NORMAL)  # make window resizable
+    cv2.imshow("YOLO Predictions", annotated_image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()

rpi/board-detector/paths.py

@@ -0,0 +1,3 @@
+model_path = "C:/Users/Laurent/Desktop/board-mate/rpi/assets/models/epoch-130.pt"
+#img_path = "./test/4.jpg"
+img_path = "../training/datasets/unified/train/images/WIN_20221220_11_27_27_Pro_jpg.rf.4f01cb68c8944ef1c4c7dc57847b4cd3.jpg"

rpi/requirements.txt

@@ -10,3 +10,6 @@ requests
 python-dotenv
 pyyaml
 pyserial
+opencv-python
+numpy
+ultralytics

rpi/scripts/timer/grove_rgb_lcd.py

@@ -111,7 +111,7 @@ def create_char(location, pattern):
 
 # example code
 if __name__=="__main__":
-    setText("Hello world\nThis is an LCD test")
+    setText("Hello world\nThis is an LCD chesscog-bck")
     setRGB(0,128,64)
     time.sleep(2)
     for c in range(0,255):

rpi/training/chess.yaml

@@ -0,0 +1,6 @@
+train: C:/Users/Laurent/Desktop/board-mate/rpi/training/datasets/roboflow/labels-bck-bck-bck
+val: C:/Users/Laurent/Desktop/board-mate/rpi/training/datasets/roboflow/labels-bck-bck-bck
+
+nc: 12
+names: ['w_pawn','w_knight','w_bishop','w_rook','w_queen','w_king',
+        'b_pawn','b_knight','b_bishop','b_rook','b_queen','b_king']

rpi/training/datasets/chesscog/data.yaml

@@ -0,0 +1,7 @@
+train: ../train/
+val: ../valid/
+test: ../test/
+
+nc: 12
+names: ['w_pawn','w_knight','w_bishop','w_rook','w_queen','w_king',
+        'b_pawn','b_knight','b_bishop','b_rook','b_queen','b_king']

rpi/training/datasets/unified/data.yaml

@@ -0,0 +1,7 @@
+train: ../train/
+val: ../valid/
+test: ../test/
+
+nc: 12
+names: ['w_pawn','w_knight','w_bishop','w_rook','w_queen','w_king',
+        'b_pawn','b_knight','b_bishop','b_rook','b_queen','b_king']

rpi/training/decrease_labels.py

@@ -0,0 +1,36 @@
+import os
+
+# --------------------------
+# Configuration
+# --------------------------
+labels_dir = "datasets/visiope/test/labels"
+
+# --------------------------
+# Process each label file
+# --------------------------
+for filename in os.listdir(labels_dir):
+    if not filename.endswith(".txt"):
+        continue
+
+    txt_path = os.path.join(labels_dir, filename)
+    new_lines = []
+
+    with open(txt_path, "r") as f:
+        lines = f.readlines()
+
+    for line in lines:
+        parts = line.strip().split()
+        if len(parts) < 5:
+            continue  # skip invalid lines
+        cls = int(parts[0])
+        cls -= 1  # subtract 1 from class index
+        cls = max(cls, 0)  # ensure no negative indices
+        new_lines.append(" ".join([str(cls)] + parts[1:]))
+
+    # Overwrite file with updated indices
+    with open(txt_path, "w") as f:
+        f.write("\n".join(new_lines))
+
+    print(f"Updated {filename}")
+
+print("All label files have been adjusted!")

rpi/training/labelizer.py

@@ -0,0 +1,70 @@
+import os
+import cv2
+from ultralytics import YOLO
+
+# --------------------------
+# Configuration
+# --------------------------
+model_path = "models/bck/best-3.pt"  # your trained YOLO model
+images_dir = "C:/Users/Laurent/Desktop/board-mate/rpi/training/datasets/universe/train/images"
+labels_dir = "C:/Users/Laurent/Desktop/board-mate/rpi/training/datasets/universe/train/labels"
+img_width = 640
+img_height = 640
+
+os.makedirs(labels_dir, exist_ok=True)
+
+# --------------------------
+# Load model
+# --------------------------
+model = YOLO(model_path)
+
+# --------------------------
+# Mapping YOLO class index -> piece name (optional)
+# --------------------------
+names = ['w_pawn','w_knight','w_bishop','w_rook','w_queen','w_king',
+         'b_pawn','b_knight','b_bishop','b_rook','b_queen','b_king']
+
+# --------------------------
+# Process images
+# --------------------------
+for img_file in os.listdir(images_dir):
+    if not img_file.lower().endswith((".png", ".jpg", ".jpeg")):
+        continue
+
+    img_path = os.path.join(images_dir, img_file)
+    img = cv2.imread(img_path)
+    if img is None:
+        print(f"Failed to read {img_file}")
+        continue
+
+    height, width = img.shape[:2]
+
+    # Run YOLO detection
+    results = model(img)
+    res = results[0]
+
+    lines = []
+    boxes = res.boxes.xyxy.cpu().numpy()  # [x1, y1, x2, y2]
+    classes = res.boxes.cls.cpu().numpy()
+    confs = res.boxes.conf.cpu().numpy()
+
+    for box, cls, conf in zip(boxes, classes, confs):
+        if conf < 0.5:  # skip low-confidence predictions
+            continue
+
+        x1, y1, x2, y2 = box
+        x_center = (x1 + x2) / 2 / width
+        y_center = (y1 + y2) / 2 / height
+        w_norm = (x2 - x1) / width
+        h_norm = (y2 - y1) / height
+
+        lines.append(f"{int(cls)} {x_center:.6f} {y_center:.6f} {w_norm:.6f} {h_norm:.6f}")
+
+    # Save YOLO .txt file with same basename as image
+    txt_path = os.path.join(labels_dir, os.path.splitext(img_file)[0] + ".txt")
+    with open(txt_path, "w") as f:
+        f.write("\n".join(lines))
+
+    print(f"Pre-labeled {img_file} -> {txt_path}")
+
+print("All images have been pre-labeled!")

rpi/training/move_image.py

@@ -0,0 +1,25 @@
+import os
+import shutil
+
+# ----------------------------
+# Configuration
+# ----------------------------
+
+source_folder = "datasets/twhpv/valid/images"
+destination_folder = "datasets/_unified/valid/images"
+
+os.makedirs(destination_folder, exist_ok=True)
+
+# Supported image extensions
+image_extensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif"]
+
+# ----------------------------
+# Copy images
+# ----------------------------
+for filename in os.listdir(source_folder):
+    if any(filename.lower().endswith(ext) for ext in image_extensions):
+        src_path = os.path.join(source_folder, filename)
+        dst_path = os.path.join(destination_folder, filename)
+        shutil.copy2(src_path, dst_path)  # copy2 preserves metadata
+
+print(f"All images copied to '{destination_folder}'")

rpi/training/sort_labels.py

@@ -0,0 +1,59 @@
+import os
+
+# ----------------------------
+# Configuration
+# ----------------------------
+
+src_dir = "datasets/visiope/test/labels"
+dest_dir = "datasets/_unified/test/labels"
+
+os.makedirs(dest_dir, exist_ok=True)
+
+# Reference class order you want to follow
+"""[
+'w_pawn','w_knight','w_bishop','w_rook','w_queen','w_king',
+'b_pawn','b_knight','b_bishop','b_rook','b_queen','b_king'
+]"""
+
+reference_classes = [
+    'w_pawn','w_knight','w_bishop','w_rook','w_queen','w_king',
+    'b_pawn','b_knight','b_bishop','b_rook','b_queen','b_king'
+]
+
+# Current class order in your dataset (change this to match your dataset!)
+current_classes = ['bishop', 'black-bishop', 'black-king', 'black-knight', 'black-pawn', 'black-queen', 'black-rook',
+        'white-bishop', 'white-king', 'white-knight', 'white-pawn', 'white-queen', 'white-rook']
+
+# ----------------------------
+# Build index mapping
+# ----------------------------
+index_map = {current_classes.index(cls): reference_classes.index(cls) for cls in current_classes}
+
+# ----------------------------
+# Process each label file
+# ----------------------------
+
+count = 0
+for filename in os.listdir(src_dir):
+    if filename.endswith(".txt"):
+        input_path = os.path.join(src_dir, filename)
+        output_path = os.path.join(dest_dir, filename)
+
+        with open(input_path, "r") as f:
+            lines = f.readlines()
+
+        new_lines = []
+        for line in lines:
+            parts = line.strip().split()
+            old_idx = int(parts[0])
+            new_idx = index_map[old_idx]
+            new_lines.append(" ".join([str(new_idx)] + parts[1:]))
+
+        with open(output_path, "w") as f:
+            f.write("\n".join(new_lines))
+
+        if count%100 == 0:
+            print(count)
+        count += 1
+
+print(f"All labels remapped and saved to '{dest_dir}'")

rpi/training/training.py

@@ -0,0 +1,18 @@
+from ultralytics import YOLO
+
+def main():
+    model = YOLO("models/yolo11n.pt")
+    model.train(
+        data="./datasets/unified/data.yaml",
+        epochs=200,
+        patience=30,
+        imgsz=640,
+        batch=12,
+        device=0,
+        project="result",
+        name="unified-training",
+        exist_ok=True
+    )
+
+if __name__ == "__main__":
+    main()