updated the segmentation model with the roboflow model for time being

app.py

@@ -1,3 +1,4 @@
+from inference_sdk import InferenceHTTPClient
 from ultralytics import YOLO
 import cv2
 from stockfish import Stockfish
@@ -5,6 +6,10 @@ import os
 import numpy as np 
 import streamlit as st
 
+CLIENT = InferenceHTTPClient(
+    api_url="https://outline.roboflow.com",
+    api_key="9Ez1hwfkqVa2h6pRQQHH"
+)
 
 # Constants
 FEN_MAPPING = {
@@ -91,14 +96,58 @@ def main():
 
         # Load the YOLO models
         model = YOLO("fine_tuned_on_all_data.pt")  # Replace with your trained model weights file
-        seg_model = YOLO("segmentation.pt")
-
-        # Load and process the image
-        img = cv2.imread(temp_file_path)
-        r = seg_model.predict(source=temp_file_path)
-        xyxy = r[0].boxes.xyxy
-        x_min, y_min, x_max, y_max = map(int, xyxy[0])
-        new_img = img[y_min:y_max, x_min:x_max]
+
+        
+        # seg_model = YOLO("segmentation.pt")
+
+        # # Load and process the image
+        # img = cv2.imread(temp_file_path)
+        # r = seg_model.predict(source=temp_file_path)
+        # xyxy = r[0].boxes.xyxy
+        # x_min, y_min, x_max, y_max = map(int, xyxy[0])
+        # new_img = img[y_min:y_max, x_min:x_max]
+
+
+
+        
+        result = CLIENT.infer(temp_file_path, model_id="chessboard-segmentation/1")
+        
+        image = cv2.imread(temp_file_path)
+        
+        if image is None:
+            st.write("Error: Image not loaded.")
+        
+        
+        prediction_data = result
+        
+        
+        if not prediction_data.get('predictions'):
+            st.write("No board found.")
+            return 
+        else:
+            for prediction in prediction_data.get('predictions', []):
+                if 'x' in prediction and 'y' in prediction and 'width' in prediction and 'height' in prediction:
+                    x, y, w, h = prediction['x'], prediction['y'], prediction['width'], prediction['height']
+                    # print(f"Bounding box coordinates: ({x}, {y}), width={w}, height={h}")
+            
+                    x1, y1 = int(x - w / 2), int(y - h / 2)
+                    x2, y2 = int(x + w / 2), int(y + h / 2)
+            
+                    src_pts = np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]], dtype="float32")
+                    # print(f"Source Points: {src_pts}")
+            
+                    chessboard_size = 600
+                    dst_pts = np.array([[0, 0], [chessboard_size - 1, 0], [chessboard_size - 1, chessboard_size - 1], [0, chessboard_size - 1]], dtype="float32")
+                    # print(f"Destination Points: {dst_pts}")
+            
+                    matrix = cv2.getPerspectiveTransform(src_pts, dst_pts)
+            
+                    # Apply the perspective warp
+                    transformed_chessboard = cv2.warpPerspective(image, matrix, (chessboard_size, chessboard_size))
+            
+                    # Convert images to RGB for display
+                    new_img = cv2.cvtColor(transformed_chessboard, cv2.COLOR_BGR2RGB)
+
 
         # Resize the image to 224x224
         image = cv2.resize(new_img, (224, 224))