Update app.py

app.py

@@ -11,6 +11,7 @@ import cv2
 import numpy as np
 import matplotlib.pyplot as plt
 import threading
+from scipy.interpolate import make_interp_spline
 
 should_continue = True
 
@@ -107,33 +108,62 @@ def yolov9_inference(model_id, img_path=None, vid_path=None, tracking_algorithm
         plt.tight_layout()  # Ensure the entire plot fits into the figure area
         #ax.set_facecolor('#D3D3D3')
     elif output_extension.lower() in vid_extensions:
-        output_video = output_path  # Load the video file here
+        output_video = output_path
         output_image = None
+        
+        # Interpolation preprocessing
+        interpolated_data = []
+        
+        labels = frame_counts_df['label'].unique()
+        for label in labels:
+            df_label = frame_counts_df[frame_counts_df['label'] == label]
+            
+            # Sort data by frame to ensure smooth interpolation
+            df_label = df_label.sort_values('frame')
+        
+            # Original data points
+            x = df_label['frame']
+            y = df_label['count']
+        
+            # Create spline interpolation
+            x_smooth = np.linspace(x.min(), x.max(), 500)
+            spline = make_interp_spline(x, y, k=3)  # Cubic spline interpolation
+            y_smooth = spline(x_smooth)
+        
+            # Append the smoothed data to the list
+            interpolated_data.append(pd.DataFrame({'frame': x_smooth, 'count': y_smooth, 'label': label}))
+
+    # Concatenate all interpolated data into a single DataFrame
+        interpolated_df = pd.concat(interpolated_data)
+        
         plt.style.use("ggplot")
         fig, ax = plt.subplots(figsize=(10, 6))
-        #for label in labels:
-            #df_label = frame_counts_df[frame_counts_df['label'] == label]
-        sns.lineplot(ax = ax, data = frame_counts_df,  x = 'frame', y = 'count', hue = 'label', palette=palette,linewidth=2.5)
-
+        
+        # Plot using Seaborn
+        sns.lineplot(ax=ax, data=interpolated_df, x='frame', y='count', hue='label', palette=palette, linewidth=2.5)
+        
         ax.set_title('Count of Labels over Frames', fontsize=20, pad=20)  # Increase padding for the title
         ax.set_xlabel('Frame', fontsize=16)  # Increase font size
         ax.set_ylabel('Count', fontsize=16)  # Increase font size
         ax.tick_params(axis='x', labelsize=12)  # Increase label size for x-axis
         ax.tick_params(axis='y', labelsize=12)  # Increase label size for y-axis
-
+        
         # Add grid but make it lighter and put it behind bars
         ax.grid(True, linestyle=':', linewidth=0.6, color='gray', alpha=0.6)
         ax.set_axisbelow(True)
-
+        
         # Change the background color to a lighter shade
         ax.set_facecolor('#F0F0F0')
-
+        
         # Add a legend with a smaller font size
         ax.legend(fontsize=10)
-
-        plt.tight_layout()  # Ensure the entire
+        
+        plt.tight_layout()  # Ensure the entire plot is visible
+        
+        # Return the figure, output_image, and output_video
     return output_image, output_video, fig
 
+
 def app():
     img = Image.open('./img_examples/classes.png')
     img = img.resize((410, 260), Image.Resampling.LANCZOS)