Update app.py

app.py

@@ -108,31 +108,88 @@ 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  # Load the video file here
+        # output_image = None
+        # 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)
+
+        # ax.set_title('Number of Objects over Seconds', fontsize=20, pad=20)  # Increase padding for the title
+        # ax.set_xlabel('Second', fontsize=16)  # Increase font size
+        # ax.set_ylabel('Object 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
+        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']
+        
+            # Check if we have enough points for interpolation
+            if len(x) > 1:
+                # 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
+        if interpolated_data:
+            interpolated_df = pd.concat(interpolated_data)
+        else:
+            interpolated_df = pd.DataFrame(columns=['frame', 'count', 'label'])
+        
         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('Number of Objects over Seconds', fontsize=20, pad=20)  # Increase padding for the title
         ax.set_xlabel('Second', fontsize=16)  # Increase font size
         ax.set_ylabel('Object 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 plot is visible
 
-        plt.tight_layout()  # Ensure the entire
     return output_image, output_video, fig