Update main.py

main.py

@@ -19,35 +19,72 @@
 __version__ = "0.1.0"
 __license__ = "MIT"
 
+import pandas as pd
 import argparse
 from proyecto2.cloud_coverage import CloudCoverage
 from proyecto2.io import IO
 
 
 def main(image_path, export):
     """ Main entry point of the app """
+    # Newly added Feature(s)
+    """
+    Included print statement for info/warning/error messages in English
+    Added new feature to write computed CCI % values into a DataFrame
+    """ 
     # Read the image located in image_path.
     try:
         image = IO.read(image_path)
         IO.write(image, "test.jpg")
         print("Se recibió la imagen {}".format(image_path))
+        print("   ")
+        print("Translation: Picture was received {}".format(image_path))
     except IOError:
         print("No se pudo leer la imagen {}".format(image_path))
+        print("   ")
+        print("Translation: Image could not be read {}".format(image_path))
         exit(1)
 
     # Get and print the cloud coverage index.
     index, new_image = CloudCoverage.compute(image)
 
     print("El índice de cobertura nubosa es: {}".format(index))
+    print("   ")
+    print("Translation: The cloud cover index is: {}".format(index))
 
     # Save image if the export flag was received.
     if export:
         out_path = '.'.join(image_path.split('.')[:-1]) + "-seg.png"
         try:
             IO.write(new_image, out_path)
             print("Se guardó exitosamente la imagen {}".format(out_path))
+            print("   ")
+            print("Translation: Image was successfully saved {}".format(out_path))
         except:
             print("Ocurrió un error al guardar la imagen {}".format(out_path))
+            print("   ")
+            print("Translation: An error occurred while saving the image {}".format(out_path))
+
+    # Capture the computed CCI % Value into an output DataFrame
+    opt_df_path = image_path.replace(image_path.split("/",)[-1],"",) + 'Image-CCI.csv'
+    try:
+        df = pd.read_csv(opt_df_path)
+    except:
+        df_new = pd.DataFrame({'Image':[image_path], 'CCI':[index]})
+        df_new['Image'] = pd.Series(df_new['Image']).astype('str')
+        df_new['CCI'] = pd.Series(df_new['CCI']).astype('float')
+        df = df_new.copy()
+    else:
+        if df.loc[df['Image'] == image_path,].shape[0] > 0:
+            df.loc[df['Image'] == image_path, 'CCI'] = index
+            df['Image'] = pd.Series(df['Image']).astype('str')
+            df['CCI'] = pd.Series(df['CCI']).astype('float')
+        else:
+            df_new = pd.DataFrame({'Image':[image_path], 'CCI':[index]})
+            df_new['Image'] = pd.Series(df_new['Image']).astype('str')
+            df_new['CCI'] = pd.Series(df_new['CCI']).astype('float')
+            df = pd.concat([df, df_new], axis=0)
+    df.to_csv(opt_df_path, index=False)
 
 
 if __name__ == "__main__":
@@ -64,3 +101,4 @@ def main(image_path, export):
     args = parser.parse_args()
     main(args.image_path, bool(args.S))
 
+