Update app_parallel.py

app_parallel.py

@@ -109,7 +109,7 @@ def process_chunk(audio_chunk, preprocessed_data, args):
 
     first_coeff_path = preprocessed_data["first_coeff_path"]
     crop_pic_path = preprocessed_data["crop_pic_path"]
-    crop_info_path = preprocessed_data["crop_info_path"]
+    crop_info_path = preprocessed_data["crop_info"]
     with open(crop_info_path , "rb") as f:
             crop_info = pickle.load(f)
 
@@ -225,15 +225,31 @@ def run_preprocessing(args):
     global path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting
     first_frame_dir = os.path.join(args.result_dir, 'first_frame_dir')
     os.makedirs(first_frame_dir, exist_ok=True)
-    fixed_temp_dir = "/home/user/app/preprocess_data/"
-    # os.makedirs(fixed_temp_dir, exist_ok=True)
+
+    # Check if preprocessed data already exists
+    fixed_temp_dir = "C:/Users/fd01076/Downloads/preprocess_data"
+    os.makedirs(fixed_temp_dir, exist_ok=True)
     preprocessed_data_path = os.path.join(fixed_temp_dir, "preprocessed_data.pkl")
 
     if os.path.exists(preprocessed_data_path) and args.image_hardcoded == "yes":
-        print("Loading preprocessed data...")
         with open(preprocessed_data_path, "rb") as f:
             preprocessed_data = pickle.load(f)
         print("Loaded existing preprocessed data from:", preprocessed_data_path)
+    else:
+        preprocess_model = CropAndExtract(path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting, args.device)
+        first_coeff_path, crop_pic_path, crop_info = preprocess_model.generate(args.source_image, first_frame_dir, args.preprocess, source_image_flag=True)
+
+        if not first_coeff_path:
+            raise Exception("Failed to get coefficients")
+
+        # Save the preprocessed data
+        preprocessed_data = {
+            "first_coeff_path": first_coeff_path,
+            "crop_pic_path": crop_pic_path,
+            "crop_info": crop_info
+        }
+        with open(preprocessed_data_path, "wb") as f:
+            pickle.dump(preprocessed_data, f)
 
     return preprocessed_data