Update PlotQA_dataset.py

PlotQA_dataset.py

@@ -76,9 +76,9 @@ class PlotQA(datasets.GeneratorBasedBuilder):
                 "imgname": datasets.Value("string"),
                 "image": datasets.Image(),
                 "version": datasets.Value("string"),
-                "query": datasets.Value("string"),
+                "query": datasets.Sequence(datasets.Value("string")),
                 "query_token": datasets.Sequence(datasets.Value("string")),
-                "label": datasets.Value("string"),
+                "label": datasets.Sequence(datasets.Value("string")),
                 "img_ann": datasets.Value("string"),
                 ###
                 "image_index": datasets.Value("string"),
@@ -139,6 +139,24 @@ class PlotQA(datasets.GeneratorBasedBuilder):
             ),
         ]
 
+    def find_qa(self, annotation_v1_path:str, annotation_v2_path:str, img_idx):
+        with open(annotation_v1_path, "r", encoding="utf-8") as v1:
+            data_v1 = json.load(v1) 
+        
+        with open(annotation_v2_path, "r", encoding="utf-8") as v2:
+            data_v2 = json.load(v2)
+        
+        #1.
+        version = 1
+        _temp_item = []
+        for ele in data_v1:
+            if ele['image_index'] == img_idx:
+                _temp_item.append(ele)
+        for ele in data_v2:
+            if ele['image_index'] == img_idx:
+                _temp_item.append(ele)
+        return _temp_item
+        
     def _generate_examples(self, annotation_v1_path:str, annotation_v2_path:str, img_anno_path:str  ,images_path: str):
         #Load image folder
         # open file
@@ -146,52 +164,41 @@ class PlotQA(datasets.GeneratorBasedBuilder):
         # extracting file
         file.extractall('./imgs')
         file.close()
-        #_multi_anno = [annotation_v1_path, annotation_v2_path]
-        _multi_anno = [annotation_v1_path]
+        _multi_anno = [annotation_v1_path, annotation_v2_path]
+  
         idx = 0
+        with open(img_anno_path, "r", encoding="utf-8") as a:
+            img_data = json.load(a)
+        for ele in img_data:
+            item = {}
+            item["img_ann"] = ele
+            item["imgname"] = str(ele['image_index'])+'.png'
+            item['image'] = os.path.join('./imgs',item['imgname'])
+            item["query_token"] = []
+            qa_returns = find_qa(annotation_v1_path,annotation_v2_path, item['image_index'])
+            _question = []
+            _label = []
+            for pair in qa_returns:
+                _question.append(pair['question_string'])
+                _label.append(pair["answer"])
         
-        ###
-        for anno_path in _multi_anno:
-            with open(anno_path, "r", encoding="utf-8") as f:
-                data = json.load(f)
-                #returns the examples in the raw in json file
-                for idx in range(0,len(data['qa_pairs'])-1):
-                    item = copy.deepcopy(data['qa_pairs'][idx])
-                    item["imgname"] = item["image_index"]+'.png'
-                    item["image"] = os.path.join('./imgs',str(item["imgname"]))
-                    item["query_token"] = []
-                    item["query"] = item["question_string"]
-                    item["label"] = item["answer"]
-                    
-                    item["version"] = "temp"
-                    item["img_ann"] = "TBD"
-
-                    # item["image"] = os.path.join(images_path,item["imgname"])
-                    # item["query_token"] = []
-                    # item["table_name"] = os.path.splitext(item["imgname"])[0]+'.csv'
-                    # #item["table"] = os.path.join(table_path,item[idx]["table_name"])
-                    # file_name = os.path.basename(anno_path)
-                    # #Table load
-                    # df = pd.read_csv (os.path.join(table_path,item["imgname"].split('.')[0]+'.csv'))
-                    # item["table"] = df.to_dict()
-                    
-                    # file = os.path.splitext(file_name)
-                    # if file == "test_augmented":
-                    #     item["human"] = False
-                    # else:
-                    #     item["human"] = True
-                    
-                    # img_anot_file = os.path.splitext(item["imgname"])[0]+'.json'
-                    # img_anot = os.path.join(img_anno_path, img_anot_file)
-                    # with open(img_anot) as f:
-                    #     item["img_ann"] = json.load(f)
-                    """
-                    item['table'] = os.path.join(images_path,item["imgname"])
-                    # annotation
-                    item["img_anno"] = load json file... 
-                    t_path = os.path.join(table_path,item["table_name"])
-                    table_data = load_dataset("csv", data_files=[t_path])
-                    yield table_data
-                    """
-                    yield idx, item
-                    idx += 1
+            # file = os.path.splitext(file_name)
+            # if file == "test_augmented":
+            #     item["human"] = False
+            # else:
+            #     item["human"] = True
+            
+            # img_anot_file = os.path.splitext(item["imgname"])[0]+'.json'
+            # img_anot = os.path.join(img_anno_path, img_anot_file)
+            # with open(img_anot) as f:
+            #     item["img_ann"] = json.load(f)
+            """
+            item['table'] = os.path.join(images_path,item["imgname"])
+            # annotation
+            item["img_anno"] = load json file... 
+            t_path = os.path.join(table_path,item["table_name"])
+            table_data = load_dataset("csv", data_files=[t_path])
+            yield table_data
+            """
+            yield idx, item
+            idx += 1