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detact_color.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:94ca7c177549c93d9d858748c85c360b029e19cb2c51143ee80c7575fe3632c0
+size 65100

detact_icon.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:23bc186a65c405d0ac8d3a84749ac5aa770980920300f9391b98fd2e774a5df3
+size 67139

ocr.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:74cbf1180c2263f34a723de1da93bd4d07dae8d3e0804a9c3cf00e608f3cad0a
+size 200305

split.py

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+from glob import glob
+import os
+import json 
+import random
+
+train_cnt = 0
+test_cnt = 0
+val_cnt = 0
+combined_cnt = 0
+
+train_final = {}
+test_final = {}
+val_final = {}
+combined_final = {}
+for fn in glob("data/tasks/*/*"):
+    print(fn)
+    app_name = fn.split('/')[3]
+    app_type = fn.split('/')[2]
+    for i in range(1,10):
+        image = f"data/data/{app_type}/{app_name}/screen_{i}.png"
+        if os.path.exists(image) == False:
+            if i == 1:
+                print(image)
+            continue
+        ocr = f"ocr/{app_type}/{app_name}/screen_{i}.json"
+        assert os.path.exists(ocr), ocr
+        color = f"detact_color/{app_type}/{app_name}/screen_{i}.json"
+        assert os.path.exists(color), color
+        icon = f"detact_icon/{app_type}/{app_name}/screen_{i}.json"
+        assert os.path.exists(icon), icon
+        box = f"data/metadata/{app_type}/boxes/{app_name}/screen_{i}.json"
+        assert os.path.exists(box), box
+        script_to_task_fn = {}
+        cnt = 0
+        for task_fn in glob(f"{fn}/task_{i}.*.txt"):
+            txt =  '\n'.join(open(task_fn).read().strip().split('\n')[1:])
+            if txt not in script_to_task_fn:
+                script_to_task_fn[txt] = []
+            script_to_task_fn[txt].append(task_fn)
+            cnt += 1
+        
+            
+        for k in script_to_task_fn:
+            c_tasks = script_to_task_fn[k]
+            rand = random.randint(1,10)
+            if rand <= 7:
+                for c in c_tasks:
+                    train_final[train_cnt] = {"task": c, "image": image, "ocr": ocr, "color": color, "icon": icon, "box": box}
+                    train_cnt += 1
+                    combined_final[combined_cnt] = {"task": c, "image": image, "ocr": ocr, "color": color, "icon": icon, "box": box}
+                    combined_cnt += 1
+            elif rand == 8:
+                for c in c_tasks:
+                    val_final[val_cnt] = {"task": c, "image": image, "ocr": ocr, "color": color, "icon": icon, "box": box}
+                    val_cnt += 1
+                    combined_final[combined_cnt] = {"task": c, "image": image, "ocr": ocr, "color": color, "icon": icon, "box": box}
+                    combined_cnt += 1
+            elif rand > 8:
+                for c in c_tasks:
+                    test_final[test_cnt] = {"task": c, "image": image, "ocr": ocr, "color": color, "icon": icon, "box": box}
+                    test_cnt += 1
+                    combined_final[combined_cnt] = {"task": c, "image": image, "ocr": ocr, "color": color, "icon": icon, "box": box}
+                    combined_cnt += 1
+                    
+            
+            
+print(train_cnt)
+print(val_cnt)
+print(test_cnt)
+print(combined_cnt)
+
+json.dump(train_final, open('train.json', "w"), indent=4)
+json.dump(val_final, open("val.json","w"), indent=4)
+json.dump(test_final, open("test.json","w"), indent=4)
+json.dump(combined_final, open('combined.json','w'), indent=4)