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aimbot.py

@@ -1,16 +1,81 @@
-import tensorflow as tf
-import tensorflow_hub as hub
-import numpy as np
 import pyautogui
 import win32api, win32con, win32gui
 import cv2
 import math
 import time
+import argparse
+import os
+import json
+import numpy as np
+from threading import Lock
+
+# printing only warnings and error messages
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1"
+
+try:
+    import tensorflow as tf
+    from PIL import Image
+except ImportError:
+    raise ImportError("ERROR: Failed to import libraries. Please refer to READEME.md file\n")
+
+EXPORT_MODEL_VERSION = 1
+
+
+class TFModel:
+    def __init__(self, dir_path) -> None:
+        # Assume model is in the parent directory for this file
+        self.model_dir = os.path.dirname(dir_path)
+        # make sure our exported SavedModel folder exists
+        with open(os.path.join(self.model_dir, "signature.json"), "r") as f:
+            self.signature = json.load(f)
+        self.model_file = os.path.join(self.model_dir, self.signature.get("filename"))
+        if not os.path.isfile(self.model_file):
+            raise FileNotFoundError(f"Model file does not exist")
+        self.inputs = self.signature.get("inputs")
+        self.outputs = self.signature.get("outputs")
+        self.lock = Lock()
+
+        # loading the saved model
+        self.model = tf.saved_model.load(tags=self.signature.get("tags"), export_dir=self.model_dir)
+        self.predict_fn = self.model.signatures["serving_default"]
+
+        # Look for the version in signature file.
+        # If it's not found or the doesn't match expected, print a message
+        version = self.signature.get("export_model_version")
+        if version is None or version != EXPORT_MODEL_VERSION:
+            print(
+                f"There has been a change to the model format. Please use a model with a signature 'export_model_version' that matches {EXPORT_MODEL_VERSION}."
+            )
+
+    def predict(self, image: Image.Image) -> dict:
+        with self.lock:
+            # create the feed dictionary that is the input to the model
+            feed_dict = {}
+            # first, add our image to the dictionary (comes from our signature.json file)
+            feed_dict[list(self.inputs.keys())[0]] = tf.convert_to_tensor(image)
+            # run the model!
+            outputs = self.predict_fn(**feed_dict)
+            # return the processed output
+            return self.process_output(outputs)
+
+    def process_output(self, outputs) -> dict:
+        # do a bit of postprocessing
+        out_keys = ["label", "confidence"]
+        results = {}
+        # since we actually ran on a batch of size 1, index out the items from the returned numpy arrays
+        for key, tf_val in outputs.items():
+            val = tf_val.numpy().tolist()[0]
+            if isinstance(val, bytes):
+                val = val.decode()
+            results[key] = val
+        confs = results["Confidences"]
+        labels = self.signature.get("classes").get("Label")
+        output = [dict(zip(out_keys, group)) for group in zip(labels, confs)]
+        sorted_output = {"predictions": sorted(output, key=lambda k: k["confidence"], reverse=True)}
+        return sorted_output
 
-dirname = os.path.dirname(__file__)
-detector = tf.saved_model.load(dirname)
 size_scale = 3
-
+model = TFModel(dir_path=os.path.dirname(__file__))
 while True:
     # Get rect of Window
     hwnd = win32gui.FindWindow(None, 'Roblox')
@@ -25,7 +90,7 @@ while True:
     img_w, img_h = image.shape[2], image.shape[1]
 
     # Detection
-    result = detector(image)
+    outputs = model.predict(image)
     result = {key:value.numpy() for key,value in result.items()}
     boxes = result['detection_boxes'][0]
     scores = result['detection_scores'][0]
@@ -75,8 +140,4 @@ while True:
         time.sleep(0.1)
         win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, x, y, 0, 0)
 
-    #ori_img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)
-    #cv2.imshow("ori_img", ori_img)
-    #cv2.waitKey(1)
-
     time.sleep(0.1)