Update run.py

azure-pipelines.yml

@@ -0,0 +1,129 @@
+# Python package
+# Create and test a Python package on multiple Python versions.
+# Add steps that analyze code, save the dist with the build record, publish to a PyPI-compatible index, and more:
+# https://docs.microsoft.com/azure/devops/pipelines/languages/python
+
+trigger:
+- main
+
+pool:
+  vmImage: ubuntu-latest
+
+strategy:
+  matrix:
+    Python38:
+      python.version: '3.8'
+    Python39:
+      python.version: '3.9'
+    Python310:
+      python.version: '3.10'
+    Python311:
+      python.version: '3.11'
+
+steps:
+- task: UsePythonVersion@0
+  inputs:
+    versionSpec: '$(python.version)'
+  displayName: 'Use Python $(python.version)'
+
+- script: |
+    python -m pip install --upgrade pip
+    pip install -r requirements.txt
+  displayName: 'Install dependencies'
+
+- script: |
+    pip install pytest pytest-azurepipelines
+    pytest
+  displayName: 'pytest'
++@torch.inference_mode()
++def perform_detection(model, image, tracker=None):
++    if tracker is not None:
++        results = model.predict(
++            source=image,
++            cfg="logic/tracker.yaml",
++            imgsz=cfg.ai_model_image_size,
++            stream=True,
++            conf=cfg.AI_conf,
++            iou=0.5,
++            device=cfg.AI_device,
++            half=False if "cpu" in cfg.AI_device else True,
++            max_det=20,
++            agnostic_nms=False,
++            augment=False,
++            vid_stride=False,
++            visualize=False,
++            verbose=False,
++            show_boxes=False,
++            show_labels=False,
++            show_conf=False,
++            save=False,
++            show=False)
++        
++        for result in results:
++            # Convert results to detections
++            detections = sv.Detections.from_ultralytics(result)
++            tracked_detections = byte_tracker.update_with_detections(detections)
++            return tracked_detections
++    else:
++        result = next(model.predict(
++            source=image,
++            cfg="logic/game.yaml",
++            imgsz=cfg.ai_model_image_size,
++            stream=True,
++            conf=cfg.AI_conf,
++            iou=0.5,
++            device=cfg.AI_device,
++            half=False if "cpu" in cfg.AI_device else True,
++            max_det=20,
++            agnostic_nms=False,
++            augment=False,
++            vid_stride=False,
++            visualize=False,
++            verbose=False,
++            show_boxes=False,
++            show_labels=False,
++            show_conf=False,
++            save=False,
++            show=False))
++        
++        return result
++
++def init():
++    run_checks()
++    
++    try:
++        model = YOLO(f"models/{cfg.AI_model_name}", task="detect")
++    except Exception as e:
++        print("An error occurred when loading the AI model:\n", e)
++        quit(0)
++        
++    while True:
++        image = capture.get_new_frame()
++        
++        if image is not None:
++            if cfg.circle_capture:
++                image = capture.convert_to_circle(image)
++            
++            if cfg.show_window or cfg.show_overlay:
++                visuals.queue.put(image)
++                
++            result = perform_detection(model, image, byte_tracker)
++
++            if hotkeys_watcher.app_pause == 0:
++                frameParser.parse(result)
++
++if __name__ == "__main__":
++    init()
++    
++    from ultralytics import YOLO
++import torch
++
++from logic.config_watcher import cfg
++from logic.capture import capture
++from logic.visual import visuals
++from logic.frame_parser import frameParser
++from logic.hotkeys_watcher import hotkeys_watcher
++from logic.checks import run_checks
++import supervision as sv
++    
++byte_tracker = sv.ByteTrack()

run.py

@@ -89,4 +89,189 @@ def init():
                 frameParser.parse(result)
 
 if __name__ == "__main__":
-    init()
+    init()
+
+    from ultralytics import YOLO
+import torch
+
+from logic.config_watcher import cfg
+from logic.capture import capture
+from logic.visual import visuals
+from logic.frame_parser import frameParser
+from logic.hotkeys_watcher import hotkeys_watcher
+from logic.checks import run_checks
+import supervision as sv
+
+byte_tracker = sv.ByteTrack()
+
+@torch.inference_mode()
+def perform_detection(model, image, tracker=None):
+    if tracker is not None:
+        results = model.predict(
+            source=image,
+            cfg="logic/tracker.yaml",
+            imgsz=cfg.ai_model_image_size,
+            stream=True,
+            conf=cfg.AI_conf,
+            iou=0.5,
+            device=cfg.AI_device,
+            half=False if "cpu" in cfg.AI_device else True,
+            max_det=20,
+            agnostic_nms=False,
+            augment=False,
+            vid_stride=False,
+            visualize=False,
+            verbose=False,
+            show_boxes=False,
+            show_labels=False,
+            show_conf=False,
+            save=False,
+            show=False)
+
+        for result in results:
+            # Convert results to detections
+            detections = sv.Detections.from_ultralytics(result)
+            tracked_detections = byte_tracker.update_with_detections(detections)
+            return tracked_detections
+    else:
+        result = next(model.predict(
+            source=image,
+            cfg="logic/game.yaml",
+            imgsz=cfg.ai_model_image_size,
+            stream=True,
+            conf=cfg.AI_conf,
+            iou=0.5,
+            device=cfg.AI_device,
+            half=False if "cpu" in cfg.AI_device else True,
+            max_det=20,
+            agnostic_nms=False,
+            augment=False,
+            vid_stride=False,
+            visualize=False,
+            verbose=False,
+            show_boxes=False,
+            show_labels=False,
+            show_conf=False,
+            save=False,
+            show=False))
+
+        return result
+
+def init():
+    run_checks()
+
+    try:
+        model = YOLO(f"models/{cfg.AI_model_name}", task="detect")
+    except Exception as e:
+        print("An error occurred when loading the AI model:\n", e)
+        quit(0)
+
+    while True:
+        image = capture.get_new_frame()
+
+        if image is not None:
+            if cfg.circle_capture:
+                image = capture.convert_to_circle(image)
+
+            if cfg.show_window or cfg.show_overlay:
+                visuals.queue.put(image)
+
+            result = perform_detection(model, image, byte_tracker)
+
+            if hotkeys_watcher.app_pause == 0:
+                frameParser.parse(result)
+
+if __name__ == "__main__":
+    init()
+    +@torch.inference_mode()
++def perform_detection(model, image, tracker=None):
++    if tracker is not None:
++        results = model.predict(
++            source=image,
++            cfg="logic/tracker.yaml",
++            imgsz=cfg.ai_model_image_size,
++            stream=True,
++            conf=cfg.AI_conf,
++            iou=0.5,
++            device=cfg.AI_device,
++            half=False if "cpu" in cfg.AI_device else True,
++            max_det=20,
++            agnostic_nms=False,
++            augment=False,
++            vid_stride=False,
++            visualize=False,
++            verbose=False,
++            show_boxes=False,
++            show_labels=False,
++            show_conf=False,
++            save=False,
++            show=False)
++        
++        for result in results:
++            # Convert results to detections
++            detections = sv.Detections.from_ultralytics(result)
++            tracked_detections = byte_tracker.update_with_detections(detections)
++            return tracked_detections
++    else:
++        result = next(model.predict(
++            source=image,
++            cfg="logic/game.yaml",
++            imgsz=cfg.ai_model_image_size,
++            stream=True,
++            conf=cfg.AI_conf,
++            iou=0.5,
++            device=cfg.AI_device,
++            half=False if "cpu" in cfg.AI_device else True,
++            max_det=20,
++            agnostic_nms=False,
++            augment=False,
++            vid_stride=False,
++            visualize=False,
++            verbose=False,
++            show_boxes=False,
++            show_labels=False,
++            show_conf=False,
++            save=False,
++            show=False))
++        
++        return result
++
++def init():
++    run_checks()
++    
++    try:
++        model = YOLO(f"models/{cfg.AI_model_name}", task="detect")
++    except Exception as e:
++        print("An error occurred when loading the AI model:\n", e)
++        quit(0)
++        
++    while True:
++        image = capture.get_new_frame()
++        
++        if image is not None:
++            if cfg.circle_capture:
++                image = capture.convert_to_circle(image)
++            
++            if cfg.show_window or cfg.show_overlay:
++                visuals.queue.put(image)
++                
++            result = perform_detection(model, image, byte_tracker)
++
++            if hotkeys_watcher.app_pause == 0:
++                frameParser.parse(result)
++
++if __name__ == "__main__":
++    init()
++    
++    from ultralytics import YOLO
++import torch
++
++from logic.config_watcher import cfg
++from logic.capture import capture
++from logic.visual import visuals
++from logic.frame_parser import frameParser
++from logic.hotkeys_watcher import hotkeys_watcher
++from logic.checks import run_checks
++import supervision as sv
++    
++byte_tracker = sv.ByteTrack()