Create edge_functions.py

projects/PiNetAI/cloud_edge/edge_functions.py

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+import os
+import json
+import numpy as np
+from typing import Dict, List
+from tensorflow import keras
+from tensorflow.keras import layers
+from edgeiq import EdgeIQ
+from edgeiq.object_detection import ObjectDetector
+from edgeiq.video import VideoStream
+
+class EdgeFunctions:
+    def __init__(self, model_path: str, device: str = "CPU"):
+        self.model_path = model_path
+        self.device = device
+        self.model = keras.models.load_model(model_path)
+        self.object_detector = ObjectDetector(self.model, device)
+
+    def classify_image(self, image_path: str) -> Dict[str, float]:
+        image = keras.preprocessing.image.load_img(image_path, target_size=(224, 224))
+        input_data = keras.preprocessing.image.img_to_array(image)
+        input_data = np.expand_dims(input_data, axis=0)
+        predictions = self.model.predict(input_data)
+        return {label: confidence for label, confidence in zip(self.model.labels, predictions[0])}
+
+    def detect_objects(self, image_path: str) -> List[Dict[str, float]]:
+        image = keras.preprocessing.image.load_img(image_path, target_size=(224, 224))
+        input_data = keras.preprocessing.image.img_to_array(image)
+        input_data = np.expand_dims(input_data, axis=0)
+        predictions = self.object_detector.detect(input_data)
+        return [{"label": label, "confidence": confidence, "bbox": [x, y, w, h]} for label, confidence, x, y, w, h in zip(self.object_detector.labels, predictions[0], *predictions[1:])]
+
+    def process_video(self, video_path: str) -> None:
+        video_stream = VideoStream(video_path)
+        while True:
+            frame = video_stream.read()
+            if frame is None:
+                break
+            # Pre-process frame
+            frame = cv2.resize(frame, (224, 224))
+            frame = frame / 255.0
+            frame = np.expand_dims(frame, axis=0)
+            # Make predictions
+            predictions = self.object_detector.detect(frame)
+            # Post-process predictions
+            for prediction in predictions:
+                # Draw bounding boxes and labels on the frame
+                cv2.rectangle(frame, (prediction["bbox"][0], prediction["bbox"][1]), (prediction["bbox"][0] + prediction["bbox"][2], prediction["bbox"][1] + prediction["bbox"][3]), (0, 255, 0), 2)
+                cv2.putText(frame, prediction["label"], (prediction["bbox"][0], prediction["bbox"][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+            # Display the frame
+            cv2.imshow("Frame", frame)
+            if cv2.waitKey(1) & 0xFF == ord("q"):
+                break
+        video_stream.release()
+        cv2.destroyAllWindows()
+
+    def optimize_model(self) -> None:
+        # Optimize the model for edge deployment using EdgeIQ
+        edgeiq_model = EdgeIQ.optimize_model(self.model, "edgeiq_config.json", device=self.device)
+        edgeiq_model.save("optimized_model.h5")
+
+    def deploy_model(self) -> None:
+        # Deploy the optimized model to the edge device
+        edgeiq_model = EdgeIQ.deploy_model("optimized_model.h5", self.device)
+        print("Model deployed to edge device")
+
+# Example usage:
+edge_functions = EdgeFunctions("model.h5", device="GPU")
+print(edge_functions.classify_image("image.jpg"))
+print(edge_functions.detect_objects("image.jpg"))
+edge_functions.process_video("video.mp4")
+edge_functions.optimize_model()
+edge_functions.deploy_model()