Add application file

app.py

@@ -0,0 +1,101 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Nov 13 18:37:31 2024
+
+@author: sabar
+"""
+
+import gradio as gr
+import cv2
+import numpy as np
+import os
+import json
+from openvino.runtime import Core  # Assuming you're using OpenVINO
+from tqdm import tqdm
+from tf_post_processing import non_max_suppression #,optimized_object_detection
+
+# Load the OpenVINO model
+classification_model_xml = "./model/best_openvino_model\best.xml"
+core = Core()
+config = {
+    "INFERENCE_NUM_THREADS": 2,
+    "ENABLE_CPU_PINNING": True
+}
+model = core.read_model(model=classification_model_xml)
+compiled_model = core.compile_model(model=model, device_name="CPU", config=config)
+
+# Function to perform inference
+def predict_image(image):
+    # Resize, preprocess, and reshape the input image
+    img_size = 960
+    resized_image = cv2.resize(image, (img_size, img_size)) / 255.0
+    resized_image = resized_image.transpose(2, 0, 1)
+    reshaped_image = np.expand_dims(resized_image, axis=0).astype(np.float32)
+    
+    im_height, im_width, _ = image.shape
+    output_numpy = compiled_model(reshaped_image)[0]
+    results = non_max_suppression(output_numpy, conf_thres=0.2, iou_thres=0.6, max_wh=15000)[0]
+
+    # Prepare output paths
+    output_path = "D:/Research/Auto_CAD_detection/output_file_train/"
+    output_image_folder = os.path.join(output_path, "images_alienware_openvino/")
+    os.makedirs(output_image_folder, exist_ok=True)
+
+    output_json_folder = os.path.join(output_path, "json_output/")
+    os.makedirs(output_json_folder, exist_ok=True)
+    
+    predictions = []
+
+    # Draw boxes and collect prediction data
+    for result in results:
+        boxes = result[:4]
+        prob = result[4]
+        classes = int(result[5])
+
+        x1, y1, x2, y2 = np.uint16([
+            boxes[0] * im_width,
+            boxes[1] * im_height,
+            boxes[2] * im_width,
+            boxes[3] * im_height
+        ])
+
+        if prob > 0.2:
+            cv2.rectangle(image, (x1, y1), (x2, y2), (255, 255, 0), 2)
+            label_text = f"{classes} {round(prob, 2)}"
+            cv2.putText(image, label_text, (x1, y1), 0, 0.5, (0, 255, 0), 2)
+
+            # Store prediction info in a JSON-compatible format
+            predictions.append({
+                "class": classes,
+                "probability": round(float(prob), 2),
+                "coordinates": {
+                    "xmin": int(x1),
+                    "ymin": int(y1),
+                    "xmax": int(x2),
+                    "ymax": int(y2)
+                }
+            })
+
+    # Save the processed image and JSON file
+    output_image_path = os.path.join(output_image_folder, "result_image.jpg")
+    cv2.imwrite(output_image_path, image)
+
+    output_json_path = os.path.join(output_json_folder, "predictions.json")
+    with open(output_json_path, 'w') as f:
+        json.dump(predictions, f, indent=4)
+
+    return output_image_path, predictions
+
+# Set up Gradio interface
+def gradio_interface(image):
+    output_image_path, predictions = predict_image(image)
+    return output_image_path, json.dumps(predictions, indent=4)
+
+# Launch the Gradio app
+gr.Interface(
+    fn=gradio_interface,
+    inputs="image",
+    outputs=["image", "json"],
+    title="OpenVINO Model Inference with Gradio",
+    description="Upload an image to get model predictions with bounding boxes and probabilities."
+).launch()