Update New_file.txt

New_file.txt

@@ -1,63 +1,56 @@
-import cv2
-import numpy as np
-
-# Load the images
-image_paths = [
-    'path_to_your_first_image.jpg',
-    'path_to_your_second_image.jpg',
-    'path_to_your_third_image.jpg',
-    'path_to_your_fourth_image.jpg',
-    'path_to_your_fifth_image.jpg'
-]
-images = [cv2.imread(image_path) for image_path in image_paths]
-
-# Define the desired height for all images (e.g., maximum height among the images)
-desired_height = max(image.shape[0] for image in images)
-
-# Function to resize an image while maintaining aspect ratio
-def resize_with_aspect_ratio(image, height):
-    aspect_ratio = image.shape[1] / image.shape[0]
-    new_width = int(height * aspect_ratio)
-    resized_image = cv2.resize(image, (new_width, height))
-    return resized_image
-
-# Resize images
-resized_images = [resize_with_aspect_ratio(image, desired_height) for image in images]
-
-# Add white padding to make the images the same width
-max_width = max(image.shape[1] for image in resized_images)
-padded_images = [cv2.copyMakeBorder(
-    image, 
-    0, 0, 0, max_width - image.shape[1], 
-    cv2.BORDER_CONSTANT, 
-    value=[255, 255, 255]
-) for image in resized_images]
-
-# Combine images side by side
-combined_image = np.hstack(padded_images)
-
-# Add labels to the top of each image
-labels = ['Image 1', 'Image 2', 'Image 3', 'Image 4', 'Image 5']
-font = cv2.FONT_HERSHEY_SIMPLEX
-font_scale = 1
-color = (0, 0, 0)  # Black color for the text
-thickness = 2
-
-# Add labels to the combined image
-x_offset = 0
-for i, label in enumerate(labels):
-    label_size = cv2.getTextSize(label, font, font_scale, thickness)[0]
-    x = x_offset + 10
-    y = label_size[1] + 10
-    cv2.putText(combined_image, label, (x, y), font, font_scale, color, thickness)
-    x_offset += max_width
-
-# Display the combined image
-cv2.imshow('Combined Image', combined_image)
-cv2.waitKey(0)
-cv2.destroyAllWindows()
+# Initialize the configuration
+cfg = get_cfg()
+
+# Load the config file from the model zoo
+cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"))
+
+# Set the pre-trained model weights
+cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml")
+
+# Set the confidence threshold for predictions
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # You can adjust this threshold
+
+# Specify the device to run on (GPU if available, else CPU)
+cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Create the predictor
+predictor = DefaultPredictor(cfg)
+
+# Path to your image
+image_path = "path_to_your_image.jpg"
+
+# Read the image using OpenCV
+image = cv2.imread(image_path)
+
+# Check if the image was loaded successfully
+if image is None:
+    raise ValueError(f"Image not found at {image_path}")
 
-# Save the combined image
-cv2.imwrite('combined_image.jpg', combined_image)
 
-# https://drive.google.com/drive/folders/17XqTtGL-oBl8WbLVXsalfF6ry0UCDE2h?usp=sharing
+# Perform inference
+outputs = predictor(image)
+
+
+# Convert the image from BGR to RGB for visualization
+image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+# Create a Visualizer instance
+v = Visualizer(image_rgb, metadata=model_zoo.get_cfg().MODEL.META_ARCHITECTURE, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
+
+# Draw the predictions on the image
+out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+
+# Convert back to BGR for OpenCV compatibility (if needed)
+output_image = out.get_image()[:, :, ::-1]
+
+# Display the image using OpenCV
+cv2.imshow("Object Detection", output_image)
+cv2.waitKey(0)  # Press any key to close the window
+cv2.destroyAllWindows()
+
+# Alternatively, display using matplotlib
+plt.figure(figsize=(12, 8))
+plt.imshow(out.get_image())
+plt.axis('off')
+plt.title("Object Detection Results")
+plt.show()