Create getting_started.ipynb

First version of example

notebook/getting_started.ipynb

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+# Start the timer
+import time
+start_time = time.time()
+
+import numpy as np
+import matplotlib.pyplot as plt
+import requests
+from PIL import Image
+from io import BytesIO
+from lang_sam import LangSAM
+
+model = LangSAM()
+image_url = "https://static01.nyt.com/images/2020/09/08/well/physed-cycle-walk/physed-cycle-walk-videoSixteenByNineJumbo1600-v2.jpg"
+response = requests.get(image_url)
+image_pil = Image.open(BytesIO(response.content)).convert("RGB")
+text_prompt = "bicycle"
+
+masks, boxes, phrases, logits = model.predict(image_pil, text_prompt)
+
+# Convert masks to numpy arrays
+masks_np = [mask.squeeze().cpu().numpy() for mask in masks]
+
+# Visualize the masks
+if len(masks_np) > 1:
+ fig, axes = plt.subplots(1, len(masks_np), figsize=(12, 4))
+ for i, mask in enumerate(masks_np):
+ axes[i].imshow(mask, cmap='gray')
+ axes[i].set_title('Mask {}'.format(i+1))
+ axes[i].axis('off')
+else:
+ fig, ax = plt.subplots(figsize=(6, 4))
+ ax.imshow(masks_np[0], cmap='gray')
+ ax.set_title('Mask')
+ ax.axis('off')
+
+# Save the mask as an image in the current working directory
+mask_path = 'image_mask.png'
+mask_image = Image.fromarray((masks_np[0] * 255).astype(np.uint8))
+mask_image.save(mask_path)
+
+plt.show()
+
+# Print other variables
+print("Boxes:", boxes)
+print("Phrases:", phrases)
+print("Logits:", logits)
+
+# Calculate the elapsed time
+elapsed_time = time.time() - start_time
+print("Elapsed Time:", elapsed_time, "seconds")