Delete SAM.py

SAM.py

@@ -1,205 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# # Utility functions
-
-# In[ ]:
-
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-def show_mask(mask, ax, random_color=False):
-    if random_color:
-        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
-    else:
-        color = np.array([30/255, 144/255, 255/255, 0.6])
-    h, w = mask.shape[-2:]
-    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
-    ax.imshow(mask_image)
-
-
-def show_box(box, ax):
-    x0, y0 = box[0], box[1]
-    w, h = box[2] - box[0], box[3] - box[1]
-    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))
-
-def show_boxes_on_image(raw_image, boxes):
-    plt.figure(figsize=(10,10))
-    plt.imshow(raw_image)
-    for box in boxes:
-      show_box(box, plt.gca())
-    plt.axis('on')
-    plt.show()
-
-def show_points_on_image(raw_image, input_points, input_labels=None):
-    plt.figure(figsize=(10,10))
-    plt.imshow(raw_image)
-    input_points = np.array(input_points)
-    if input_labels is None:
-      labels = np.ones_like(input_points[:, 0])
-    else:
-      labels = np.array(input_labels)
-    show_points(input_points, labels, plt.gca())
-    plt.axis('on')
-    plt.show()
-
-def show_points_and_boxes_on_image(raw_image, boxes, input_points, input_labels=None):
-    plt.figure(figsize=(10,10))
-    plt.imshow(raw_image)
-    input_points = np.array(input_points)
-    if input_labels is None:
-      labels = np.ones_like(input_points[:, 0])
-    else:
-      labels = np.array(input_labels)
-    show_points(input_points, labels, plt.gca())
-    for box in boxes:
-      show_box(box, plt.gca())
-    plt.axis('on')
-    plt.show()
-
-
-def show_points_and_boxes_on_image(raw_image, boxes, input_points, input_labels=None):
-    plt.figure(figsize=(10,10))
-    plt.imshow(raw_image)
-    input_points = np.array(input_points)
-    if input_labels is None:
-      labels = np.ones_like(input_points[:, 0])
-    else:
-      labels = np.array(input_labels)
-    show_points(input_points, labels, plt.gca())
-    for box in boxes:
-      show_box(box, plt.gca())
-    plt.axis('on')
-    plt.show()
-
-
-def show_points(coords, labels, ax, marker_size=375):
-    pos_points = coords[labels==1]
-    neg_points = coords[labels==0]
-    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
-    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
-
-
-def show_masks_on_image(raw_image, masks, scores):
-    if len(masks.shape) == 4:
-      masks = masks.squeeze()
-    if scores.shape[0] == 1:
-      scores = scores.squeeze()
-
-    nb_predictions = scores.shape[-1]
-    fig, axes = plt.subplots(1, nb_predictions, figsize=(15, 15))
-
-    for i, (mask, score) in enumerate(zip(masks, scores)):
-      mask = mask.cpu().detach()
-      axes[i].imshow(np.array(raw_image))
-      show_mask(mask, axes[i])
-      axes[i].title.set_text(f"Mask {i+1}, Score: {score.item():.3f}")
-      axes[i].axis("off")
-    plt.show()
-
-
-# # Model loading
-
-# In[ ]:
-
-
-import torch
-from transformers import SamModel, SamProcessor
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
-processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
-
-
-# In[ ]:
-
-
-from PIL import Image
-import requests
-
-img_url = "thuya.jpeg"
-raw_image = Image.open(img_url)
-
-plt.imshow(raw_image)
-
-
-# ## Step 1: Retrieve the image embeddings
-
-# In[ ]:
-
-
-inputs = processor(raw_image, return_tensors="pt").to(device)
-image_embeddings = model.get_image_embeddings(inputs["pixel_values"])
-
-
-# In[ ]:
-
-
-input_points = [[[200, 300]]]
-show_points_on_image(raw_image, input_points[0])
-
-
-# In[ ]:
-
-
-inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
-# pop the pixel_values as they are not neded
-inputs.pop("pixel_values", None)
-inputs.update({"image_embeddings": image_embeddings})
-
-with torch.no_grad():
-    outputs = model(**inputs)
-
-masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu())
-scores = outputs.iou_scores
-
-
-# In[ ]:
-
-
-show_masks_on_image(raw_image, masks[0], scores)
-
-
-# ## Export the masked images
-
-# In[92]:
-
-
-import cv2
-
-if len(masks[0].shape) == 4:
-      masks[0] = masks[0].squeeze()
-if scores.shape[0] == 1:
-      scores = scores.squeeze()
-
-nb_predictions = scores.shape[-1]
-fig, axes = plt.subplots(1, nb_predictions, figsize=(15, 15))
-for i, (mask, score) in enumerate(zip(masks[0], scores)):
-    mask = mask.cpu().detach()
-    axes[i].imshow(np.array(raw_image))
-    # show_mask(mask, axes[i])
-
-    mask_image = (mask.numpy() * 255).astype(np.uint8)  # Convert to uint8 format
-    cv2.imwrite('mask.png', mask_image)
-
-    image = cv2.imread('thuya.jpeg')
-
-    color_mask = np.zeros_like(image)
-    color_mask[mask > 0.5] = [30, 144, 255] # Choose any color you like
-    masked_image = cv2.addWeighted(image, 0.6, color_mask, 0.4, 0)
-
-    color = np.array([30/255, 144/255, 255/255])
-    #mask_image =  * color.reshape(1, 1, -1)
-
-    new_image = -image* np.tile(mask_image[...,None], 3)
-
-    cv2.imwrite('masked_image2.png', cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR))
-
-
-
-# In[85]:
-
-
-.shape
-