process test image

run.py

@@ -4,6 +4,19 @@ import numpy as np
 import matplotlib.pyplot as plt
 import app
 import os
+from patchify import patchify
+from PIL import Image
+
+
+def patchify(large_image):
+    all_img_patches = []
+    patches_img = patchify(large_image, (patch_size, patch_size), step=step)  #Step=256 for 256 patches means no overlap
+    for i in range(patches_img.shape[0]):
+        for j in range(patches_img.shape[1]):
+            single_patch_img = patches_img[i,j,:,:]
+            all_img_patches.append(single_patch_img)
+    images = np.array(all_img_patches)
+
 
 def pred(src):
     # os.environ['HUGGINGFACE_HUB_HOME'] = './.cache'
@@ -18,7 +31,37 @@ def pred(src):
     my_sam_model.load_state_dict(torch.load("sam_model.pth", map_location=torch.device('cpu')))
 
     new_image = np.array(Image.open(src))
-    inputs = processor(new_image, return_tensors="pt")
+    patches = patchify(new_image, (256, 256), step=256)
+
+    # Define the size of your array
+    array_size = 256
+
+    # Define the size of your grid
+    grid_size = 10
+
+    # Generate the grid points
+    x = np.linspace(0, array_size-1, grid_size)
+    y = np.linspace(0, array_size-1, grid_size)
+
+    # Generate a grid of coordinates
+    xv, yv = np.meshgrid(x, y)
+
+    # Convert the numpy arrays to lists
+    xv_list = xv.tolist()
+    yv_list = yv.tolist()
+
+    # Combine the x and y coordinates into a list of list of lists
+    input_points = [[[int(x), int(y)] for x, y in zip(x_row, y_row)] for x_row, y_row in zip(xv_list, yv_list)]
+    input_points = torch.tensor(input_points).view(1, 1, grid_size*grid_size, 2)
+
+    i, j = 1, 2
+
+    # Selectelected patch for segmentation
+    random_array = patches[i, j]
+
+    single_patch = Image.fromarray(random_array)
+    inputs = processor(single_patch, input_points=input_points, return_tensors="pt")
+    
     inputs = {k: v.to(device) for k, v in inputs.items()}
     x = 1
     # my_sam_model.eval()