Upload Colony_Analyzer_AI2_HF.py

Colony_Analyzer_AI2_HF.py

@@ -8,96 +8,37 @@ Created on Thu Mar 20 14:23:27 2025
 
 import os
 import cv2
-from PIL import Image
-
-
-def pad(img_np, tw=2048, th=1536):
-    """
-    Pads a numpy image (grayscale or RGB) to 2048x1536 (width x height) with white pixels.
-    Pads at the bottom and right as needed.
-    """
-    height, width = img_np.shape[:2]
-    pad_bottom = max(0, th - height)
-    pad_right = max(0, tw - width)
-    # Padding: (top, bottom, left, right)
-    if img_np.ndim == 3:
-        # Color image (H, W, 3)
-        border_value = [255, 255, 255]
-    else:
-        # Grayscale image (H, W)
-        border_value = 255
-
-    padded = cv2.copyMakeBorder(
-        img_np, 
-        top=0, bottom=pad_bottom,
-        left=0, right=pad_right,
-        borderType=cv2.BORDER_CONSTANT,
-        value=border_value
-    )
-    return padded
-
 
 #this is the huggingface version
-import numpy as np
-from PIL import Image
-
-def cut_img(img, patch_size=512):
-    """
-    Cuts an image (PIL or numpy array) into non-overlapping patch_size x patch_size tiles.
-    Returns img_map, number of rows, number of cols.
-    """
-    # If it's a PIL image, convert to numpy array
-    if isinstance(img, Image.Image):
-        img_np = np.array(img)
-        height, width = img_np.shape[:2]
-    else:
-        img_np = img
-        height, width = img_np.shape[:2]
-
-    i_num = height // patch_size
-    j_num = width // patch_size
+def cut_img(img):
     img_map = {}
+    width, height = img.size
+    i_num = height // 512
+    j_num = width // 512
     count = 1
     for i in range(i_num):
         for j in range(j_num):
-            cropped_img = img_np[
-                patch_size * i: patch_size * (i + 1),
-                patch_size * j: patch_size * (j + 1),
-                ...
-            ]
+            cropped_img = img.crop((512*j, 512*i, 512*(j+1), 512*(i+1)))
             img_map[count] = cropped_img
+            #print(type(cropped_img))
             count += 1
-    return img_map, i_num, j_num
+    return img_map
     
 import numpy as np
 
-import numpy as np
+def stitch(img_map):
+    rows = [
+        np.hstack([img_map[1], img_map[2], img_map[3], img_map[4]]),  # First row (images 0 to 3)
+        np.hstack([img_map[5], img_map[6], img_map[7], img_map[8]]),  # Second row (images 4 to 7)
+        np.hstack([img_map[9], img_map[10], img_map[11], img_map[12]])  # Third row (images 8 to 11)
+    ]
+    # Stack rows vertically
+    return(np.vstack(rows))
+
+
+
 from PIL import Image
 
-def stitch(img_map, i_num, j_num, min_width=2048, min_height=1536):
-    tiles = []
-    count = 1
-    for i in range(i_num):
-        row_tiles = []
-        for j in range(j_num):
-            tile = np.array(img_map[count])
-            row_tiles.append(tile)
-            count += 1
-        row_img = np.hstack(row_tiles)
-        tiles.append(row_img)
-    stitched = np.vstack(tiles)
-
-    # Pad the stitched image if it's less than min_width/min_height
-    h, w = stitched.shape[:2]
-    pad_h = max(0, min_height - h)
-    pad_w = max(0, min_width - w)
-    if pad_h > 0 or pad_w > 0:
-        # Pad as (top, bottom), (left, right), (channels)
-        if stitched.ndim == 3:
-            stitched = np.pad(stitched, ((0, pad_h), (0, pad_w), (0, 0)), 'constant')
-        else:
-            stitched = np.pad(stitched, ((0, pad_h), (0, pad_w)), 'constant')
-    return stitched
 
 
 import matplotlib.pyplot as plt
@@ -273,24 +214,21 @@ def main(args):
     min_circ = args[2]
     do_necrosis = args[3]
     colonies = {}
-    img_map, i_num, j_num = cut_img(pad(np.array(args[0]),512,512))
+    img_map = cut_img(args[0])
     for z in img_map:
         img_map[z] = eval_img(img_map[z])
     del z
-    p = stitch(img_map, i_num, j_num)
+    p = stitch(img_map)
     colonies = analyze_colonies(p, min_size, min_circ, np.array(args[0]))
     if len(colonies) <=0:
-    	img = pad(np.array(args[0]))
     	caption = np.ones((150, 2048, 3), dtype=np.uint8) * 255  # Multiply by 255 to make it white
     	cv2.putText(caption, 'No colonies detected.', (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 3)
-    	cv2.imwrite('results.png', np.vstack((img, caption)))
+    	cv2.imwrite('results.png', np.vstack((np.array(args[0]), caption)))
     	colonies = pd.DataFrame({"Colony Number":[], 'Colony volume':[], "colony_area":[],'mean_pixel_value':[], "centroid":[], "necrotic_area":[],"percent_necrotic":[]})
     	with pd.ExcelWriter('results.xlsx') as writer:
         	colonies.to_excel(writer, sheet_name="Colony data", index=False)
-    	return(np.vstack((img, caption)), 'results.png', 'results.xlsx')
-
-    img =pad(np.array(args[0]))
-
+    	return(np.vstack((args[0], caption)), 'results.png', 'results.xlsx')
+    img = np.array(args[0])
     img = cv2.copyMakeBorder(img,top=0, bottom=10,left=0,right=10, borderType=cv2.BORDER_CONSTANT,  value=[255, 255, 255]) 
     #print(colonies.to_string())