Upload 3 files

Colony_Analyzer_AI2_HF.py

@@ -66,7 +66,7 @@ from transformers import SegformerForSemanticSegmentation
 # Load fine-tuned model
 #ReyaLabColumbia/Segformer_Colony_Counter
 #ReyaLabColumbia/OrganoidCounter
-model = SegformerForSemanticSegmentation.from_pretrained("ReyaLabColumbia/Segformer_Colony_Counter")
+model = SegformerForSemanticSegmentation.from_pretrained("ReyaLabColumbia/Segformer_Colony_Counter_GP")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 model.eval()  # Set to evaluation mode

Colony_Analyzer_AI_zstack2_HF.py

@@ -63,7 +63,7 @@ from transformers import SegformerForSemanticSegmentation
 # Load fine-tuned model
 #ReyaLabColumbia/Segformer_Colony_Counter
 #ReyaLabColumbia/OrganoidCounter
-model = SegformerForSemanticSegmentation.from_pretrained("ReyaLabColumbia/Segformer_Colony_Counter")  # Adjust path
+model = SegformerForSemanticSegmentation.from_pretrained("ReyaLabColumbia/Segformer_Colony_Counter_GP")  # Adjust path
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 model.eval()  # Set to evaluation mode
@@ -155,16 +155,16 @@ def contours_overlap_using_mask(contour1, contour2, image_shape=(1536, 2048)):
     return np.any(overlap)
 
 def analyze_colonies(mask, size_cutoff, circ_cutoff, img):
-    colonies = find_colonies(mask, size_cutoff, circ_cutoff)
+    colonies,areas = find_colonies(mask, size_cutoff, circ_cutoff)
     necrosis = find_necrosis(mask)
     
     data = []
     
-    for colony in colonies:
-        colony_area = cv2.contourArea(colony)
+    for x in range(len(colonies)):
+        colony = colonies[x]
+        colony_area = areas[x]
         centroid = compute_centroid(colony)
-        if colony_area <= 50:
-            continue
+        
         mask = np.zeros(img.shape, np.uint8)
         cv2.drawContours(mask, [colony], -1, 255, cv2.FILLED)
         pix = img[mask == 255]
@@ -369,9 +369,9 @@ def main(args):
     colonies['Colony volume'] = volumes
     del radii, volumes
     meanpix = sum(colonies['mean_pixel_value'] * colonies['colony_area'])/total_area_light
-    colonies.loc[len(colonies)+1] = ["Total", total_area_light, total_area_dark, None, ratio, meanpix, sum(colonies['Colony volume'])]
+    colonies.loc[len(colonies)+1] = ["Total", total_area_light, total_area_dark, None, ratio, None, meanpix, sum(colonies['Colony volume'])]
     del meanpix
-    colonies = colonies[["Colony Number", 'Colony volume', "colony_area",'mean_pixel_value', "centroid", "necrotic_area","percent_necrotic"]]
+    colonies = colonies[["Colony Number", 'Colony volume', "colony_area",'mean_pixel_value', "centroid", "necrotic_area","percent_necrotic", "source"]]
     Parameters = pd.DataFrame({"Minimum colony size in pixels":[min_size], "Minimum colony circularity":[min_circ]})
     if do_necrosis == False:
         colonies = colonies.drop('necrotic_area', axis=1)