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Colony_Analyzer_AI2_HF.py

@@ -170,7 +170,7 @@ def contours_overlap_using_mask(contour1, contour2, image_shape=(1536, 2048)):
     
     return np.any(overlap)
 
-def analyze_colonies(mask, size_cutoff, circ_cutoff):
+def analyze_colonies(mask, size_cutoff, circ_cutoff, img):
     colonies = find_colonies(mask, size_cutoff, circ_cutoff)
     necrosis = find_necrosis(mask)
     
@@ -181,7 +181,9 @@ def analyze_colonies(mask, size_cutoff, circ_cutoff):
         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]
         # Check if any necrosis contour is inside the colony
         necrosis_area = 0
         nec_list =[]
@@ -194,80 +196,30 @@ def analyze_colonies(mask, size_cutoff, circ_cutoff):
 
         data.append({
             "colony_area": colony_area,
-            "necrosis_area": necrosis_area,
+            "necrotic_area": necrosis_area,
             "centroid": centroid,
-            "percent_necrosis": necrosis_area/colony_area,
+            "percent_necrotic": necrosis_area/colony_area,
             "contour": colony,
-            "nec_contours": nec_list
+            "nec_contours": nec_list,
+            'mean_pixel_value':np.mean(pix)
         })
 
     # Convert results to a DataFrame
     df = pd.DataFrame(data)
     df.index = range(1,len(df.index)+1)
     return(df)
-
-
-def contour_overlap(contour1, contour2, centroid1, centroid2, area1, area2, centroid_thresh=25, area_thresh = 85, img_shape = (1536, 2048)):
-    """
-    Determines the overlap between two contours.
-    Returns:
-        0: No overlap
-        1: Overlap but does not meet strict conditions
-        2: Overlap >= 80% of the larger contour and centroids are close
-    """
-    # Create blank images
-    img1 = np.zeros(img_shape, dtype=np.uint8)
-    img2 = np.zeros(img_shape, dtype=np.uint8)
-    
-    # Draw filled contours
-    cv2.drawContours(img1, [contour1], -1, 255, thickness=cv2.FILLED)
-    cv2.drawContours(img2, [contour2], -1, 255, thickness=cv2.FILLED)
-    
-    # Compute overlap
-    intersection = cv2.bitwise_and(img1, img2)
-    intersection_area = np.count_nonzero(intersection)
-    
-    if intersection_area == 0:
-        return 0  # No overlap
-    
-    # Compute centroid distance
-    centroid_distance = float(np.sqrt(abs(centroid1[0]-centroid2[0])**2 + abs(centroid1[1]-centroid2[1])**2))
-    # Check percentage overlap relative to the larger contour
-    overlap_ratio = intersection_area/max(area1, area2)
-    
-    if overlap_ratio >= area_thresh and centroid_distance <= centroid_thresh:
-        if area1 > area2:
-            return(2)
-        else:
-            return(3)
-    else:
-        return 1  # Some overlap but not meeting strict criteria
-    
-def compare_frames(frame1, frame2):
-    for i in range(1, len(frame1)+1):
-        for j in range(1, len(frame2)+1):
-            temp = contour_overlap(frame1.loc[i, "contour"], frame2.loc[j, "contour"], frame1.loc[i, "centroid"], frame2.loc[j, "centroid"], frame1.loc[i, "colony_area"], frame2.loc[j, "colony_area"])
-            if temp ==2:
-                frame2.loc[j,"exclude"] = True
-            elif temp ==3:
-                frame1.loc[j, "exclude"] = True
-                break
-    frame1 = frame1[frame1["exclude"]==False]
-    frame2 = frame2[frame2["exclude"]==False]
-    df = pd.concat([frame1, frame2], axis=0)
-    df.index = range(1,len(df.index)+1)  
-    return(df)
     
 def main(args):
     min_size = args[1]
     min_circ = args[2]
+    do_necrosis = args[3]
     colonies = {}
     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)
-    colonies = analyze_colonies(p, min_size, min_circ)
+    colonies = analyze_colonies(p, min_size, min_circ, np.array(args[0]))
     
     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]) 
@@ -279,7 +231,8 @@ def main(args):
     
     for i in range(len(colonies)): 
         cv2.drawContours(img, [list(colonies["contour"])[i]], -1, (0, 255, 0), 2)
-        cv2.drawContours(img, list(colonies['nec_contours'])[i], -1, (0, 0, 255), 2)
+        if do_necrosis == True:
+            cv2.drawContours(img, list(colonies['nec_contours'])[i], -1, (0, 0, 255), 2)
         coords = list(list(colonies["centroid"])[i])
         if coords[0] > 1950:
             #if a colony is too close to the right edge, makes the label move to left
@@ -293,15 +246,27 @@ def main(args):
     total_area_dark = sum(colonies['necrosis_area'])
     total_area_light = sum(colonies['colony_area'])
     ratio = total_area_dark/(abs(total_area_light)+1)
-
-    colonies.loc[len(colonies)+1] = ["Total", total_area_light, total_area_dark, None, ratio]
+    radii = [np.sqrt(x/3.1415) for x in list(colonies['colony_area'])]
+    volumes = [4.189*(x**3) for x in radii]
+    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'])]
+    del meanpix
+    colonies = colonies[["Colony Number", 'Colony volume', "colony_area",'mean_pixel_value', "centroid", "necrotic_area","percent_necrotic"]]
+    if do_necrosis == False:
+        colonies = colonies.drop('necrotic_area', axis=1)
+        colonies = colonies.drop('percent_necrotic', axis=1)
     Parameters = pd.DataFrame({"Minimum colony size in pixels":[min_size], "Minimum colony circularity":[min_circ]})
     with pd.ExcelWriter('results.xlsx') as writer:
         colonies.to_excel(writer, sheet_name="Colony data", index=False)
         Parameters.to_excel(writer, sheet_name="Parameters", index=False)
     caption = np.ones((150, 2068, 3), dtype=np.uint8) * 255  # Multiply by 255 to make it white
-    cv2.putText(caption, "Total area necrotic: "+str(total_area_dark)+ ", Total area living: "+str(total_area_light)+", Ratio: "+str(ratio), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 3)
-
+    if do_necrosis == True:
+        cv2.putText(caption, "Total area necrotic: "+str(total_area_dark)+ ", Total area living: "+str(total_area_light)+", Ratio: "+str(ratio), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 3)
+    else:
+        cv2.putText(caption, "Total area: "+str(total_area_light), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 3)
+    cv2.putText(caption, "Total number of colonies: "+str(len(colonies)-1), (40, 110), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 3)
 
 
     cv2.imwrite('results.png', np.vstack((img, caption)))

Colony_Analyzer_AI_zstack2_HF.py

@@ -177,9 +177,9 @@ def analyze_colonies(mask, size_cutoff, circ_cutoff):
 
         data.append({
             "colony_area": colony_area,
-            "necrosis_area": necrosis_area,
+            "necrotic_area": necrosis_area,
             "centroid": centroid,
-            "percent_necrosis": necrosis_area/colony_area,
+            "percent_necrotic": necrosis_area/colony_area,
             "contour": colony,
             "nec_contours": nec_list
         })
@@ -247,6 +247,7 @@ def compare_frames(frame1, frame2):
 def main(args):
     min_size = args[1]
     min_circ = args[2]
+    do_necrosis = args[3]
     colonies = {}
     files = args[0]
     for idx,x in enumerate(files):
@@ -307,7 +308,8 @@ def main(args):
         img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
     for i in range(len(colonies)): 
         cv2.drawContours(img, [list(colonies["contour"])[i]], -1, (0, 255, 0), 2)
-        cv2.drawContours(img, list(colonies['nec_contours'])[i], -1, (0, 0, 255), 2)
+        if do_necrosis == True:
+            cv2.drawContours(img, list(colonies['nec_contours'])[i], -1, (0, 0, 255), 2)
         coords = list(list(colonies["centroid"])[i])
         if coords[0] > 1950:
             #if a colony is too close to the right edge, makes the label move to left
@@ -350,15 +352,27 @@ def main(args):
     total_area_dark = sum(colonies['necrosis_area'])
     total_area_light = sum(colonies['colony_area'])
     ratio = total_area_dark/(abs(total_area_light)+1)
-
-    colonies.loc[len(colonies)+1] = ["Total", total_area_light, total_area_dark, None, ratio, None]
+    radii = [np.sqrt(x/3.1415) for x in list(colonies['colony_area'])]
+    volumes = [4.189*(x**3) for x in radii]
+    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'])]
+    del meanpix
+    colonies = colonies[["Colony Number", 'Colony volume', "colony_area",'mean_pixel_value', "centroid", "necrotic_area","percent_necrotic"]]
     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)
+        colonies = colonies.drop('percent_necrotic', axis=1)
     with pd.ExcelWriter("Group_analysis_results.xlsx") as writer:
         colonies.to_excel(writer, sheet_name="Colony data", index=False)
         Parameters.to_excel(writer, sheet_name="Parameters", index=False)
     caption = np.ones((150, 2068, 3), dtype=np.uint8) * 255  # Multiply by 255 to make it white
-    cv2.putText(caption, "Total area necrotic: "+str(total_area_dark)+ ", Total area living: "+str(total_area_light)+", Ratio: "+str(ratio), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 3)
-
+    if do_necrosis == True:
+        cv2.putText(caption, "Total area necrotic: "+str(total_area_dark)+ ", Total area living: "+str(total_area_light)+", Ratio: "+str(ratio), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 3)
+    else:
+        cv2.putText(caption, "Total area: "+str(total_area_light), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 3)
+    cv2.putText(caption, "Total number of colonies: "+str(len(colonies)-1), (40, 110), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 3)    
     print('img ndim: ' +str(img.ndim))
     print('caption ndim: ' +str(caption.ndim))
 

app.py

@@ -2,25 +2,26 @@ import gradio as gr
 from PIL import Image
 
 # Single image analysis function (your existing logic)
-def analyze_image(image, min_size, circularity):
+def analyze_image(image, min_size, circularity, do_necrosis=True):
     import Colony_Analyzer_AI2_HF as analyzer
-    processed_img, picname, excelname = analyzer.main([image, min_size, circularity])
+    processed_img, picname, excelname = analyzer.main([image, min_size, circularity,do_necrosis])
     return Image.fromarray(processed_img), picname, excelname
 
 # Z-stack analysis function (adapt with your own logic)
-def analyze_zstack(images, min_size, circularity):
+def analyze_zstack(images, min_size, circularity, do_necrosis=True):
     # images: list of PIL images
     # Plug in your own z-stack segmentation logic here
     # Example stub: pass images as a list to your analyzer
     import Colony_Analyzer_AI_zstack2_HF as analyzer
     images = [Image.open(f.name) for f in images]
-    processed_img, picname, excelname = analyzer.main([images, min_size, circularity])
+    processed_img, picname, excelname = analyzer.main([images, min_size, circularity,do_necrosis])
     return Image.fromarray(processed_img), picname, excelname
 
 with gr.Blocks() as demo:
     gr.Markdown("# AI Colony Analyzer\nUpload an image (or Z-Stack) to run colony analysis.")
 
     z_stack_checkbox = gr.Checkbox(label="Enable Z-Stack", value=False)
+    do_necrosis_checkbox = gr.Checkbox(label="Enable necrosis detection", value=False)
     image_input_single = gr.Image(type="pil", label="Upload Image", visible=True)
     image_input_multi = gr.File(file_count="multiple", type="filepath", label="Upload Z-Stack Images", visible=False)
     min_size_input = gr.Number(label="Minimum Colony Size (pixels)", value=1000)
@@ -42,16 +43,16 @@ with gr.Blocks() as demo:
         outputs=[image_input_single, image_input_multi]
     )
 
-    def conditional_analyze(z_stack, single_image, multi_images, min_size, circularity):
+    def conditional_analyze(z_stack, single_image, multi_images, min_size, circularity, do_necrosis=True):
         if z_stack:
-            return analyze_zstack(multi_images, min_size, circularity)
+            return analyze_zstack(multi_images, min_size, circularity,do_necrosis)
         else:
-            return analyze_image(single_image, min_size, circularity)
+            return analyze_image(single_image, min_size, circularity,do_necrosis)
 
-    process_btn.click(
-        conditional_analyze,
-        inputs=[z_stack_checkbox, image_input_single, image_input_multi, min_size_input, circularity_input],
-        outputs=[output_image, output_file_img, output_file_excel]
-    )
+process_btn.click(
+    conditional_analyze,
+    inputs=[z_stack_checkbox, image_input_single, image_input_multi, min_size_input, circularity_input, do_necrosis_checkbox],
+    outputs=[output_image, output_file_img, output_file_excel]
+)
 
 demo.launch()