from pyscript_bootstrap_templates import bootstrap_templates from pyscript_bootstrap_templates import bootstrap_HTML as bHTML from pyscript_bootstrap_templates import bootstrap_inputs as bInputs from pyscript_bootstrap_templates import HTML as HTML import numpy as np from PIL import Image import cv2 loaded_img = None app = bootstrap_templates.PyScriptBootstrapDashboard(parent_element="pyscript_app", brand_name="Pyscript Cell Detector") main_div = bHTML.BootstrapContainer(parent=app.main) main_div.w = 100 result_div = bHTML.BootstrapContainer(parent=main_div) def process_image(image, hough_min_dist = 500, hough_param1 = 80, hough_param2 = 500, minRadius = 100, maxRadius=500, inner_hough_circles=True): for child in result_div.children: child.destroy() #image = cv2.imread(str(test_img_path), cv2.IMREAD_COLOR) # Convert the image to grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Use median blur to reduce noise gray = cv2.medianBlur(gray, 5) # Apply Hough transform on the image to find circles circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 2, minDist=hough_min_dist, param1=hough_param1, param2=hough_param2, minRadius=minRadius, maxRadius=maxRadius) if circles is None: bHTML.AlertDanger("No cell areas detected in image", parent=app.main) return False # Convert to integers circles = np.uint16(np.around(circles)) # Loop over all detected circles and add them to the image #for i in circles[0,:]: # # Draw outer circle # cv2.circle(image,(i[0],i[1]),i[2],(0,255,0),2) # # Draw center of circle # cv2.circle(image,(i[0],i[1]),2,(0,0,255),3) first_wells = [] first_wells_gray = [] for i in circles[0,:]: center = (i[0], i[1]) radius = i[2] # Cut out the well well = image[center[1]-radius:center[1]+radius, center[0]-radius:center[0]+radius] well_gray = gray[center[1]-radius:center[1]+radius, center[0]-radius:center[0]+radius] first_wells.append(well) first_wells_gray.append(well_gray) if inner_hough_circles: second_wells = [] second_wells_gray = [] for well, well_gray in zip(first_wells, first_wells_gray): circles = cv2.HoughCircles(well_gray, cv2.HOUGH_GRADIENT, 1, minDist=hough_min_dist, param1=50, param2=50, minRadius=(well.shape[0] // 2) - 50, maxRadius=(well.shape[0] // 2) - 10) if circles is not None: circles = np.uint16(np.around(circles)) i = circles[0,:][0] center = (i[0], i[1]) radius = i[2] - 1 min_y = max(center[1]-radius, 0) max_y = center[1]+radius min_x = max(center[0]-radius, 0) max_x = center[0]+radius second_wells.append(well[min_y:max_y, min_x:max_x]) second_wells_gray.append(well_gray[min_y:max_y, min_x:max_x]) else: second_wells.append(well) second_wells_gray.append(well_gray) else: second_wells = first_wells second_wells_gray = first_wells_gray for well, well_gray in zip(second_wells, second_wells_gray): _, binary = cv2.threshold(255 - well_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # Perform some morphological operations to remove small noise - you can change the kernel size kernel = np.ones((3,3),np.uint8) cleaned = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel) radius = well.shape[0] // 2 -1 # Now clear all pixels outside the circular well. # We do this by creating a mask for the well and applying it to the image. well_mask = np.zeros_like(cleaned, dtype=np.uint8) cv2.circle(well_mask, (radius, radius), radius, 1, thickness=-1) cleaned = cleaned * well_mask circle = well_mask > 0 # now create an image overlay to display ratio = np.sum((cleaned > 0).astype(int)) / np.sum(circle.astype(int)) div_result = bHTML.BootstrapContainer(f"ratio: {ratio * 100}%", parent=result_div) div_result.shadow = bHTML.Shadow.LARGE div_result.rounded = True div_result.w = 50 well[cleaned > 0,0] = 255 final_img = bHTML.Image.from_numpy_array(well, parent=div_result) final_img.rounded = True final_img.rounded_size = 10 final_img.shadow = bHTML.Shadow.LARGE final_img.w = 100 div = bHTML.BootstrapContainer("Controls", parent=app.sidebar) div.font_size = 4 image_input = bInputs.InputFile(label_text="choose image file", parent=app.sidebar) i_hough_min_dist = bInputs.InputInt("hough min distance [px]", parent=app.sidebar) i_hough_min_dist.value = 500 i_hough_param1 = bInputs.InputInt("hough param1", parent=app.sidebar) i_hough_param1.value = 80 i_hough_param2 = bInputs.InputInt("hough param2", parent=app.sidebar) i_hough_param2.value = 500 i_hough_min_radius = bInputs.InputInt("hough min radius [px]", parent=app.sidebar) i_hough_min_radius.value = 100 i_hough_max_radius = bInputs.InputInt("hough max radius [px]", parent=app.sidebar) i_hough_max_radius.value = 500 btn = bHTML.ButtonPrimary("Process", parent=app.sidebar) btn.w = 100 def on_image_change(f, *args): global loaded_img f.seek(0) img = np.array(Image.open(f)) for child in result_div.children: child.destroy() output = bHTML.Image.from_numpy_array(img, parent=result_div) output.rounded = True output.rounded_size = 10 output.shadow = bHTML.Shadow.LARGE output.w=50 loaded_img = img image_input.onchange = on_image_change def on_click(*args, **kwargs): if loaded_img is None: bHTML.AlertDanger("No image loaded", parent=app.main) return h_min_dist = int(i_hough_min_dist.value) h_param1 = int(i_hough_param1.value) h_param2 = int(i_hough_param2.value) h_min_radius = int(i_hough_min_radius.value) h_max_radius = int(i_hough_max_radius.value) process_image(loaded_img, hough_min_dist=h_min_dist, hough_param1=h_param1, hough_param2=h_param2, minRadius=h_min_radius, maxRadius=h_max_radius, inner_hough_circles=True) btn.onclick = on_click