many small improvements
This commit is contained in:
		| @ -20,6 +20,8 @@ toast_container.position_end = 0 | ||||
|  | ||||
|  | ||||
| def onclick(_): | ||||
|     # NOTE: you can also use app.toast(...) and app.alert_success(...) to show toasts | ||||
|     # and alerts at the default bottom right position | ||||
|     alert = bHTML.AlertSuccess("You clicked me!", parent=app.main) | ||||
|     alert.w = 25 | ||||
|     toast = bHTML.Toast("You clicked me!", parent=toast_container) | ||||
| @ -27,4 +29,3 @@ def onclick(_): | ||||
|     toast.show() | ||||
|  | ||||
| btn.onclick = onclick | ||||
|      | ||||
										
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							| @ -1,5 +1,5 @@ | ||||
|  | ||||
| const pwa_version = "01_hello_world_202306031006" | ||||
| const pwa_version = "01_hello_world_202306041650" | ||||
| const assets = ["./index.html", | ||||
|     "./main.py", | ||||
|     "./resources/bootstrap.css", | ||||
| @ -48,4 +48,15 @@ self.addEventListener('fetch', event => { | ||||
|         return response; | ||||
|     })()); | ||||
| }); | ||||
| self.addEventListener('activate', (event) => { | ||||
|     event.waitUntil( | ||||
|         caches.keys().then((keyList) => { | ||||
|             return Promise.all(keyList.map((key) => { | ||||
|                 if(key !== pwa_version) { | ||||
|                     return caches.delete(key); | ||||
|                 } | ||||
|             })); | ||||
|         }) | ||||
|     ); | ||||
| }); | ||||
|      | ||||
										
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							| @ -1,5 +1,5 @@ | ||||
|  | ||||
| const pwa_version = "02_image_filter_202306031006" | ||||
| const pwa_version = "02_image_filter_202306041650" | ||||
| const assets = ["./index.html", | ||||
|     "./main.py", | ||||
|     "./resources/bootstrap.css", | ||||
| @ -48,4 +48,15 @@ self.addEventListener('fetch', event => { | ||||
|         return response; | ||||
|     })()); | ||||
| }); | ||||
| self.addEventListener('activate', (event) => { | ||||
|     event.waitUntil( | ||||
|         caches.keys().then((keyList) => { | ||||
|             return Promise.all(keyList.map((key) => { | ||||
|                 if(key !== pwa_version) { | ||||
|                     return caches.delete(key); | ||||
|                 } | ||||
|             })); | ||||
|         }) | ||||
|     ); | ||||
| }); | ||||
|      | ||||
										
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							| @ -1,5 +1,5 @@ | ||||
|  | ||||
| const pwa_version = "03_numpy_grid_demo_202306031006" | ||||
| const pwa_version = "03_numpy_grid_demo_202306041650" | ||||
| const assets = ["./index.html", | ||||
|     "./main.py", | ||||
|     "./resources/bootstrap.css", | ||||
| @ -48,4 +48,15 @@ self.addEventListener('fetch', event => { | ||||
|         return response; | ||||
|     })()); | ||||
| }); | ||||
| self.addEventListener('activate', (event) => { | ||||
|     event.waitUntil( | ||||
|         caches.keys().then((keyList) => { | ||||
|             return Promise.all(keyList.map((key) => { | ||||
|                 if(key !== pwa_version) { | ||||
|                     return caches.delete(key); | ||||
|                 } | ||||
|             })); | ||||
|         }) | ||||
|     ); | ||||
| }); | ||||
|      | ||||
| @ -9,13 +9,13 @@ import numpy as np | ||||
| from PIL import Image | ||||
| import cv2 | ||||
|  | ||||
| loaded_img = None | ||||
| loaded_img:np.ndarray = None | ||||
|  | ||||
| app = bootstrap_templates.PyScriptBootstrapDashboard(parent_element="pyscript_app", brand_name="Pyscript Cell Detector") | ||||
| app = bootstrap_templates.PyScriptBootstrapDashboard(parent_element="pyscript_app", brand_name="Pyscript Cell Colony Detector") | ||||
|  | ||||
| main_div = bHTML.BootstrapContainer(parent=app.main) | ||||
| main_div.w = 100 | ||||
| result_div = bHTML.BootstrapContainer(parent=main_div) | ||||
| result_div = bHTML.BootstrapContainer (parent=main_div) | ||||
|  | ||||
|  | ||||
| def process_image(image, | ||||
| @ -24,13 +24,20 @@ def process_image(image, | ||||
|                   hough_param2 = 500, | ||||
|                   minRadius = 100, | ||||
|                   maxRadius=500, | ||||
|                   inner_hough_circles=True): | ||||
|                   inner_hough_param1 = 25, | ||||
|                   inner_hough_param2 = 50, | ||||
|                   inner_hough_circles=True, | ||||
|                   cell_colony_color_channel:int = None): | ||||
|      | ||||
|     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) | ||||
|     if cell_colony_color_channel is None: | ||||
|         gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | ||||
|     else: | ||||
|         gray = image.astype(float)[...,cell_colony_color_channel] | ||||
|         gray = (255 * gray / gray.max()).astype(np.uint8) | ||||
|  | ||||
|     # Use median blur to reduce noise | ||||
|     gray = cv2.medianBlur(gray, 5) | ||||
| @ -46,7 +53,7 @@ def process_image(image, | ||||
|                                maxRadius=maxRadius) | ||||
|  | ||||
|     if circles is None: | ||||
|         bHTML.AlertDanger("No cell areas detected in image", parent=app.main) | ||||
|         app.alert_danger("No cell areas detected in image") | ||||
|         return False | ||||
|      | ||||
|     # Convert to integers | ||||
| @ -84,7 +91,14 @@ def process_image(image, | ||||
|  | ||||
|         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) | ||||
|             circles = cv2.HoughCircles(well_gray, | ||||
|                                        cv2.HOUGH_GRADIENT, | ||||
|                                        1, | ||||
|                                        minDist=hough_min_dist, | ||||
|                                        param1=inner_hough_param1, | ||||
|                                        param2=inner_hough_param2, | ||||
|                                        minRadius=int((min(well.shape[0],well.shape[1]) // 2)*0.79), | ||||
|                                        maxRadius=int((min(well.shape[0],well.shape[1]) // 2)*0.95)) | ||||
|             if circles is not None: | ||||
|                 circles = np.uint16(np.around(circles)) | ||||
|                 i = circles[0,:][0] | ||||
| @ -109,9 +123,11 @@ def process_image(image, | ||||
|         second_wells = first_wells | ||||
|         second_wells_gray = first_wells_gray | ||||
|      | ||||
|     tabs = {} | ||||
|  | ||||
|     for well, well_gray in zip(second_wells, second_wells_gray): | ||||
|     for well in second_wells: | ||||
|          | ||||
|         well_gray = cv2.cvtColor(well, cv2.COLOR_BGR2GRAY) | ||||
|         _, binary = cv2.threshold(255 - well_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) | ||||
|      | ||||
|  | ||||
| @ -120,29 +136,37 @@ def process_image(image, | ||||
|         cleaned = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel) | ||||
|          | ||||
|  | ||||
|         radius = well.shape[0] // 2 -1 | ||||
|         radius = min(well.shape[0], well.shape[1]) // 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) | ||||
|         cv2.circle(well_mask, (well.shape[1] // 2, well.shape[0] // 2), 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 = bHTML.BootstrapContainer(f"ratio: {ratio * 100}%") | ||||
|         div_result.shadow = bHTML.Shadow.LARGE | ||||
|         div_result.rounded = True | ||||
|         div_result.w = 50 | ||||
|         div_result.w = 75 | ||||
|         div_result.h = 75 | ||||
|         div_result.rounded_size = 50 | ||||
|         div_result.p = 3 | ||||
|  | ||||
|         well = well.copy() | ||||
|         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.rounded_size = 50 | ||||
|         final_img.shadow = bHTML.Shadow.LARGE | ||||
|         final_img.w = 100 | ||||
|  | ||||
|         tabs[f"Well #{len(tabs) + 1}"] = div_result | ||||
|      | ||||
|     tabs = bHTML.Tabs(tabs, parent=result_div) | ||||
|     tabs.w = 100 | ||||
|  | ||||
|  | ||||
| div = bHTML.BootstrapContainer("Controls", parent=app.sidebar) | ||||
| @ -150,64 +174,135 @@ 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 | ||||
| btn.mt = 3 | ||||
| btn.mb = 3 | ||||
| btn.ml = 4 | ||||
| btn.mr = 4 | ||||
|  | ||||
| i_hough_min_dist = bInputs.InputInt("hough min distance [px]", | ||||
|                                     parent=app.sidebar, | ||||
|                                     help_text="minimal distance between wells in pixels") | ||||
| i_hough_min_dist.value = 500 | ||||
|  | ||||
| i_hough_param1 = bInputs.InputInt("hough param1", | ||||
|                                   parent=app.sidebar, | ||||
|                                   help_text="parameter for canny edge detector") | ||||
| i_hough_param1.value = 80 | ||||
|  | ||||
| i_hough_param2 = bInputs.InputInt("hough param2", | ||||
|                                   parent=app.sidebar, | ||||
|                                   help_text="increase this value to prevent false circle detection") | ||||
| i_hough_param2.value = 500 | ||||
|  | ||||
| i_hough_min_radius = bInputs.InputInt("hough min radius [px]", | ||||
|                                        parent=app.sidebar, | ||||
|                                        help_text="min radius for circle detection") | ||||
| i_hough_min_radius.value = 100 | ||||
|  | ||||
| i_hough_max_radius = bInputs.InputInt("hough max radius [px]", | ||||
|                                        parent=app.sidebar, | ||||
|                                        help_text="max radius for circle detection") | ||||
| i_hough_max_radius.value = 500 | ||||
|  | ||||
| i_inner_hough = bInputs.InputCheckboxSingle("nested hough transform?", | ||||
|                                              parent=app.sidebar, | ||||
|                                              help_text="if set, circle detection will be applied twice on detected wells") | ||||
| i_inner_hough.value = True | ||||
| i_inner_hough.m = 3 | ||||
|  | ||||
| i_inner_hough_param1 = bInputs.InputInt("inner hough param1", parent=app.sidebar) | ||||
| i_inner_hough_param1.value = 25 | ||||
|  | ||||
| i_inner_hough_param2 = bInputs.InputInt("inner hough param2", parent=app.sidebar) | ||||
| i_inner_hough_param2.value = 50 | ||||
|  | ||||
| i_use_color_channel = bInputs.InputSelect(["all", | ||||
|                                             "red", | ||||
|                                             "green", | ||||
|                                             "blue"], | ||||
|                                             label_text="cell colony color channel", | ||||
|                                             parent=app.sidebar, | ||||
|                                             help_text="if cells are more present in a specific channel, select it here") | ||||
| i_use_color_channel.value = "blue" | ||||
|  | ||||
|  | ||||
|  | ||||
|  | ||||
|  | ||||
| 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 | ||||
|  | ||||
|     try: | ||||
|         global loaded_img | ||||
|         f.seek(0) | ||||
|         img = np.array(Image.open(f)) | ||||
|         for child in result_div.children: | ||||
|             child.destroy() | ||||
|  | ||||
|         bHTML.BootstrapContainer("input image:", parent=result_div) | ||||
|         output = bHTML.Image.from_numpy_array(img, parent=result_div) | ||||
|         output.rounded = True | ||||
|         output.rounded_size = 10 | ||||
|         output.shadow = bHTML.Shadow.LARGE | ||||
|         output.width = "100%" | ||||
|         output.height = "100%" | ||||
|          | ||||
|         loaded_img = img | ||||
|  | ||||
|         app.toast("successfully loaded image", "info") | ||||
|         #toast = bHTML.Toast("successfully loaded image", title="info", parent=toast_container) | ||||
|         #toast.animation = True | ||||
|         #toast.show() | ||||
|     except Exception as e: | ||||
|         for child in result_div.children: | ||||
|             child.destroy() | ||||
|         bHTML.AlertDanger(f"error while loading image: {str(e)}", parent=alert_container) | ||||
|  | ||||
|  | ||||
| image_input.onchange = on_image_change     | ||||
|  | ||||
| def on_click(*args, **kwargs): | ||||
|     if loaded_img is None: | ||||
|         bHTML.AlertDanger("No image loaded", parent=app.main) | ||||
|         app.alert_danger("No image loaded") | ||||
|         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) | ||||
|     for child in result_div.children: | ||||
|         child.destroy() | ||||
|  | ||||
|     try: | ||||
|         h_min_dist = int(i_hough_min_dist.value) | ||||
|         h_param1 = int(i_hough_param1.value) | ||||
|         h_param2 = int(i_hough_param2.value) | ||||
|         h_inner_param1 = int(i_inner_hough_param1.value) | ||||
|         h_inner_param2 = int(i_inner_hough_param2.value) | ||||
|         h_min_radius = int(i_hough_min_radius.value) | ||||
|         h_max_radius = int(i_hough_max_radius.value) | ||||
|         inner_hough = bool(i_inner_hough.value) | ||||
|         color_channel = { | ||||
|             "all": None, | ||||
|             "red": 0, | ||||
|             "green": 1, | ||||
|             "blue": 2 | ||||
|         }[i_use_color_channel.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) | ||||
|  | ||||
|         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_param1=h_inner_param1, | ||||
|                     inner_hough_param2=h_inner_param2, | ||||
|                     inner_hough_circles=inner_hough, | ||||
|                     cell_colony_color_channel=color_channel) | ||||
|         app.toast("successfully processed image", title="info") | ||||
|  | ||||
|     except Exception as e: | ||||
|         for child in result_div.children: | ||||
|             child.destroy() | ||||
|         app.alert_danger(f"error while processing image: {str(e)}") | ||||
|  | ||||
| btn.onclick = on_click | ||||
|  | ||||
|  | ||||
										
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							| @ -1,5 +1,5 @@ | ||||
|  | ||||
| const pwa_version = "04_cell_detector_202306031006" | ||||
| const pwa_version = "04_cell_detector_202306041650" | ||||
| const assets = ["./index.html", | ||||
|     "./main.py", | ||||
|     "./resources/bootstrap.css", | ||||
| @ -48,4 +48,15 @@ self.addEventListener('fetch', event => { | ||||
|         return response; | ||||
|     })()); | ||||
| }); | ||||
| self.addEventListener('activate', (event) => { | ||||
|     event.waitUntil( | ||||
|         caches.keys().then((keyList) => { | ||||
|             return Promise.all(keyList.map((key) => { | ||||
|                 if(key !== pwa_version) { | ||||
|                     return caches.delete(key); | ||||
|                 } | ||||
|             })); | ||||
|         }) | ||||
|     ); | ||||
| }); | ||||
|      | ||||
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