Merge branch 'master' into wataru/instruction

.github/workflows/main.yml

@@ -7,7 +7,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.7, 3.8]
+        python-version: [3.9, 3.10.9]
 
     steps:
       - uses: actions/checkout@v2
@@ -29,4 +29,4 @@ jobs:
       - name: Upload Coverage to Codecov
         uses: codecov/codecov-action@v1
         with:
-          fail_ci_if_error: true
+          fail_ci_if_error: true

.gitignore

@@ -7,3 +7,5 @@ devolearn.egg-info/
 dist/
 centroids.csv
 .vscode/
+.venv/
+.DS_Store/

README.md

@@ -19,6 +19,7 @@
 * [Predicting populations of cells within the C. elegans embryo](https://github.com/DevoLearn/devolearn#predicting-populations-of-cells-within-the-c-elegans-embryo)
 * [Contributing to DevoLearn](https://github.com/DevoLearn/devolearn/blob/master/.github/contributing.md#contributing-to-devolearn)
 * [Links to datasets](https://github.com/DevoLearn/devolearn#links-to-datasets)
+* [Links to Huggingface spaces](https://github.com/DevoLearn/devolearn#links-to-datasets)
 * [Contact us](https://github.com/DevoLearn/devolearn#authorsmaintainers)
 
 
@@ -160,6 +161,15 @@ plot.show()
 | Segmenting the cell membrane in C. elegans embryo                | [3DMMS: robust 3D Membrane Morphological Segmentation of C. elegans embryo](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2720-x#Abs1/) |
 | Segmenting the nucleus in C. elegans embryo    | [C. elegans Cell-Tracking-Challenge dataset](http://celltrackingchallenge.net/3d-datasets/)
 | Cell lineage population prediction + embryo GAN | [EPIC dataset](https://epic.gs.washington.edu/)
+| Segmenting the nucleus in C. elegans embryo    | [C. elegans Cell-Tracking-Challenge dataset](http://celltrackingchallenge.net/3d-datasets/)                                                           
+| Cell lineage population prediction + embryo GAN | [EPIC dataset](https://epic.gs.washington.edu/)   
+
+## Links to HuggingFace spaces
+| **Model**                                       | **Huggingface**                                                                                                                                                   |
+|-------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Segmenting the cell membrane in C. elegans embryo                | [Cell Membrane segmentor](https://huggingface.co/spaces/devoworm-group/membrane_segmentation) |
+| Segmenting the nucleus in C. elegans embryo    | [C. elegans Nucleus segmentor](https://huggingface.co/spaces/devoworm-group/nucleus_segmentor)                                                           
+| Cell lineage population prediction  | [Lineage population](https://huggingface.co/spaces/devoworm-group/Lineage_Population)    
 
 ## Authors/maintainers:
 * [Mayukh Deb](https://twitter.com/mayukh091)

devolearn/cell_membrane_segmentor/cell_membrane_segmentor.py

@@ -26,11 +26,12 @@
 3d segmentation model for C elegans embryo
 """
 
-def generate_centroid_image(thresh):
+def generate_centroid_image(thresh, color_mode=False):
     """Used when centroid_mode is set to True
  
     Args:
         thresh (np.array): 2d numpy array that is returned from the segmentation model
+        color_mode (bool, optional): If True, returns a 3 channel colored image. Dafaults to False.
 
     Returns:
         np.array : image containing the contours and their respective centroids 
@@ -39,7 +40,11 @@ def generate_centroid_image(thresh):
 
     thresh = cv2.blur(thresh, (5,5))
     thresh = thresh.astype(np.uint8)
-    centroid_image = np.zeros(thresh.shape)
+    if color_mode == False:
+        centroid_image = np.zeros(thresh.shape)
+    else:
+        centroid_image = np.zeros((thresh.shape[0], thresh.shape[1], 3))
+
     cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
     cnts = imutils.grab_contours(cnts)
     centroids = []
@@ -50,8 +55,12 @@ def generate_centroid_image(thresh):
             cX = int(M["m10"] / M["m00"])
             cY = int(M["m01"] / M["m00"])
             # draw the contour and center of the shape on the image
-            cv2.drawContours(centroid_image, [c], -1, (255, 255, 255), 2)
-            cv2.circle(centroid_image, (cX, cY), 2, (255, 255, 255), -1)
+            if color_mode == False:
+                cv2.drawContours(centroid_image, [c], -1, (255, 255, 255), 2)
+                cv2.circle(centroid_image, (cX, cY), 2, (255, 255, 255), -1)
+            else:
+                cv2.drawContours(centroid_image, [c], -1, (0, 0, 255), 2) #blue
+                cv2.circle(centroid_image, (cX, cY), 2, (0, 255, 0), -1) #green
             centroids.append((cX, cY))
         except:
             pass
@@ -115,7 +124,7 @@ def preprocess(self, image_grayscale_numpy):
         tensor = self.mini_transform(image_grayscale_numpy).unsqueeze(0).to(self.device)
         return tensor
 
-    def predict(self, image_path, pred_size = (350,250), centroid_mode = False):
+    def predict(self, image_path, pred_size = (350,250), centroid_mode = False, color_mode = False):
         """Loads an image from image_path and converts it to grayscale, 
         then passes it through the model and returns centroids of the segmented features.
         reference{
@@ -131,8 +140,12 @@ def predict(self, image_path, pred_size = (350,250), centroid_mode = False):
             centroid_mode set to False:
                 np.array : 1 channel image.
             centroid_mode set to True:
-                np.array : 1 channel image,
-                list : list of centroids.
+                color_mode set to False:
+                    np.array : 1 channel image,
+                    list : list of centroids.
+                color_mode set to True:
+                    np.array : 3 channel image,
+                    list : list of centroids.
         """
 
         im = cv2.imread(image_path,0)
@@ -149,11 +162,10 @@ def predict(self, image_path, pred_size = (350,250), centroid_mode = False):
         if centroid_mode == False:
             return res
         else:
-            centroid_image, centroids = generate_centroid_image(res)
+            centroid_image, centroids = generate_centroid_image(res, color_mode = color_mode)
             return centroid_image, centroids
-
 
-    def predict_from_video(self, video_path, pred_size = (350,250), save_folder = "preds", centroid_mode = False, notebook_mode = False):
+    def predict_from_video(self, video_path, pred_size = (350,250), save_folder = "preds", centroid_mode = False, color_mode = False, notebook_mode = False):
         """Splits a video from video_path into frames and passes the 
         frames through the model for predictions. Saves predicted images in save_folder.
         And optionally saves all the centroid predictions into a pandas.DataFrame. 
@@ -163,6 +175,7 @@ def predict_from_video(self, video_path, pred_size = (350,250), save_folder = "p
             pred_size (tuple, optional): size of output image,(width,height). Defaults to (350,250).
             save_folder (str, optional): path to folder to be saved in. Defaults to "preds".
             centroid_mode (bool, optional): set to true to return both the segmented image and the list of centroids. Defaults to False.
+            color_mode (bool, optional): set to true to return a color image. Defaults to False.
             notebook_mode (bool, optional): toogle between script(False) and notebook(True), for better user interface. Defaults to False.
 
         Returns:
@@ -202,7 +215,7 @@ def predict_from_video(self, video_path, pred_size = (350,250), save_folder = "p
                 res = self.model(tensor).detach().cpu().numpy()[0][0]
 
                 if centroid_mode == True:
-                    res, centroids = generate_centroid_image(res)
+                    res, centroids = generate_centroid_image(res, color_mode = color_mode)
                     filenames_centroids.append([save_name, centroids])
 
                 res = cv2.resize(res,pred_size)
@@ -214,7 +227,7 @@ def predict_from_video(self, video_path, pred_size = (350,250), save_folder = "p
                 res = self.model(tensor).detach().cpu().numpy()[0][0]
 
                 if centroid_mode == True:
-                    res, centroids = generate_centroid_image(res)
+                    res, centroids = generate_centroid_image(res, color_mode = color_mode)
                     filenames_centroids.append([save_name, centroids])
 
                 res = cv2.resize(res,pred_size)

requirements.in

@@ -0,0 +1,37 @@
+cycler
+decorator
+efficientnet-pytorch
+future
+imageio
+imgaug
+imutils
+joblib
+kiwisolver
+matplotlib
+munch
+networkx
+numpy
+opencv-python
+pandas
+Pillow
+pretrainedmodels
+pyparsing
+pytest
+python-dateutil
+pytz
+PyWavelets
+scikit-image
+scikit-learn
+scipy
+segmentation-models-pytorch
+six
+sklearn
+threadpoolctl
+tifffile
+timm
+torch
+torchvision
+tqdm
+typing-extensions
+wget
+pytest-cov