Merge pull request #30 from HelmholtzAI-Consultants-Munich/integratio…

…n-tests

Integration tests + Patch Model

src/client/dcp_client/app.py

@@ -36,7 +36,7 @@ def search_segs(self, img_directory, cur_selected_img):
         """Returns a list of full paths of segmentations for an image"""
         # Take all segmentations of the image from the current directory:
         search_string = utils.get_path_stem(cur_selected_img) + '_seg'
-        seg_files = [file_name for file_name in os.listdir(img_directory) if search_string in file_name]
+        seg_files = [file_name for file_name in os.listdir(img_directory) if (search_string == utils.get_path_stem(file_name) or str(file_name).startswith(search_string))]
         return seg_files
 
 

src/client/requirements.txt

@@ -1,2 +1,3 @@
 napari[pyqt5]>=0.4.17
-bentoml[grpc]>=1.0.13
+bentoml[grpc]>=1.0.13
+pytest>=7.4.3

src/client/test/test_app.py

@@ -1,62 +1,69 @@
 import os
+import sys
 from skimage import data
 from skimage.io import imsave
-import unittest
+import pytest
+
+sys.path.append("../")
 
 from dcp_client.app import Application
 from dcp_client.utils.bentoml_model import BentomlModel
 from dcp_client.utils.fsimagestorage import FilesystemImageStorage
 from dcp_client.utils.sync_src_dst import DataRSync
 
-class TestApplication(unittest.TestCase):
-
-    def test_run_train(self):
-        pass
-
-    def test_run_inference(self):
-        pass
-
-    def test_load_image(self):
-
-        img = data.astronaut()
-        img2 = data.cat()
-        os.mkdir('in_prog')
-        imsave('in_prog/test_img.png', img)
-        imsave('in_prog/test_img2.png', img2)
-        rsyncer = DataRSync(user_name="local",
-                          host_name="local",
-                          server_repo_path='.')
-        self.app = Application(BentomlModel(),
-                               rsyncer,
-                               FilesystemImageStorage(),
-                               "0.0.0.0",
-                               7010)
-
-        self.app.cur_selected_img = 'test_img.png'
-        self.app.cur_selected_path = 'in_prog'
-
-        img_test = self.app.load_image() # if image_name is None
-        self.assertEqual(img.all(), img_test.all())
-        img_test2 = self.app.load_image('test_img2.png') # if a filename is given
-        self.assertEqual(img2.all(), img_test2.all())
-
-        # delete everyting we created
-        os.remove('in_prog/test_img.png')
-        os.remove('in_prog/test_img2.png')
-        os.rmdir('in_prog')
-
-    def test_save_image(self):
-         pass
-
-    def test_move_images(self):
-         pass
-
-    def test_delete_images(self):
-         pass
-
-    def test_search_segs(self):
-         pass
+
+@pytest.fixture
+def app():
+    img = data.astronaut()
+    img2 = data.cat()
+    os.mkdir('in_prog')
+
+    imsave('in_prog/test_img.png', img)
+    imsave('in_prog/test_img2.png', img2)
+
+    rsyncer = DataRSync(user_name="local", host_name="local", server_repo_path='.')
+    app = Application(BentomlModel(), rsyncer, FilesystemImageStorage(), "0.0.0.0", 7010)
+
+    app.cur_selected_img = 'test_img.png'
+    app.cur_selected_path = 'in_prog'
+
+    return app, img, img2
+
+def test_load_image(app):
+    app, img, img2 = app  # Unpack the app, img, and img2 from the fixture
+
+    img_test = app.load_image()  # if image_name is None
+    assert img.all() == img_test.all()
+
+    img_test2 = app.load_image('test_img2.png')  # if a filename is given
+    assert img2.all() == img_test2.all()
+
+    # delete everything we created
+    os.remove('in_prog/test_img.png')
+    os.remove('in_prog/test_img2.png')
+    os.rmdir('in_prog')
+
+def test_run_train():
+    pass
+
+def test_run_inference():
+    pass
+
+def test_save_image():
+    pass
+
+def test_move_images():
+    pass
+
+def test_delete_images():
+    pass
+
+def test_search_segs():
+    pass
+
+
+
+
+
 
 
-if __name__=='__main__':
-	unittest.main()

src/client/test/test_fsimagestorage.py

@@ -1,51 +1,46 @@
 import os
+import pytest
 from skimage.io import imsave
 from skimage import data
-import unittest
 
 from dcp_client.utils.fsimagestorage import FilesystemImageStorage
 
-
-class TestFilesystemImageStorage(unittest.TestCase):
-
-    def test_load_image(self):
-        fis = FilesystemImageStorage()
-        img = data.astronaut()
-        fname = 'test_img.png'
-        imsave(fname, img)
-        img_test = fis.load_image('.', fname)
-        self.assertEqual(img.all(), img_test.all())
-        os.remove(fname)
-
-    def test_move_image(self):
-        fis = FilesystemImageStorage()
-        img = data.astronaut()
-        fname = 'test_img.png'
-        os.mkdir('temp')
-        imsave(fname, img)
-        fis.move_image('.', 'temp', fname)
-        self.assertTrue(os.path.exists('temp/test_img.png'))
-        os.remove('temp/test_img.png')
-        os.rmdir('temp')
-
-    def test_save_image(self):
-        fis = FilesystemImageStorage()
-        img = data.astronaut()
-        fname = 'test_img.png'
-        fis.save_image('.', fname, img)
-        self.assertTrue(os.path.exists(fname))
-        os.remove(fname)
-
-    def test_delete_image(self):
-        fis = FilesystemImageStorage()
-        img = data.astronaut()
-        fname = 'test_img.png'
-        os.mkdir('temp')
-        imsave('temp/test_img.png', img)
-        fis.delete_image('temp', fname)
-        self.assertFalse(os.path.exists('temp/test_img.png'))
-        os.rmdir('temp')
-
-
-if __name__=='__main__':
-	unittest.main()
+@pytest.fixture
+def fis():
+    return FilesystemImageStorage()
+
+@pytest.fixture
+def sample_image():
+    # Create a sample image
+    img = data.astronaut()
+    fname = 'test_img.png'
+    imsave(fname, img)
+    return fname
+
+def test_load_image(fis, sample_image):
+    img_test = fis.load_image('.', sample_image)
+    assert img_test.all() == data.astronaut().all()
+    os.remove(sample_image)
+
+def test_move_image(fis, sample_image):
+    temp_dir = 'temp'
+    os.mkdir(temp_dir)
+    fis.move_image('.', temp_dir, sample_image)
+    assert os.path.exists(os.path.join(temp_dir, 'test_img.png'))
+    os.remove(os.path.join(temp_dir, 'test_img.png'))
+    os.rmdir(temp_dir)
+
+def test_save_image(fis):
+    img = data.astronaut()
+    fname = 'output.png'
+    fis.save_image('.', fname, img)
+    assert os.path.exists(fname)
+    os.remove(fname)
+
+def test_delete_image(fis, sample_image):
+    temp_dir = 'temp'
+    os.mkdir(temp_dir)
+    fis.move_image('.', temp_dir, sample_image)
+    fis.delete_image(temp_dir, 'test_img.png')
+    assert not os.path.exists(os.path.join(temp_dir, 'test_img.png'))
+    os.rmdir(temp_dir)

src/server/dcp_server/config.cfg

@@ -1,26 +1,66 @@
 {
-    "setup":{
-    "segmentation": "GeneralSegmentation",
-    "accepted_types": [".jpg", ".jpeg", ".png", ".tiff", ".tif"],
-    "seg_name_string": "_seg"
+    "setup": {
+        "segmentation": "GeneralSegmentation",
+        "accepted_types": [".jpg", ".jpeg", ".png", ".tiff", ".tif"],
+        "seg_name_string": "_seg"
     },
-    "service":{
-        "model_to_use": "CustomCellposeModel",
+
+    "service": {
+        "model_to_use": "CellposePatchCNN", 
         "save_model_path": "mytrainedmodel",
         "runner_name": "cellpose_runner",
         "service_name": "data-centric-platform",
         "port": 7010
     },
-    "model": {  
-        "model_type":"cyto"
+
+    "model": { 
+        "segmentor": {
+            "model_type": "cyto"
+        },
+        "classifier":{
+            "in_channels": 1,
+            "num_classes": 3,
+            "black_bg": "False",
+            "include_mask": "False"
+        }
     },
+
     "data": {
-        "data_root": "/home/ubuntu/dcp-data"
+        "data_root": "data"
     },
+
     "train":{
-        "n_epochs": 2,
-        "channels":[0]
+        "segmentor":{
+            "n_epochs": 7,
+            "channels": [0,0],
+            "min_train_masks": 1
+        },
+        "classifier":{
+            "train_data":{
+                "patch_size": 64,
+                "noise_intensity": 5,
+                "num_classes": 3
+            },
+            "n_epochs": 8,
+            "lr": 0.001,
+            "batch_size": 1,
+            "optimizer": "Adam"
+        }
     },
+
     "eval":{
+        "segmentor": {
+            "z_axis": null,
+            "channel_axis": null,
+            "rescale": 1,
+            "batch_size": 1
+        },
+        "classifier": {
+            "data":{
+                "patch_size": 64,
+                "noise_intensity": 5
+            }
+        },
+        "mask_channel_axis": 0
     }
 }