small fix to video

create_video.py

@@ -18,7 +18,7 @@ def noise_process(numpy_image, steps=149):
 def generate_video(numpy_image):
     save_path = "result.mp4"
     width = 256
-    hieght = 256
+    height = 256
     fps = 30
     sec = 5
     image_lst = noise_process(numpy_image)
@@ -29,16 +29,10 @@ def generate_video(numpy_image):
     image_lst = np.repeat(image_lst, copies, axis=0)
     image_lst = np.concatenate((image_lst, image_lst[:spill_over]), axis=0)
     image_lst = image_lst[::-1]
-    fourcc = cv2.VideoWriter_fourcc(*'MP42')
-    video = cv2.VideoWriter(save_path, fourcc, float(fps), (width, hieght))
+    fourcc = cv2.VideoWriter_fourcc(*'avc1')
+    video = cv2.VideoWriter(save_path, fourcc, float(fps), (width, height))
     for frame_count in range(fps * sec):
-        img = np.expand_dims(image_lst[frame_count],2)
+        img = np.expand_dims(image_lst[frame_count], 2)
         video.write(img.astype(np.uint8))
     video.release()
     return save_path
-
-
-if __name__ == "__main__":
-    im = Image.open(r"C:\Users\folle\Downloads\ldm_images\Atelectasis\0_4.png")
-    im_to_disp = np.array(im)
-    generate_video(im_to_disp)

grad_cam.py

@@ -69,11 +69,9 @@ class GradCamGenerator:
         image_PIL.save(gc_filename)
 
     def transform_pil_to_tensor(self, pil_image):
-        # ImageNet mean and std
         mean = [0.485, 0.456, 0.406]
         std = [0.229, 0.224, 0.225]
 
-        # transformation to be applied
         transform = transforms.Compose([
             transforms.Resize(224),
             transforms.ToTensor(),
@@ -83,7 +81,6 @@ class GradCamGenerator:
         return tensor.unsqueeze(0)
 
     def pil_loader(self, path, n_channels):
-        # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
         with open(path, 'rb') as f:
             img = Image.open(f)
             if n_channels == 1:
@@ -102,10 +99,3 @@ def create_GC_from_folder(path, classifier='checkpoint', layer_name='features.no
                                                       (not os.path.exists(cf+'/'+f[:-4]+'_overlay.png') or not overlay))]
         for cfil in tqdm(files):
             GC.generate_grad_cam(cfil)
-            #GCmap = GC.generate_grad_cam(cfil)
-            #GC.save_img(GCmap, cfil)
-
-if __name__=='__main__':
-    import matplotlib.pyplot as plt
-    path = 'data/'
-    create_GC_from_folder(path)

load_disease_info.py

@@ -10,7 +10,3 @@ def disease_info(disease_index):
     html_text += "<h1>Symptoms</h1>\n"  + "<p>" + str(df["Symptoms"]) + "</p>" + "<br><br>"
     html_text += "<h1>Treatment</h1>\n"  + "<p>" + str(df["Treatment"]) + "</p>" + "<br><br>"
     return html_text
-
-
-if __name__ == "__main__":
-    disease_info("Pneumothorax")

overlay_image.py

@@ -18,6 +18,3 @@ def overlay_numpy(im, heat_map, path):
     file_name = os.path.splitext(path)[0] + '_overlay.png'
     fig.savefig(file_name, bbox_inches='tight', pad_inches=0, transparent=True, dpi=1200)
     plt.close()
-
-if __name__=='__main__':
-    overlay_file('1_99.png', '1_99_gc.png')

packages.txt

@@ -0,0 +1 @@
+ubuntu-restricted-extras