Update app.py

app.py

@@ -84,6 +84,7 @@ if file_str:
     start_x = geo_tiff.get_wgs_84_coords(0, 0)[0]
     start_y = geo_tiff.get_wgs_84_coords(i, j)[1]
 
+    '''
     print(start_x, start_y)
     print(deg_pixel_x,deg_pixel_y )
     print('_'*50 + ' Ende '+ '_'*50)
@@ -94,7 +95,7 @@ if file_str:
                                                          deg_pixel_x*size+size*deg_pixel_x, start_y+int(j/size/2)*deg_pixel_y*size+size*deg_pixel_y)]
 
     array = geo_tiff.read_box(area_box.copy())
-    
+    '''
 
     size=(416, 416)
 
@@ -102,8 +103,8 @@ if file_str:
     with Image.open(file) as im:
         im.thumbnail(size)
 
-    #gloabl_image = c2.image(im)
-    gloabl_image = c2.image(array/255)    
+    gloabl_image = c2.image(im)
+    #gloabl_image = c2.image(array/255)    
 
     threshold = c2.text_input(
         label='Detection threshold: Reduce to detect more trees, increase to remove duplicates', value=0.05)
@@ -190,10 +191,12 @@ if file_str:
                 t1 = time.time()
                 boxes, scores, classes, nums = yolo.predict(img_in, verbose=False)
 
+                '''
                 print('image min max:', img.min(), img.max(), img.shape)
 
                 images.append(img.astype('float')/255)
                 imgg.image(images, width=230)
+                '''
 
                 if nums > 0:
 
@@ -215,8 +218,8 @@ if file_str:
                     img = cv2.putText(img, "Time: {:.2f}ms".format(sum(times)/len(times)*1000), (0, 30),
                                       cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
 
-                    #images.append(img)
-                    #imgg.image(images, width=230)
+                    images.append(img/255)
+                    imgg.image(images, width=230)
 
                     for ind in range(nums[0]):
                         classes_found.append(class_names[int(classes[0][ind])])