log

aimodel.py

@@ -79,6 +79,7 @@ def get_meter_bbox(fl:Florence, img:Image):
     return bbox
 
 def get_circles(fl:Florence, img2:Image, polygons:list):    
+    logger.info('get_circle')
     img3 = Image.new('L', img2.size, color = 'black')
     draw = ImageDraw.Draw(img3)
     for polygon in polygons:
@@ -102,12 +103,14 @@ def get_needle_polygons(fl:Florence, img2:Image):
     return needle_polygons
 
 def get_ocr(fl:Florence, img2:Image):
+    logger.info('get_ocr')
     parsed_answer = fl.run(img2, TASK_OCR)
     assert len(parsed_answer)==1
     k,v = parsed_answer.popitem()
     return v
 
 def get_ai_model_result(img:Image.Image|Path|str, fl:Florence, fl_ft:Florence):
+    logger.info("get_ai_model_result")
     if isinstance(img, Path):
         key = img.parts[-1]
     elif isinstance(img, str):
@@ -119,13 +122,15 @@ def get_ai_model_result(img:Image.Image|Path|str, fl:Florence, fl_ft:Florence):
     if isinstance(img, (Path, str)):        
         img = Image.open(img)
     meter_bbox = get_meter_bbox(fl, img)
+    logger.info("get meter_bbox")
     img2 = img.crop(meter_bbox)
     needle_polygons = get_needle_polygons(fl, img2)
+    logger.info("get needle_polygons")
     result = AIModelResult(img, img2, meter_bbox, needle_polygons,
                             get_circles(fl, img2, needle_polygons),
                             get_ocr(fl, img2),
                             get_ocr(fl_ft, img2)
-                            )
+                            )            
     if key is not None:
         cached_results[key] = result
     return result
@@ -134,6 +139,7 @@ from skimage.measure import regionprops
 from skimage.measure import EllipseModel
 from skimage.draw import ellipse_perimeter
 def get_regionprops(polygons:list) -> regionprops:
+    logger.info('get_regionprops')
     coords = np.concatenate(polygons).reshape(-1, 2)
     size = tuple( (coords.max(axis=0)+2).astype('int') )
     img = Image.new('L', size, color = 'black')
@@ -193,11 +199,15 @@ MeterResult = namedtuple('MeterResult', [
 
 def read_meter(img:Image.Image|str|Path, fl, fl_ft):
     # ai model results
+    logger.info("read_meter")
     result = get_ai_model_result(img, fl, fl_ft)
+    logger.info('ai model done')
     
     # needle direction
     coords = np.concatenate(result.needle_polygons).reshape(-1, 2)
     orign, direction = estimate_line(coords)
+    logger.info('needle direction done')
+
     
     # calculate the meter center 
     circle_props = get_regionprops(result.circle_polygons)
@@ -248,6 +258,7 @@ def read_meter(img:Image.Image|str|Path, fl, fl_ft):
     # use the model to calculated the needle psi and kg/cm2
     needle_psi = lm.predict(needle_theta)[1]
     needle_kg_cm2 = needle_psi / 14.223
+    logger.info('meter result done')
 
     return MeterResult(result=result,
                           needle_psi=needle_psi,
@@ -270,6 +281,7 @@ def read_meter(img:Image.Image|str|Path, fl, fl_ft):
 
 
 def more_visualization_data(meter_result:MeterResult):
+    logger.info('more visualization')
     result = meter_result.result
     center = meter_result.center
     # following calculations are for visualization and debugging
@@ -290,6 +302,7 @@ def more_visualization_data(meter_result:MeterResult):
     return inlier_theta, inlier_psi, predict_theta, predict_psi, needle_head, direction
 
 def visualization(meter_result:MeterResult):
+    logger.info('visualization')
     result = meter_result.result
     center = meter_result.center
     needle_psi, needle_kg_cm2 = meter_result.needle_psi, meter_result.needle_kg_cm2
@@ -358,6 +371,7 @@ def visualization(meter_result:MeterResult):
              f'psi={needle_psi:.1f} kg_cm2={needle_kg_cm2:.2f}', color='yellow')
     plt.xlim(0, 360)
     plt.ylim(0, 160)
+    logger.info('visualization done')
     return draw._image, plt.gcf()
 
 def clear_cache():