Optimisation

Author: tlkh

segmentation/app.py

@@ -52,7 +52,7 @@ def update(self):
                 continue
 
             depth_image = np.asanyarray(aligned_depth_frame.get_data())
-            depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.1), cv2.COLORMAP_RAINBOW)
+            depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.06), cv2.COLORMAP_RAINBOW)
             depth_colormap = cv2.resize(depth_colormap, (320,180))
             depth_colormap = cv2.flip(depth_colormap, 1)
 
@@ -117,15 +117,15 @@ class InferenceConfig(coco.CocoConfig):
                'bus', 'train', 'truck', 'boat', 'traffic light',
                'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
                'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
-               'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
-               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
+               'zebra', 'giraffe', 'bag', 'umbrella', 'bag', 'tie',
+               'suitcase', 'frisbee', 'skis', 'snowboard', 'ball',
                'kite', 'baseball bat', 'baseball glove', 'skateboard',
                'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
                'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
                'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
                'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
-               'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
-               'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
+               'table', 'toilet', 'monitor', 'laptop', 'mouse', 'remote',
+               'keyboard', 'phone', 'microwave', 'oven', 'toaster',
                'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
                'teddy bear', 'hair drier', 'toothbrush']
 
@@ -140,7 +140,7 @@ class InferenceConfig(coco.CocoConfig):
 cv2.namedWindow("window", cv2.WND_PROP_FULLSCREEN)
 cv2.setWindowProperty("window",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
 
-N = 20
+N = 12
 hsv = [(i / N, 1, 1.0) for i in range(N)]
 colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
 
@@ -172,7 +172,7 @@ def render_masks(image, boxes, masks, class_ids, N):
                                     (1 - alpha) + alpha * color[2] * 255,
                                     image[:, :, 2])
 
-        cv2.putText(image,caption,(cent_x, cent_y), font, 0.8,(255,255,255), 1, cv2.LINE_AA)
+        cv2.putText(image,caption,(cent_x, cent_y), font, 0.8,(60,60,255), 2, cv2.LINE_AA)
 
     return image
 
@@ -187,6 +187,8 @@ def render_masks(image, boxes, masks, class_ids, N):
     boxes, masks = r["rois"], r["masks"]
     class_ids = r["class_ids"]
     N = boxes.shape[0]
+    if N>12:
+        N=12
 
     if not N:
         masked_image = color_image_f