Add gluoncv display

Author: LauLauThom

test.py

@@ -1,21 +1,32 @@
 from skimage.data import coins
 import matplotlib.pyplot as plt
 import MTM, cv2
+import numpy as np
 
-## Get image and templates by cropping
+#%% Get image and templates by cropping
 smallCoin = coins()[37:37+38, 80:80+41] 
 bigCoin   = coins()[14:14+59,302:302+65]
 image     = coins()
 
-## Perform matching
-#tableHit = MTM.matchTemplates([('small', smallCoin), ('big', bigCoin)], image, score_threshold=0.6, method=cv2.TM_CCOEFF_NORMED, maxOverlap=0) # Correlation-score
-tableHit = MTM.matchTemplates([('small', smallCoin), ('big', bigCoin)], image, score_threshold=0.4, method=cv2.TM_SQDIFF_NORMED, maxOverlap=0) # Difference-score
+#%% Perform matching
+tableHit = MTM.matchTemplates([('small', smallCoin), ('big', bigCoin)], image, score_threshold=0.6, method=cv2.TM_CCOEFF_NORMED, maxOverlap=0) # Correlation-score
+#tableHit = MTM.matchTemplates([('small', smallCoin), ('big', bigCoin)], image, score_threshold=0.4, method=cv2.TM_SQDIFF_NORMED, maxOverlap=0) # Difference-score
 
 print("Found {} coins".format(len(tableHit)))
-
 print(tableHit)
 
-
-## Display matches
+#%% Display matches
 Overlay = MTM.drawBoxesOnRGB(image, tableHit, showLabel=True)
+plt.figure()
 plt.imshow(Overlay)
+
+#%% Use GluonCV for display
+import gluoncv as gcv
+
+# Convert from x,y,w,h to xmin, ymin, xmax, ymax
+BBoxes_xywh = np.array( tableHit["BBox"].tolist() ) 
+BBoxes_xyxy = gcv.utils.bbox.bbox_xywh_to_xyxy(BBoxes_xywh)
+
+Overlay2 = gcv.utils.viz.cv_plot_bbox(cv2.cvtColor(image, cv2.COLOR_GRAY2RGB), BBoxes_xyxy.astype("float64"), scores=tableHit["Score"].to_numpy(), thresh=0  )
+plt.figure()
+plt.imshow(Overlay2)