《zw版·Halcon-delphi系列原创教程》 水果自动分类脚本（机器学习、人工智能）

 1 * This example program shows how to apply a general GMM
 2 * classification to distinguish citrus fruits using the
 3 * features ‘area‘ and ‘circularity‘. Additionally, the
 4 * 2D feature space for the extracted fruits is visualized.
 5 * 
 6 read_image (Image, ‘color/citrus_fruits_01‘)
 7 get_image_pointer1 (Image, Pointer, Type, Width, Height)
 8 dev_close_window ()
 9 dev_open_window (0, 0, Width, Height, ‘white‘, WindowHandle)
10 set_display_font (WindowHandle, 12, ‘courier‘, ‘true‘, ‘false‘)
11 dev_set_draw (‘margin‘)
12 dev_set_line_width (2)
13 dev_display (Image)
14 dev_update_window (‘off‘)
15 dev_update_pc (‘off‘)
16 dev_update_var (‘off‘)
17 * 
18 FeaturesArea := []
19 FeaturesCircularity := []
20 ClassName := [‘orange‘,‘lemon‘]
21 * 
22 * Create a GMM classifier
23 create_class_gmm (2, 2, 1, ‘spherical‘, ‘normalization‘, 10, 42, GMMHandle)
24 * 
25 * Add training samples
26 for i := 1 to 4 by 1
27 read_image (Image, ‘color/citrus_fruits_‘ + i$‘.2d‘)
28 dev_display (Image)
29 * ‘Add Samples‘
30 get_regions (Image, SelectedRegions)
31 dev_display (SelectedRegions)
32 count_obj (SelectedRegions, NumberObjects)
33 for j := 1 to NumberObjects by 1
34 select_obj (SelectedRegions, ObjectSelected, j)
35 get_features (ObjectSelected, WindowHandle, Circularity, Area, RowRegionCenter, ColumnRegionCenter)
36 FeaturesArea := [FeaturesArea,Area]
37 FeaturesCircularity := [FeaturesCircularity,Circularity]
38 FeatureVector := real([Circularity,Area])
39 if (i <= 2)
40 add_sample_class_gmm (GMMHandle, FeatureVector, 0, 0)
41 disp_message (WindowHandle, ‘Add to Class:‘ + ClassName[0], ‘window‘, RowRegionCenter, ColumnRegionCenter - 100, ‘black‘, ‘true‘)
42 else
43 add_sample_class_gmm (GMMHandle, FeatureVector, 1, 0)
44 disp_message (WindowHandle, ‘Add to Class:‘ + ClassName[1], ‘window‘, RowRegionCenter, ColumnRegionCenter - 100, ‘black‘, ‘true‘)
45 endif
46 endfor
47 disp_continue_message (WindowHandle, ‘black‘, ‘true‘)
48 stop ()
49 endfor
50 dev_clear_window ()
51 * 
52 * Visualize the feature space
53 visualize_2D_feature_space (Cross, Height, Width, WindowHandle, FeaturesArea[0:5], FeaturesCircularity[0:5], ‘dim gray‘, 18)
54 * ‘oranges‘, 40, 440
55 visualize_2D_feature_space (Cross, Height, Width, WindowHandle, FeaturesArea[6:11], FeaturesCircularity[6:11], ‘light gray‘, 18)
56 * ‘lemons‘, 70, 440
57 disp_continue_message (WindowHandle, ‘black‘, ‘true‘)
58 stop ()
59 * 
60 * Train the classifier
61 train_class_gmm (GMMHandle, 100, 0.001, ‘training‘, 0.0001, Centers, Iter)
62 * 
63 * Classify
64 for i := 1 to 15 by 1
65 read_image (Image, ‘color/citrus_fruits_‘ + i$‘.2d‘)
66 dev_display (Image)
67 * ‘Classify Image‘, 10, 10
68 get_regions (Image, SelectedRegions)
69 dev_display (SelectedRegions)
70 count_obj (SelectedRegions, NumberObjects)
71 for j := 1 to NumberObjects by 1
72 select_obj (SelectedRegions, ObjectSelected, j)
73 get_features (ObjectSelected, WindowHandle, Circularity, Area, RowRegionCenter, ColumnRegionCenter)
74 FeaturesArea := [FeaturesArea,Area]
75 FeaturesCircularity := [FeaturesCircularity,Circularity]
76 FeatureVector := real([Circularity,Area])
77 classify_class_gmm (GMMHandle, FeatureVector, 1, ClassID, ClassProb, Density, KSigmaProb)
78 disp_message (WindowHandle, ‘Class: ‘ + ClassName[ClassID], ‘window‘, RowRegionCenter, ColumnRegionCenter - 100, ‘black‘, ‘true‘)
79 disp_message (WindowHandle, ‘KSigmaProb: ‘ + KSigmaProb, ‘window‘, RowRegionCenter + 30, ColumnRegionCenter - 100, ‘black‘, ‘true‘)
80 endfor
81 if (i != 15)
82 disp_continue_message (WindowHandle, ‘black‘, ‘true‘)
83 endif
84 stop ()
85 endfor
86 * 
87 * Clear the classifier from memory
88 clear_class_gmm (GMMHandle)