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Update main.py
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‎main.py‎

Lines changed: 112 additions & 81 deletions
Original file line numberDiff line numberDiff line change
@@ -4,37 +4,117 @@
44
import PIL.Image
55
import numpy
66
import numpycnn
7+
import threading
8+
import kivy.clock
79

810
class AppGUI(kivy.uix.boxlayout.BoxLayout):
911
layer_num = 1
12+
def run_cnn_thread(self):
13+
print(self.layer_num%3)
14+
print("Layer num : ", self.layer_num)
15+
self.curr_img = self.cnn_example(numpy_img=self.curr_img, layer=self.layer_num%3)
16+
self.refresh_GUI(layer=self.layer_num%3)
17+
self.ids.lbl_details.text = "Shape of output="+str(self.curr_img.shape)
18+
self.layer_num = self.layer_num + 1
19+
print("Output shape : ", self.curr_img.shape)
1020

1121
def start_cnn(self):
1222
img1 = self.ids.img1#Original Image
1323
im = PIL.Image.open(img1.source).convert("L")
1424
img_arr = numpy.asarray(im, dtype=numpy.uint8)
1525
if self.layer_num%3 == 1:
16-
self.curr_img = self.cnn_example(numpy_img=img_arr, layer=1)
17-
self.layer_num = self.layer_num + 1
18-
self.ids.lbl_details.text = "Layer 1 (2ConvFilters,ReLU,Pool)\n"+"Shape of output="+str(self.curr_img.shape)
19-
print("Layer num : ", self.layer_num)
20-
print("Output shape : ", self.curr_img.shape)
21-
self.ids.btn.text = "Run Second Conv. Layer"
26+
img1 = self.ids.img1#Original Image
27+
im = PIL.Image.open(img1.source).convert("L")
28+
img_arr = numpy.asarray(im, dtype=numpy.uint8)
29+
self.curr_img = img_arr
30+
threading.Thread(target=self.run_cnn_thread).start()
31+
self.ids.btn.text = "Wait."
2232
elif self.layer_num%3 == 2:
23-
self.curr_img = self.cnn_example(numpy_img=self.curr_img, layer=2)
24-
self.layer_num = self.layer_num + 1
25-
self.ids.lbl_details.text = "Layer 2 (3ConvFilters,ReLU,Pool)\n"+"Shape of output="+str(self.curr_img.shape)
26-
print("Layer num : ", self.layer_num)
27-
print("Output shape : ", self.curr_img.shape)
28-
self.ids.btn.text = "Run Third Conv. Layer"
33+
threading.Thread(target=self.run_cnn_thread).start()
34+
self.ids.btn.text = "Wait."
2935
elif self.layer_num%3 == 0:
30-
self.curr_img = self.cnn_example(numpy_img=self.curr_img, layer=3)
31-
self.layer_num = self.layer_num + 1
32-
self.ids.lbl_details.text = "Layer 3 (1ConvFilter,ReLU,Pool)\n"+"Shape of output="+str(self.curr_img.shape)
33-
print("Layer num : ", self.layer_num)
34-
print("Output shape : ", self.curr_img.shape)
35-
self.ids.btn.text = "Repeat Again"
36+
threading.Thread(target=self.run_cnn_thread).start()
37+
self.ids.btn.text = "Wait."
3638

3739
def cnn_example(self, numpy_img, layer=1):
40+
if layer == 1:
41+
#**Working with conv layer 1**
42+
l1_filter = numpy.zeros((2,3,3))
43+
l1_filter[0, :, :] = numpy.array([[[-1, 0, 1],
44+
[-1, 0, 1],
45+
[-1, 0, 1]]])
46+
l1_filter[1, :, :] = numpy.array([[[1, 1, 1],
47+
[0, 0, 0],
48+
[-1, -1, -1]]])
49+
50+
l1_feature_map = numpycnn.conv(numpy_img, l1_filter)
51+
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 0]))
52+
im.save("conv1_filter1.png")
53+
54+
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 1]))
55+
im.save("conv1_filter2.png")
56+
57+
l1_feature_map_relu = numpycnn.relu(l1_feature_map)
58+
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 0]))
59+
im.save("conv1_relu1.png")
60+
61+
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 1]))
62+
im.save("conv1_relu2.png")
63+
64+
l1_feature_map_relu_pool = numpycnn.pooling(l1_feature_map_relu, 2, 2)
65+
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map_relu_pool[:, :, 0]))
66+
im.save("conv1_relu_pool1.png")
67+
68+
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map_relu_pool[:, :, 1]))
69+
im.save("conv1_relu_pool2.png")
70+
71+
return l1_feature_map_relu_pool
72+
73+
elif layer == 2:
74+
#**Working with conv layer 2**
75+
l2_filter = numpy.random.rand(3, 5, 5, numpy_img.shape[-1])
76+
l2_feature_map = numpycnn.conv(numpy_img, l2_filter)
77+
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map[:, :, 0]))
78+
im.save("conv2_filter1.png")
79+
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map[:, :, 1]))
80+
im.save("conv2_filter2.png")
81+
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map[:, :, 2]))
82+
im.save("conv2_filter3.png")
83+
84+
l2_feature_map_relu = numpycnn.relu(l2_feature_map)
85+
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu[:, :, 0]))
86+
im.save("conv2_relu1.png")
87+
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu[:, :, 1]))
88+
im.save("conv2_relu2.png")
89+
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu[:, :, 2]))
90+
im.save("conv2_relu3.png")
91+
92+
l2_feature_map_relu_pool = numpycnn.pooling(l2_feature_map_relu, 2, 2)
93+
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu_pool[:, :, 0]))
94+
im.save("conv2_relu_pool1.png")
95+
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu_pool[:, :, 1]))
96+
im.save("conv2_relu_pool2.png")
97+
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu_pool[:, :, 2]))
98+
im.save("conv2_relu_pool3.png")
99+
return l2_feature_map_relu_pool
100+
101+
elif layer == 0:#0 corresponds to layer 3
102+
#**Working with conv layer 3**
103+
l3_filter = numpy.random.rand(1, 7, 7, numpy_img.shape[-1])
104+
l3_feature_map = numpycnn.conv(numpy_img, l3_filter)
105+
im = PIL.Image.fromarray(numpy.uint8(l3_feature_map[:, :, 0]))
106+
im.save("conv3_filter1.png")
107+
l3_feature_map_relu = numpycnn.relu(l3_feature_map)
108+
im = PIL.Image.fromarray(numpy.uint8(l3_feature_map_relu[:, :, 0]))
109+
im.save("conv3_relu1.png")
110+
l3_feature_map_relu_pool = numpycnn.pooling(l3_feature_map_relu, 2, 2)
111+
im = PIL.Image.fromarray(numpy.uint8(l3_feature_map_relu_pool[:, :, 0]))
112+
im.save("conv3_relu_pool1.png")
113+
114+
return l3_feature_map_relu_pool
115+
116+
@kivy.clock.mainthread
117+
def refresh_GUI(self, layer=1):
38118
img1 = self.ids.img1
39119
img2 = self.ids.img2
40120
img3 = self.ids.img3
@@ -56,133 +136,84 @@ def cnn_example(self, numpy_img, layer=1):
56136
lbl9 = self.ids.lbl9
57137

58138
if layer == 1:
59-
#**Working with conv layer 1**
60-
l1_filter = numpy.zeros((2,3,3))
61-
l1_filter[0, :, :] = numpy.array([[[-1, 0, 1],
62-
[-1, 0, 1],
63-
[-1, 0, 1]]])
64-
l1_filter[1, :, :] = numpy.array([[[1, 1, 1],
65-
[0, 0, 0],
66-
[-1, -1, -1]]])
67-
68-
l1_feature_map = numpycnn.conv(numpy_img, l1_filter)
69-
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 0]))
70-
im.save("conv1_filter1.png")
71139
lbl2.text = "L1Map1"
72140
img2.source = "conv1_filter1.png"
73141
img2.reload()
74142

75-
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 1]))
76-
im.save("conv1_filter2.png")
77143
lbl3.text = "L1Map2"
78144
img3.source = "conv1_filter2.png"
79145
img3.reload()
80146

81-
l1_feature_map_relu = numpycnn.relu(l1_feature_map)
82-
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 0]))
83-
im.save("conv1_relu1.png")
84147
lbl4.text = "L1Map1ReLU"
85148
img4.source = "conv1_relu1.png"
86149
img4.reload()
87150

88-
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 1]))
89-
im.save("conv1_relu2.png")
90151
lbl5.text = "L1Map2ReLU"
91152
img5.source = "conv1_relu2.png"
92-
img5.reload()
93-
94-
l1_feature_map_relu_pool = numpycnn.pooling(l1_feature_map_relu, 2, 2)
95-
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map_relu_pool[:, :, 0]))
96-
im.save("conv1_relu_pool1.png")
153+
img5.reload()
154+
97155
lbl6.text = "L1Map1ReLUPool"
98156
img6.source = "conv1_relu_pool1.png"
99157
img6.reload()
100158

101-
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map_relu_pool[:, :, 1]))
102-
im.save("conv1_relu_pool2.png")
103159
lbl7.text = "L1Map2ReLUPool"
104160
img7.source = "conv1_relu_pool2.png"
105161
img7.reload()
106162

107-
return l1_feature_map_relu_pool
163+
self.ids.btn.text = "Run Second Conv. Layer"
108164

109165
elif layer == 2:
110-
#**Working with conv layer 2**
111-
l2_filter = numpy.random.rand(3, 5, 5, numpy_img.shape[-1])
112-
l2_feature_map = numpycnn.conv(numpy_img, l2_filter)
113-
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map[:, :, 0]))
114-
im.save("conv2_filter1.png")
115166
lbl1.text = "L2Map1"
116167
img1.source = "conv2_filter1.png"
117168
img1.reload()
118-
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map[:, :, 1]))
119-
im.save("conv2_filter2.png")
169+
120170
lbl2.text = "L2Map2"
121171
img2.source = "conv2_filter2.png"
122172
img2.reload()
123-
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map[:, :, 2]))
124-
im.save("conv2_filter3.png")
173+
125174
lbl3.text = "L2Map3"
126175
img3.source = "conv2_filter3.png"
127176
img3.reload()
128177

129-
l2_feature_map_relu = numpycnn.relu(l2_feature_map)
130-
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu[:, :, 0]))
131-
im.save("conv2_relu1.png")
132178
lbl4.text = "L2Map1ReLU"
133179
img4.source = "conv2_relu1.png"
134180
img4.reload()
135-
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu[:, :, 1]))
136-
im.save("conv2_relu2.png")
181+
137182
lbl5.text = "L2Map3ReLU"
138183
img5.source = "conv2_relu2.png"
139184
img5.reload()
140-
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu[:, :, 2]))
141-
im.save("conv2_relu3.png")
185+
142186
lbl6.text = "L2Map3ReLU"
143187
img6.source = "conv2_relu3.png"
144188
img6.reload()
145189

146-
l2_feature_map_relu_pool = numpycnn.pooling(l2_feature_map_relu, 2, 2)
147-
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu_pool[:, :, 0]))
148-
im.save("conv2_relu_pool1.png")
149190
lbl7.text = "L2Map1ReLU"
150191
img7.source = "conv2_relu_pool1.png"
151192
img7.reload()
152-
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu_pool[:, :, 1]))
153-
im.save("conv2_relu_pool2.png")
193+
154194
lbl8.text = "L2Map2ReLU"
155195
img8.source = "conv2_relu_pool2.png"
156196
img8.reload()
157-
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu_pool[:, :, 2]))
158-
im.save("conv2_relu_pool3.png")
197+
159198
lbl9.text = "L2Map3ReLU"
160199
img9.source = "conv2_relu_pool3.png"
161200
img9.reload()
162-
return l2_feature_map_relu_pool
163201

164-
elif layer == 3:
202+
self.ids.btn.text = "Run Third Conv. Layer"
203+
204+
elif layer == 0:#0 corresponds to layer 3
165205
lbl1.text = "Original"
166206
img1.source = "input_image.jpg"
167207
img1.reload()
168208

169209
#**Working with conv layer 3**
170-
l3_filter = numpy.random.rand(1, 7, 7, numpy_img.shape[-1])
171-
l3_feature_map = numpycnn.conv(numpy_img, l3_filter)
172-
im = PIL.Image.fromarray(numpy.uint8(l3_feature_map[:, :, 0]))
173-
im.save("conv3_filter1.png")
174210
lbl2.text = "L3Map1"
175211
img2.source = "conv3_filter1.png"
176212
img2.reload()
177-
l3_feature_map_relu = numpycnn.relu(l3_feature_map)
178-
im = PIL.Image.fromarray(numpy.uint8(l3_feature_map_relu[:, :, 0]))
179-
im.save("conv3_relu1.png")
213+
180214
lbl3.text = "L3Map1ReLU"
181215
img3.source = "conv3_relu1.png"
182216
img3.reload()
183-
l3_feature_map_relu_pool = numpycnn.pooling(l3_feature_map_relu, 2, 2)
184-
im = PIL.Image.fromarray(numpy.uint8(l3_feature_map_relu_pool[:, :, 0]))
185-
im.save("conv3_relu_pool1.png")
186217
lbl4.text = "L3Map1ReLUPool"
187218
img4.source = "conv3_relu_pool1.png"
188219
img4.reload()
@@ -204,7 +235,7 @@ def cnn_example(self, numpy_img, layer=1):
204235
lbl8.text = ""
205236
lbl9.text = ""
206237

207-
return l3_feature_map_relu_pool
238+
self.ids.btn.text = "Repeat Again. Run First Conv. Layer"
208239

209240
class NumPyCNNApp(kivy.app.App):
210241
pass

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