--- a
+++ b/DATA_PROCESS/Results_Plot.py
@@ -0,0 +1,228 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pylab
+import joblib
+import cPickle as cp
+# t = np.arange(0, 5, 0.2)
+# t2 = np.arange(0, 5, 0.02)
+
+# def f(t):
+# return np.exp(-t)*np.cos(2*np.pi*t)
+def dice_np(y_true, y_pred):
+    y_true = y_true.reshape(y_true.shape[0], -1)
+    y_pred = y_pred.reshape(y_pred.shape[0], -1)
+
+    #y_true = np.reshape(y_true, -1)
+    #y_pred = np.reshape(y_pred, -1)
+    # y_true = y_true/np.max(np.max(y_true))
+    # y_pred = y_pred/np.max(np.max(y_pred))
+
+    #y_true[y_true > 0.0] = 1.0
+    #y_pred[y_pred > 0.0] = 1.0
+
+
+    print('Shapes  : ', y_true.shape, y_pred.shape)
+    intersection = y_true*y_pred
+
+    #print('Int shape ', intersection.shape)
+    intersection = np.sum(intersection, axis = 1)
+    #print('Int shape new ', intersection.shape)
+    dr1 = np.sum(y_true, axis=1)
+    dr2 = np.sum(y_pred, axis=1)
+
+    #print('Dr ', dr1, dr2)
+    dr = dr1+dr2
+    nr = 2*intersection
+
+
+    x = nr/dr
+    return np.mean(x)
+
+results = np.load('data.npz')
+
+print(results.files)
+
+print(results['imgs_test_X'].shape, results['imgs_test_Y'].shape, results['imgs_test_Pred'].shape)
+
+# for i in range(0,results['imgs_test_X'].shape[0]):
+# 	plt.figure(1)
+# 	#plt.axis('off')
+# 	plt.title('%d"'%(i))
+# 	plt.subplot(231)
+# 	plt.imshow(results['imgs_test_X'][i][0])
+# 	plt.subplot(232)
+# 	plt.imshow(results['imgs_test_X'][i][0]*results['imgs_test_Y'][i][0], vmin = np.min(results['imgs_test_X'][i][0]), vmax = np.max(results['imgs_test_X'][i][0]))
+# 	plt.subplot(233)
+# 	plt.imshow(results['imgs_test_X'][i][0]*results['imgs_test_Pred'][i][0], vmin = np.min(results['imgs_test_X'][i][0]), vmax = np.max(results['imgs_test_X'][i][0]))
+# 	plt.subplot(235)
+# 	plt.imshow(results['imgs_test_Y'][i][0])
+# 	plt.subplot(236)
+# 	plt.imshow(results['imgs_test_Pred'][i][0])
+# 	pylab.show()
+
+
+#weight = joblib.load(('weights'))
+weight = cp.load(open('filters.pkl'))
+
+#weight.reshape(weight.shape[0], -1)
+
+#print('Weights : ', weight.keys())
+#plt.figure(3)
+#plt.subplot(filters_per_layer)
+
+for key in (weight):
+	if key.startswith('conv_') and key.endswith('_2'):
+		print(key, len(weight[key]))
+		for j in range(0, len(weight[key])):
+				#weight[key].reshape(6)
+			plt.figure(2)
+			plt.subplot(1,2,1)
+			plt.imshow(weight[key][0])
+			plt.axis('off')
+			plt.subplot(1,2,2)
+			plt.imshow(weight[key][1])
+			plt.axis('off')
+			plt.figure(3)
+			plt.subplot(1,2,1)
+			plt.imshow(weight[key][2])
+			plt.axis('off')
+			plt.subplot(1,2,2)
+			plt.imshow(weight[key][3])
+			plt.axis('off')
+		pylab.show()
+
+
+def plot_3d(image, threshold=-300):
+    
+    # Position the scan upright, 
+    # so the head of the patient would be at the top facing the camera
+    p = image.transpose(2,1,0)
+    p = p[:,:,::-1]
+    
+    verts, faces = measure.marching_cubes(p, threshold)
+
+    fig = plt.figure(figsize=(10, 10))
+    ax = fig.add_subplot(111, projection='3d')
+
+    # Fancy indexing: `verts[faces]` to generate a collection of triangles
+    mesh = Poly3DCollection(verts[faces], alpha=0.1)
+    face_color = [0.5, 0.5, 1]
+    mesh.set_facecolor(face_color)
+    ax.add_collection3d(mesh)
+
+    ax.set_xlim(0, p.shape[0])
+    ax.set_ylim(0, p.shape[1])
+    ax.set_zlim(0, p.shape[2])
+
+    plt.show()
+
+
+#plots = np.zeros(results['imgs_test_Y'].shape[0],results['imgs_test_Y'].shape[2],results['imgs_test_Y'].shape[3])
+
+#print(plots.shape)
+
+#for i in range(0, results['imgs_test_Y'].shape[0]):
+
+
+#plot_3d(results['imgs_test_Y'][])
+
+
+####### PLOTTING
+# plot_res = results['imgs_test_X'][0][0][0:200, 300:500]
+# #*results['imgs_test_Y'][0][0][300:500]
+# print('Shape : ', plot_res.shape)
+
+ind = 19
+# plt.figure(1)
+# plt.title('Lung CT Scan')
+# plt.imshow(results['imgs_test_X'][ind][0])
+# plt.axis('off')
+# #pylab.show()
+
+# Im1 = results['imgs_test_Y'][ind][0]
+# Im2 = results['imgs_test_Pred'][ind][0]
+
+# print(dice_np(Im1, Im2))
+
+# plt.figure(2)
+# plt.title('Results')
+# ax1 = plt.subplot(131)
+# plt.imshow(results['imgs_test_X'][ind][0][150:350, 300:500], vmin = np.min(results['imgs_test_X'][ind][0]), vmax = np.max(results['imgs_test_X'][ind][0]))
+# ax1.set_title('Region of Interest')
+# plt.axis('off')
+# ax1 = plt.subplot(132)
+# plt.imshow(results['imgs_test_X'][ind][0][150:350, 300:500]*results['imgs_test_Y'][ind][0][150:350, 300:500], vmin = np.min(results['imgs_test_X'][ind][0]), vmax = np.max(results['imgs_test_X'][ind][0]))
+# ax1.set_title('Gold Standard Mask')
+# plt.axis('off')
+# ax2 = plt.subplot(133)
+# plt.imshow(results['imgs_test_X'][ind][0][150:350, 300:500]*results['imgs_test_Pred'][ind][0][150:350, 300:500], vmin = np.min(results['imgs_test_X'][ind][0]), vmax = np.max(results['imgs_test_X'][ind][0]))
+# ax2.set_title('Predicted Mask')
+# plt.axis('off')
+# pylab.show()
+
+###### PLOTTING END
+
+
+
+
+
+
+# testMasks = np.load('testMasks.npy')
+# trainedMasks = np.load('masksTestPredicted.npy')
+# testIm = np.load('testImages.npy')
+# trainIm = np.load('trainImages.npy')
+# trainMasks = np.load('trainMasks.npy')
+# print(testMasks.shape)
+# print(trainedMasks.shape)
+# print(testIm.shape)
+# print(trainIm.shape)
+# print(trainMasks.shape)
+
+# for i in range(0,testIm.shape[0]):
+# 	plt.figure(1)
+# 	plt.subplot(231)
+# 	plt.imshow(testIm[i][0])
+# 	plt.subplot(232)
+# 	plt.imshow(testMasks[i][0])
+# 	plt.subplot(234)
+# 	plt.imshow(testIm[i][0]*testMasks[i][0])
+# 	plt.subplot(233)
+# 	plt.imshow(trainedMasks[i][0])
+# 	plt.subplot(235)
+# 	plt.imshow(testIm[i][0]*trainedMasks[i][0])
+# 	pylab.show()
+
+# for i in range(0,trainIm.shape[0]):
+# 	plt.figure(1)
+# 	plt.subplot(131)
+# 	plt.imshow(trainIm[i][0])
+# 	plt.subplot(132)
+# 	plt.imshow(trainMasks[i][0])
+# 	plt.subplot(133)
+# 	plt.imshow(trainMasks[i][0]*trainIm[i][0])
+# 	pylab.show()
+
+# print(np.sum(trainIm[0][0]), np.sum(trainIm[1][0]))
+# for i in range(0,trainIm.shape[0]):
+# 	plt.figure(2)
+# 	plt.subplot(121)
+# 	plt.imshow(trainIm[i][0])
+# 	plt.subplot(122)
+# 	plt.imshow(trainMasks[i][0]*trainIm[i][0])
+# 	pylab.show()
+# for i in range(0,trainedMasks.shape[0]):
+# 	plt.figure(i+1)
+# 	plt.imshow(trainedMasks[i][0])
+# 	pylab.show()
+# for i in range(0,testMasks.shape[0]):
+# 	plt.figure(i+1)
+# 	plt.imshow(testMasks[i][0])
+# 	pylab.show()
+# plt.figure(1)
+# plt.subplot(211)
+# plt.plot(t, f(t), 'bo', t2, f(t2), 'k')
+
+# plt.subplot(212)
+# plt.plot(t2, np.cos(2*np.pi*t2), 'k')
+
+# pylab.show()
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