testing

app.py

@@ -120,6 +120,7 @@ def getModel(model):
 	print("===================")
 
 	lowest_score = 10000
+	lowest_image = None
 
 	for i in range(len(train_labels)):
 		# print(i)
@@ -148,43 +149,44 @@ def getModel(model):
 				if results['rmse'] < lowest_score:
 
 					lowest_score = results['rmse']
+					lowest_image = to_check
 
-					image1 = np.array(Image.open(file_path + 'real_deal.png'))
-					image2 = np.array(Image.open(file_path + 'generated_image.png'))
-					img1 = torch.from_numpy(image1).float().unsqueeze(0).unsqueeze(0)/255.0
-					img2 = torch.from_numpy(image2).float().unsqueeze(0).unsqueeze(0)/255.0
-					img1 = Variable( img1,	requires_grad=False)
-					img2 = Variable( img2,	requires_grad=True)
-					ssim_score = ssim(img1, img2).item()
+					# image1 = np.array(Image.open(file_path + 'real_deal.png'))
+					# image2 = np.array(Image.open(file_path + 'generated_image.png'))
+					# img1 = torch.from_numpy(image1).float().unsqueeze(0).unsqueeze(0)/255.0
+					# img2 = torch.from_numpy(image2).float().unsqueeze(0).unsqueeze(0)/255.0
+					# img1 = Variable( img1,	requires_grad=False)
+					# img2 = Variable( img2,	requires_grad=True)
+					# ssim_score = ssim(img1, img2).item()
 
-					# sys.exit()
-					# l2 = distance.euclidean(image1, image2)
+					# # sys.exit()
+					# # l2 = distance.euclidean(image1, image2)
 
-					low_score_log += 'rmse score:' + str(lowest_score) + "\n"
-					low_score_log += 'ssim score:' + str(ssim_score) + "\n"
-					low_score_log += 'found when:' + str(round( ((i/len(train_labels)) * 100),2 )) + '%' + "\n"
+					# low_score_log += 'rmse score:' + str(lowest_score) + "\n"
+					# low_score_log += 'ssim score:' + str(ssim_score) + "\n"
+					# low_score_log += 'found when:' + str(round( ((i/len(train_labels)) * 100),2 )) + '%' + "\n"
 
-					low_score_log += "---------\n"
+					# low_score_log += "---------\n"
 					
-					print(lowest_score, ssim_score, str(round( ((i/len(train_labels)) * 100),2 )) + '%')
+					# print(lowest_score, ssim_score, str(round( ((i/len(train_labels)) * 100),2 )) + '%')
 
-					fig = plt.figure(figsize=(1, 1))
-					plt.subplot(1, 1, 0+1)
-					plt.imshow(to_check, cmap='gray')
-					plt.axis('off')
-					plt.savefig(file_path+str(i) + "--" + str(lowest_score) + '---most_close.png')
-					plt.close()
+					# fig = plt.figure(figsize=(1, 1))
+					# plt.subplot(1, 1, 0+1)
+					# plt.imshow(to_check, cmap='gray')
+					# plt.axis('off')
+					# plt.savefig(file_path+str(i) + "--" + str(lowest_score) + '---most_close.png')
+					# plt.close()
 
 								
-	f = open(file_path + "score_log.txt", "w+")
-	f.write(low_score_log)
-	f.close()
+	# f = open(file_path + "score_log.txt", "w+")
+	# f.write(low_score_log)
+	# f.close()
 
 	print("Done!")
 
 
 	############################################ return image that you just generated
-	return image
+	return (image, lowest_image)
 
 
 import gradio as gr