[ADDED] Separated test, evaluation and metrics codes

evaluation.py

@@ -0,0 +1,83 @@
+import cv2
+import os
+from metrics import lpips, PSNR, ssim
+import time
+from tqdm import tqdm
+import pandas as pd
+
+def imwrite(results_folder, name ,img):
+    cv2.imwrite(results_folder +'/' +name,img)
+
+def imread(data_folder, name):
+    img_rgb = cv2.imread(data_folder + '/' + name)
+    return img_rgb
+
+def prepare_results_folder(order, block_shape,  method):
+    results_folder = 'results_' + f'order_{order:.3f}_'+f'blocks_{block_shape}'
+
+    if not os.path.exists(results_folder):
+        os.mkdir(results_folder)
+
+    method_folder = results_folder+'/'+method
+    if not os.path.exists(method_folder):
+        os.mkdir(method_folder)
+
+    return method_folder
+
+def average_l(lst):
+    return sum(lst) / len(lst)
+
+
+def test_for_parameters(compressor, data_folder, results_method_folder):
+
+	img_list = os.listdir( data_folder )
+	ssim_list = []
+	psnr_list = []
+	lpips_list = []
+	time_encode = []
+	time_decode = []
+
+	for imname in tqdm(img_list):
+
+		img_rgb = imread(data_folder, imname)
+		start_encode = time.time()
+		img_rgb_code = compressor.encode_rgb(img_rgb)
+		end_encode = time.time()
+
+		start_decode = time.time()
+		img_rgb_compress = compressor.decode_rgb(img_rgb_code)
+		end_decode = time.time()
+
+		imwrite(results_method_folder, imname, img_rgb_compress)
+
+		ssim_val = ssim(img_rgb, img_rgb_compress)
+		psnr_val = PSNR(img_rgb, img_rgb_compress)
+		lpips_val = lpips(img_rgb, img_rgb_compress)
+
+		time_encode_val = end_encode - start_encode
+		time_decode_val = end_decode - start_decode
+
+
+		ssim_list.append(ssim_val)
+		psnr_list.append(psnr_val)
+		lpips_list.append(lpips_val)
+		time_encode.append(time_encode_val)
+		time_decode.append(time_decode_val)
+
+
+	ssim_avg = average_l(ssim_list)
+	psnr_avg = average_l(psnr_list)
+	lpips_avg = average_l(lpips_list)
+	time_encode_avg = average_l(time_encode)
+	time_decode_avg = average_l(time_decode)
+
+
+	df = pd.DataFrame({'image_name': (img_list+['mean']),
+					'ssim': ssim_list + [ssim_avg],
+					'psnr': psnr_list + [psnr_avg],
+					'lpips': lpips_list + [lpips_avg],
+					'time_encode':time_encode + [time_encode_avg],
+					'time_decode':time_decode + [time_decode_avg]})
+
+	csv_path = results_method_folder +'/results_num.csv'
+	df.to_csv(csv_path)  

utils.py → metrics.py

@@ -1,12 +1,7 @@
-import cv2
 import numpy as np
-import os
 import scipy
 import torch
 
-def average_l(lst):
-    return sum(lst) / len(lst)
-
 def rgb_ssim(img0, img1, max_val,
              filter_size=11,
              filter_sigma=1.5,
@@ -75,25 +70,3 @@ def rgb_lpips(np_gt, np_im, net_name, device):
 ssim = lambda x,y :rgb_ssim(x/255, y/255, max_val=1)
 lpips = lambda x,y :rgb_lpips(np.float32(x/255), np.float32(y/255), net_name='vgg', device=torch.device("cpu"))
 
-
-def imwrite(results_folder,method, name ,img):
-    cv2.imwrite(results_folder + '/' + method + '/' +name,img)
-
-def imread(data_folder, name):
-    img_rgb = cv2.imread(data_folder + '/' + name)
-    return img_rgb
-
-def prepare_results_folder(order, block_shape, all_methods):
-    results_folder = 'results_' + f'order_{order:.3f}_'+f'blocks_{block_shape}'
-
-    if not os.path.exists(results_folder):
-        os.mkdir(results_folder)
-
-    for method in all_methods:
-        method_folder = results_folder+'/'+method
-        if not os.path.exists(method_folder):
-            os.mkdir(method_folder)
-
-    return results_folder
-
-

test.py

@@ -1,10 +1,5 @@
-import os
-
-from compression_transforms import compression_factory
-from utils import imwrite, imread, prepare_results_folder, PSNR, ssim, lpips, average_l
-import pandas as pd
-import time
-from tqdm import tqdm
+from compression import compression_factory
+from evaluation import test_for_parameters, prepare_results_folder
 
 
 orders = [0.20, 0.30,  0.50]
@@ -13,67 +8,12 @@
 data_folder = 'datasets/kodak/'
 
 
-def test_for_parameters(compressor, data_folder):
-
-	results_folder = prepare_results_folder(compressor.order, compressor.block_size, all_methods)
-
-	img_list = os.listdir( data_folder )
-	ssim_list = []
-	psnr_list = []
-	lpips_list = []
-	time_encode = []
-	time_decode = []
-
-	for imname in tqdm(img_list):
-
-		img_rgb = imread(data_folder, imname)
-		start_encode = time.time()
-		img_rgb_code = compressor.encode_rgb(img_rgb)
-		end_encode = time.time()
-
-		start_decode = time.time()
-		img_rgb_compress = compressor.decode_rgb(img_rgb_code)
-		end_decode = time.time()
-
-		imwrite(results_folder, method, imname, img_rgb_compress)
-
-		ssim_val = ssim(img_rgb, img_rgb_compress)
-		psnr_val = PSNR(img_rgb, img_rgb_compress)
-		lpips_val = lpips(img_rgb, img_rgb_compress)
-
-		time_encode_val = end_encode - start_encode
-		time_decode_val = end_decode - start_decode
-
-
-		ssim_list.append(ssim_val)
-		psnr_list.append(psnr_val)
-		lpips_list.append(lpips_val)
-		time_encode.append(time_encode_val)
-		time_decode.append(time_decode_val)
-
-
-	ssim_avg = average_l(ssim_list)
-	psnr_avg = average_l(psnr_list)
-	lpips_avg = average_l(lpips_list)
-	time_encode_avg = average_l(time_encode)
-	time_decode_avg = average_l(time_decode)
-
-
-	df = pd.DataFrame({'image_name': (img_list+['mean']),
-					'ssim': ssim_list + [ssim_avg],
-					'psnr': psnr_list + [psnr_avg],
-					'lpips': lpips_list + [lpips_avg],
-					'time_encode':time_encode + [time_encode_avg],
-					'time_decode':time_decode + [time_decode_avg]})
-
-	csv_path = results_folder + '/' + method +'/results_num.csv'
-	df.to_csv(csv_path)  
-
 for block in block_shapes:
 	for order in orders:
 		for method in all_methods:
 
+			results_folder = prepare_results_folder(order, block, method)
 			compressor = compression_factory(method, order, block)
 			print(f'Order: {order}, method: {method}, block: {block}')
-			test_for_parameters(compressor, data_folder)
+			test_for_parameters(compressor, data_folder, results_folder)