Compute metrics when testing

end-to-end/data/dataset_deeptempest_finetuning.py

@@ -41,7 +41,7 @@ class DatasetDrunetFineTune(data.Dataset):
         contains one or more L representations of the H image.
         """
 
-        assert os.path.isdir(opt['dataroot_H']), "No es dir"
+        assert os.path.isdir(opt['dataroot_H']), f"{opt['dataroot_H']} is not a directory"
         self.paths_H = [f for f in os.listdir(opt['dataroot_H']) if os.path.isfile(os.path.join(opt['dataroot_H'],f))]
         #------------------------------------------------------------------------------------------------------
         # For the above step you can use util.get_image_paths(), but it goes recursevely throught the tree dirs

end-to-end/main_test_drunet.py

@@ -18,6 +18,27 @@ from utils.utils_dist import get_dist_info, init_dist
 from data.select_dataset import define_Dataset
 from models.select_model import define_Model
 
+# OCR metrics
+# First, must install Tesseract: https://tesseract-ocr.github.io/tessdoc/Installation.html
+# Second, install CER/WER and tesseract python wrapper libraries
+# pip install fastwer
+# pip install pybind11
+# pip install pytesseract 
+import pytesseract
+import fastwer
+
+
+def calculate_cer_wer(img_E, img_H):
+    # Transcribe ground-truth image to text
+    text_H = pytesseract.image_to_string(img_H).strip().replace('\n',' ')
+
+    # Transcribe estimated image to text
+    text_E = pytesseract.image_to_string(img_E).strip().replace('\n',' ')
+
+    cer = fastwer.score_sent(text_E, text_H, char_level=True)
+    wer = fastwer.score_sent(text_E, text_H)
+
+    return cer, wer
 
 '''
 # --------------------------------------------
@@ -96,6 +117,7 @@ def main(json_path='options/test_drunet.json'):
     # ----------------------------------------
     """
     L_paths = util.get_image_paths(opt['datasets']['test']['dataroot_L'])
+    H_paths = util.get_image_paths(opt['datasets']['test']['dataroot_H'])
     noise_sigma = opt['datasets']['test']['sigma_test']
 
     '''
@@ -103,12 +125,15 @@ def main(json_path='options/test_drunet.json'):
     # Step--4 (main test)
     # ----------------------------------------
     '''
-    # avg_psnr = 0.0
-    # avg_ssim = 0.0
-    # idx = 0
+    avg_psnr = 0.0
+    avg_ssim = 0.0
+    avg_edgeJaccard = 0.0
+    avg_cer = 0.0
+    avg_wer = 0.0
+    idx = 0
 
-    for L_path in L_paths:
-        # idx += 1
+    for L_path, H_path in zip(L_paths,H_paths):
+        idx += 1
         image_name_ext = os.path.basename(L_path)
         img_name, ext = os.path.splitext(image_name_ext)
 
@@ -118,7 +143,7 @@ def main(json_path='options/test_drunet.json'):
         logger.info('Creating inference on test image...')
 
         # Load image
-        img_L_original = util.imread_uint(L_path, n_channels=3)
+        img_L_original = util.imread_uint(L_path, n_channels=3)[50:-50,100:-100,:]
         img_L = img_L_original[:,:,:2]
         img_L = util.uint2single(img_L)
         img_L = util.single2tensor4(img_L)
@@ -150,26 +175,36 @@ def main(json_path='options/test_drunet.json'):
 
         logger.info(f'Inference of {img_name} completed. Saved at {img_dir}.')
 
-        # -----------------------
-        # calculate PSNR
-        # -----------------------
-        # current_psnr = util.calculate_psnr(E_img, H_img)
+        # Load H image and compute metrics
+        img_H = util.imread_uint(H_path, n_channels=3)
+        if img_H.ndim == 3:
+            img_H = np.mean(img_H, axis=2)
+            img_H = img_H.astype('uint8')
 
-        # -----------------------
-        # calculate SSIM
-        # -----------------------
-        # current_ssim = util.calculate_ssim(E_img, H_img)
+        # ----------------------------------------
+        # compute PSNR, SSIM, edgeJaccard and CER
+        # ----------------------------------------
+        current_psnr = util.calculate_psnr(img_E, img_H)
+        current_ssim = util.calculate_ssim(img_E, img_H)
+        current_edgeJaccard = util.calculate_edge_jaccard(img_E, img_H)
+        current_cer, current_wer = calculate_cer_wer(img_E, img_H)
 
-        # logger.info('{:->4d}--> {:>10s} | PSNR = {:<4.2f}dB, SSIM = {:<4.2f}'.format(idx, image_name_ext, current_psnr, current_ssim))
+        logger.info('{:->4d}--> {:>10s} | PSNR = {:<4.2f}dB ; SSIM = {:.3f} ; edgeJaccard = {:.3f} ; CER = {:.3f}% ; WER = {:.3f}%'.format(idx, image_name_ext, current_psnr, current_ssim, current_edgeJaccard, current_cer, current_wer))
 
-        # avg_psnr += current_psnr
-        # avg_ssim += current_ssim
+        avg_psnr += current_psnr
+        avg_ssim += current_ssim
+        avg_edgeJaccard += current_edgeJaccard
+        avg_cer += current_cer
+        avg_wer += current_wer
 
-    # avg_psnr = avg_psnr / idx
-    # avg_ssim = avg_ssim / idx
+    avg_psnr = avg_psnr / idx
+    avg_ssim = avg_ssim / idx
+    avg_edgeJaccard = avg_edgeJaccard / idx
+    avg_cer = avg_cer / idx
+    avg_wer = avg_wer / idx
 
-    # testing log
-    # logger.info('Average PSNR : {:<.2f}dB, Average SSIM : {:<4.2f}\n'.format(avg_psnr, avg_ssim))
+    # Average log
+    logger.info('[Average metrics] PSNR : {:<4.2f}dB, SSIM = {:.3f} : edgeJaccard = {:.3f} : CER = {:.3f}% : WER = {:.3f}%'.format(avg_psnr, avg_ssim, avg_edgeJaccard, avg_cer, avg_wer))
 
 if __name__ == '__main__':
     main()