eval

Author: musimab

extract_words.py

@@ -121,7 +121,7 @@ def easyOcr(self, img, bbox):
             result = self.reader.readtext(img)
             if(len(result)):
                 box, text, prob = result[0]
-                self.IdCardInfo.EasyOcrInfo[info] = text
+                self.IdCardInfo.EasyOcrInfo[info] = text.upper()
 
         self.IdCardInfo.EasyOcrInfo["DateofBirth"] = self.getonlyDigits(self.IdCardInfo.EasyOcrInfo["DateofBirth"])
 

main.py

@@ -98,7 +98,7 @@ def getCenterOfMasks(thresh):
         #indx = indx + 1
         #if(indx == 4):
         #    break
-    print("len of detected centers:", len(detected_centers))
+    #print("len of detected centers:", len(detected_centers))
     #plt.imshow(mask, cmap='gray')
     #plt.show()
     return np.array(detected_centers)
@@ -187,10 +187,10 @@ def getBoxRegions(regions):
         ocrResult = Image2Text(ocr_method="Easy", lw_thresh = 5, up_thresh= 5, denoising=False, file_name=filename)
 
         PersonInfo = ocrResult.ocrOutput(final_img, new_bboxes)
-        
+        print(" ")
         for id, val in PersonInfo.items():
             print(id,':' ,val)
-        
+        print(" ")
         #utlis.displayMachedBoxes(final_img, new_bboxes)
 
         #utlis.displayAllBoxes(final_img, bbox_coordinates)

test/evaluate.py

@@ -71,15 +71,16 @@ def comparisionInCharacterLevel(dict_true, dict_predict):
             for truth, pred in zip(person_info_t["DateofBirth"] , person_info_p["DateofBirth"]):
                 date_result.append(truth == pred )
 
-    
+    print(" ")
+    print("#####  Character Level Comparision  #### ")
     print("tc:", sum(tc_result),"/" ,len(tc_result), "%", 100.0 * sum(tc_result)/len(tc_result))
 
     print("surname:", sum(surname_result),"/", len(surname_result), "%", 100.0 * sum(surname_result)/len(surname_result))
 
     print("name:", sum(name_result),"/", len(name_result), "%", 100 * sum(name_result)/len(name_result) )
 
     print("dateofbirth:", sum(date_result),"/", len(date_result), "%", 100 * sum(date_result)/len(date_result))
-
+    print(" ")
 
 
 def comparisionInWordLevel(dict_true, dict_predict):
@@ -99,9 +100,14 @@ def comparisionInWordLevel(dict_true, dict_predict):
             surname_result.append(person_info_t["Surname"] == person_info_p["Surname"])
             name_result.append(person_info_t["Name"] == person_info_p["Name"])
             date_result.append(person_info_t["DateofBirth"] == person_info_p["DateofBirth"])
-
-  
-    return tc_result, surname_result, name_result, date_result
+    
+    print(" ")
+    print("#####  Word Level Comparision   #### ")
+    print("tc result:",  sum(tc_result), "/", len(tc_result), "%", 100*sum(tc_result)/len(tc_result))
+    print("surname result:", sum(surname_result), "/", len(surname_result), "%", 100*sum(surname_result)/len(surname_result))
+    print("name result:", sum(name_result), "/", len(name_result) ,"%", 100*sum(name_result)/ len(name_result))
+    print("date_result:", sum(date_result), "/", len(date_result), "%", 100*sum(date_result)/len(date_result))
+    print(" ")
 
 
 
@@ -115,20 +121,10 @@ def comparisionInWordLevel(dict_true, dict_predict):
     print("####  Count true items   ####")
     countDictionaryItems(wordsListTrue)
 
-    tc_result, surname_result, name_result, date_result = comparisionInWordLevel(wordsListTrue , wordsListPred)
-    
-    print("#####  Word Level Comparision   #### ")
-
-    print("tc result:", sum(tc_result)/len(tc_result))
-    print("surname result:", sum(surname_result)/len(surname_result))
-    print("name result:", sum(name_result)/ len(name_result))
-    print("date_result:", sum(date_result)/len(date_result))
-
-
-    print("#####  Character Level Comparision  #### ")
+    comparisionInWordLevel(wordsListTrue , wordsListPred)
 
     comparisionInCharacterLevel(wordsListTrue , wordsListPred)
-
+    
 
 
 

utlis.py

@@ -132,9 +132,9 @@ def correctPerspective(img):
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
     imgBlur = cv2.GaussianBlur(gray, (5,5), 1)
     imgCanny = cv2.Canny(imgBlur,80,80)
-    ret, thresh  = cv2.threshold(imgCanny , 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    ret, thresh  = cv2.threshold(imgBlur , 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
 
-    kernel = np.ones((5,5), np.uint8)
+    kernel = np.ones((3,3), np.uint8)
     img_dilation = cv2.dilate( thresh, kernel, iterations=1)
     img_erosion = cv2.erode(img_dilation , kernel, iterations=1)