No ACL setup yet! Denying access to everyone.
en:edu:zmitac:ai:projects:idrecognition
Differences
This shows you the differences between two versions of the page.
| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| en:edu:zmitac:ai:projects:idrecognition [2018/04/13 23:06] – [2.1 Start Training] maribaj746 | en:edu:zmitac:ai:projects:idrecognition [2018/04/13 23:20] (current) – [2.3 Video MRZ Recognition] maribaj746 | ||
|---|---|---|---|
| Line 40: | Line 40: | ||
| </ | </ | ||
| ====2.2 Verify Engine ==== | ====2.2 Verify Engine ==== | ||
| - | Testowanie i weryfikacja „nauczenia Sieci | + | Testing and verification " |
| ^Step ^Description ^ | ^Step ^Description ^ | ||
| - | | 1 |Inicjujemy Silnik sieci oraz ładujemy dane uczące: <code python> | + | | 1 |We initialize engine and load data to learning: <code python> |
| self.samples = np.loadtxt(" | self.samples = np.loadtxt(" | ||
| self.responses = np.loadtxt(" | self.responses = np.loadtxt(" | ||
| Line 48: | Line 48: | ||
| self.model.train(self.samples, | self.model.train(self.samples, | ||
| </ | </ | ||
| - | | 2 |Funkcja lądujemy obrazek z danymi testowymi. {{ : | + | | 2 |Function load picture with test data. {{ : |
| - | | 3 |Nakładamy niezbędne filtry | + | | 3 |We put necessary filters |
| - | | 4 |Na podstawie konturów extraktujemy pojedyncze linie (uwaga sprawdzamy czy są rozpoznane 3 linie (obligatoryjnie dowód posiada | + | | 4 |Based on contours we extract single lines. We have to check that our application recognize |
| ef extract_lines(self, | ef extract_lines(self, | ||
| line_list = {} | line_list = {} | ||
| Line 73: | Line 73: | ||
| return None | return None | ||
| </ | </ | ||
| - | | 5 |Wycinamy obrazki | + | | 5 |We cut and extract pictures of each lines based on parameters from extract_lines |
| def decode(self, | def decode(self, | ||
| if len(image) > 0 and image is not None: | if len(image) > 0 and image is not None: | ||
| Line 88: | Line 88: | ||
| return None | return None | ||
| </ | </ | ||
| - | | 6 |Funkcja | + | | 6 |OCR function extract character from lines using extract_chars_from_line |
| def extract_chars_from_line(self, | def extract_chars_from_line(self, | ||
| image, contours, _ = cv2.findContours(image, | image, contours, _ = cv2.findContours(image, | ||
| Line 100: | Line 100: | ||
| lista_rect[x] = [x, y, w, h] | lista_rect[x] = [x, y, w, h] | ||
| return sorted(lista_rect.items()) | return sorted(lista_rect.items()) | ||
| - | </ | + | </ |
| def validate(self, | def validate(self, | ||
| print "Read Data: %s" % read_data | print "Read Data: %s" % read_data | ||
| Line 126: | Line 126: | ||
| return Accuracy | return Accuracy | ||
| </ | </ | ||
| - | | 7 |Zwrócony String w postaci tablicy | + | | 7 |Returned string in form of 3 lines array we compare with our prepared pattern. Counting all positive and negaive recognition we calculate accuracy of our engine.<code python> |
| def validate(self, | def validate(self, | ||
| print "Read Data: %s" % read_data | print "Read Data: %s" % read_data | ||
| Line 153: | Line 153: | ||
| </ | </ | ||
| ====2.3 Photo MRZ Recognition ==== | ====2.3 Photo MRZ Recognition ==== | ||
| - | Zadaniem tego Modul jest zdekodowanie strefy | + | This module is responsible for decoding |
| - | Algorytm działania: | + | |
| ^Step ^Description ^ | ^Step ^Description ^ | ||
| - | | 1 | Program Włącza kamerę i wyświetla obraz, użytkownik robi zdjęcie naciskając klawisz „SPACE” , klawisz „q” zamyka | + | | 1 | Application turn on camera and shows image, user are taking picture using ' |
| def capture_process(self, | def capture_process(self, | ||
| cap = cv2.VideoCapture(cam) | cap = cv2.VideoCapture(cam) | ||
| Line 172: | Line 171: | ||
| break | break | ||
| </ | </ | ||
| - | | 2 |Po zrobieniu zdjecia na obraz nakładane są niezbędne filtry a następnie obraz jest przekazany do funkcji oryginalny obraz oraz obraz z nałożonymi filtrami jest przekazany do funkcji locate_MRZ która | + | | 2 |When picture are taken necessary filters are put and picture is passing |
| def filtr(self, image): | def filtr(self, image): | ||
| rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, | rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, | ||
| Line 203: | Line 202: | ||
| thresh[:, image.shape[1] - p:] = 0 | thresh[:, image.shape[1] - p:] = 0 | ||
| return thresh | return thresh | ||
| - | </ | + | </ |
| def locate_MRZ(self, | def locate_MRZ(self, | ||
| _oryginal = cv2.adaptiveThreshold(_oryginal, | _oryginal = cv2.adaptiveThreshold(_oryginal, | ||
| Line 218: | Line 217: | ||
| result = _oryginal[y: | result = _oryginal[y: | ||
| return self.decode(result) | return self.decode(result) | ||
| - | </ | + | </ |
| - | | 3 | Takie zdjęcie przekazane jest do funkcji | + | | 3 | This picture are passing to Decode |
| def decode(self, | def decode(self, | ||
| if len(image) > 0 and image is not None: | if len(image) > 0 and image is not None: | ||
| Line 234: | Line 233: | ||
| return None | return None | ||
| </ | </ | ||
| - | | 4 |Funkcja | + | | 4 |OCR function extract characters of every line using extract_chars_from_line |
| def extract_chars_from_line(self, | def extract_chars_from_line(self, | ||
| image, contours, _ = cv2.findContours(image, | image, contours, _ = cv2.findContours(image, | ||
| Line 246: | Line 245: | ||
| lista_rect[x] = [x, y, w, h] | lista_rect[x] = [x, y, w, h] | ||
| return sorted(lista_rect.items()) | return sorted(lista_rect.items()) | ||
| - | </ | + | </ |
| self.clear_folder(' | self.clear_folder(' | ||
| result = "" | result = "" | ||
| Line 267: | Line 266: | ||
| return result | return result | ||
| </ | </ | ||
| - | | 5 | Wynik jest zwracany na konsolę w formie tekstowej.| | + | | 5 | Result are printed to console.| |
| ====2.3 Video MRZ Recognition ==== | ====2.3 Video MRZ Recognition ==== | ||
| - | Zadaniem tego Modul jest zdekodowanie strefy | + | The task of this module is decoding |
| {{ : | {{ : | ||
en/edu/zmitac/ai/projects/idrecognition.1523653576.txt.gz · Last modified: by maribaj746