class PaperDetection:
def __init__(self):
cwd = os.path.dirname(os.path.realpath(__file__))
os.environ["TESSDATA_PREFIX"] = cwd
self.tr = Tesseract(lang="deu")
self.gs = goslate.Goslate()
self.trained_paper = False
self.paper_row_nw = None
self.paper_row_se = None
self.paper_col_nw = None
self.paper_col_se = None
self.paper_hist = None
self.paper = None
self.words = None
self.translations = []
self.pointed_locations = deque(maxlen=20)
def draw_paper_rect(self, frame):
rows, cols, _ = frame.shape
self.paper_row_nw = rows / 5
self.paper_row_se = 4 * rows / 5
self.paper_col_nw = 2 * cols / 5
self.paper_col_se = 3 * cols / 5
cv2.rectangle(
frame, (self.paper_col_nw, self.paper_row_nw), (self.paper_col_se, self.paper_row_se), (0, 255, 0), 1
)
black = np.zeros(frame.shape, dtype=frame.dtype)
frame_final = np.vstack([frame, black])
return frame_final
def train_paper(self, frame):
self.set_paper_hist(frame)
self.trained_paper = True
def get_paper(self, frame):
paper_masked = image_analysis.apply_hist_mask(frame, self.paper_hist)
contours = image_analysis.contours(paper_masked)
max_contour = image_analysis.max_contour(contours)
paper = image_analysis.contour_interior(frame, max_contour)
return paper
def set_paper(self, frame):
self.paper = self.get_paper(frame)
def paper_copy(self):
paper = self.paper.copy()
return paper
def set_ocr_text(self, frame):
paper = self.get_paper(frame)
thresh = image_analysis.gray_threshold(paper, 100)
paper_img = Image.fromarray(thresh)
self.tr.set_image(paper_img)
self.tr.get_text()
self.words = self.tr.get_words()
for w in self.words:
translation = self.translate(w.value)
self.translations.append(translation)
def set_paper_hist(self, frame):
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
roi = hsv[self.paper_row_nw : self.paper_row_se, self.paper_col_nw : self.paper_col_se]
self.paper_hist = cv2.calcHist([roi], [0, 1], None, [180, 256], [0, 180, 0, 256])
cv2.normalize(self.paper_hist, self.paper_hist, 0, 255, cv2.NORM_MINMAX)
def get_word_at_point(self, point):
for i, w in enumerate(self.words):
x_nw, y_nw, x_se, y_sw = w.box
x, y = point
if x > x_nw and x < x_se and y > y_nw and y < y_sw:
return self.translations[i]
def get_word_index(self, point):
for i, w in enumerate(self.words):
x_nw, y_nw, x_se, y_sw = w.box
x, y = point
if x > x_nw and x < x_se and y > y_nw and y < y_sw:
return i
def translate(self, word):
translated_word = self.gs.translate(word, "en", source_language="de")
return translated_word
def update_pointed_locations(self, point):
index = self.get_word_index(point)
if index != None:
self.pointed_locations.append(index)
def get_most_common_word(self):
index = self.most_common_location()
if index != None:
word = self.translations[index].encode("ascii", errors="backslashreplace")
return word
def most_common_location(self):
values = set(self.pointed_locations)
index = None
maxi = 0
for i in values:
num = self.pointed_locations.count(i)
if num > maxi:
index = i
frequency = float(self.pointed_locations.count(index)) / float(self.pointed_locations.maxlen)
if frequency > 0.25:
return index
else:
return None
class PaperDetection:
def __init__(self):
cwd = os.path.dirname(os.path.realpath(__file__))
os.environ['TESSDATA_PREFIX'] = cwd
self.tr = Tesseract(lang='deu')
self.gs = goslate.Goslate()
self.trained_paper = False
self.paper_row_nw = None
self.paper_row_se = None
self.paper_col_nw = None
self.paper_col_se = None
self.paper_hist = None
self.paper = None
self.words = None
self.translations = []
self.pointed_locations = deque(maxlen=20)
def draw_paper_rect(self, frame):
rows,cols,_ = frame.shape
self.paper_row_nw = rows/5
self.paper_row_se = 4*rows/5
self.paper_col_nw = 2*cols/5
self.paper_col_se = 3*cols/5
cv2.rectangle(frame,(self.paper_col_nw,self.paper_row_nw),(self.paper_col_se,self.paper_row_se),
(0,255,0),1)
black = np.zeros(frame.shape, dtype=frame.dtype)
frame_final = np.vstack([frame, black])
return frame_final
def train_paper(self, frame):
self.set_paper_hist(frame)
self.trained_paper = True
def get_paper(self, frame):
paper_masked = image_analysis.apply_hist_mask(frame, self.paper_hist)
contours = image_analysis.contours(paper_masked)
max_contour = image_analysis.max_contour(contours)
paper = image_analysis.contour_interior(frame, max_contour)
return paper
def set_paper(self, frame):
self.paper = self.get_paper(frame)
def paper_copy(self):
paper = self.paper.copy()
return paper
def set_ocr_text(self, frame):
paper = self.get_paper(frame)
thresh = image_analysis.gray_threshold(paper, 100)
paper_img = Image.fromarray(thresh)
self.tr.set_image(paper_img)
self.tr.get_text()
self.words = self.tr.get_words()
for w in self.words:
translation = self.translate(w.value)
self.translations.append(translation)
def set_paper_hist(self, frame):
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
roi = hsv[self.paper_row_nw:self.paper_row_se, self.paper_col_nw:self.paper_col_se]
self.paper_hist = cv2.calcHist([roi], [0, 1], None, [180, 256], [0, 180, 0, 256])
cv2.normalize(self.paper_hist, self.paper_hist, 0, 255, cv2.NORM_MINMAX)
def get_word_at_point(self, point):
for i, w in enumerate(self.words):
x_nw,y_nw,x_se,y_sw = w.box
x,y = point
if x > x_nw and x < x_se and y > y_nw and y < y_sw:
return self.translations[i]
def get_word_index(self, point):
for i, w in enumerate(self.words):
x_nw,y_nw,x_se,y_sw = w.box
x,y = point
if x > x_nw and x < x_se and y > y_nw and y < y_sw:
return i
def translate(self, word):
translated_word = self.gs.translate(word,'en',source_language='de')
return translated_word
def update_pointed_locations(self, point):
index = self.get_word_index(point)
if index != None:
self.pointed_locations.append(index)
def get_most_common_word(self):
index = self.most_common_location()
if index != None:
word = self.translations[index].encode('ascii', errors='backslashreplace')
return word
def most_common_location(self):
values = set(self.pointed_locations)
index = None
maxi = 0
for i in values:
num = self.pointed_locations.count(i)
if num > maxi:
index = i
frequency = float(self.pointed_locations.count(index))/float(self.pointed_locations.maxlen)
if frequency > 0.25:
return index
else:
return None
