Path: blob/master/Model-5/Untitled1.ipynb
427 views
Kernel: Python 2
In [10]:
import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 from ocr.helpers import implt, resize, ratio %matplotlib inline image = cv2.cvtColor(cv2.imread("2_1.jpg"), cv2.COLOR_BGR2RGB) image_1 = image img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) img_1 = img
Out[10]:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-10-28325f6aa1be> in <module>()
8
9 image = cv2.cvtColor(cv2.imread("2_1.jpg"), cv2.COLOR_BGR2RGB)
---> 10 image_1 = image.clone()
11 img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
12 img_1 = img.clone()
AttributeError: 'numpy.ndarray' object has no attribute 'clone'
In [6]:
def sobelDetect(channel): """ The Sobel Operator""" sobelX = cv2.Sobel(channel, cv2.CV_16S, 1, 0) sobelY = cv2.Sobel(channel, cv2.CV_16S, 0, 1) # Combine x, y gradient magnitudes sqrt(x^2 + y^2) sobel = np.hypot(sobelX, sobelY) sobel[sobel > 255] = 255 return np.uint8(sobel) def edgeDetect(im): """ Edge detection The Sobel operator is applied for each image layer (RGB) """ return np.max(np.array([sobelDetect(im[:,:, 0]), sobelDetect(im[:,:, 1]), sobelDetect(im[:,:, 2]) ]), axis=0) # Image pre-processing - blur, edges, threshold, closing blurred = cv2.GaussianBlur(image, (5, 5), 18) edgeImg = edgeDetect(blurred) ret, edgeImg = cv2.threshold(edgeImg, 50, 255, cv2.THRESH_BINARY) bwImage = cv2.morphologyEx(edgeImg, cv2.MORPH_CLOSE, np.ones((20,20), np.uint8))
In [7]:
def delLines(gray): """ Delete page lines """ linek = np.ones((1,11),np.uint8) x = cv2.morphologyEx(gray, cv2.MORPH_OPEN, linek ,iterations=1) i = gray-x closing = cv2.morphologyEx(dil, cv2.MORPH_CLOSE, np.ones((17,17), np.uint8)) #implt(closing, 'gray', 'Del Lines') return closing def delBigAreas(img): """ Find and remove contours too big for a word """ gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # ret, gray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) gray = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 101, 3) #implt(gray, 'gray') gray2 = gray.copy() mask = np.zeros(gray.shape,np.uint8) im2, contours, hierarchy = cv2.findContours(gray, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: if (200 < cv2.contourArea(cnt) < 5000): cv2.drawContours(img,[cnt],0,(0,255,0),2) cv2.drawContours(mask,[cnt],0,255,-1) #implt(mask) #implt(img) def union(a,b): x = min(a[0], b[0]) y = min(a[1], b[1]) w = max(a[0]+a[2], b[0]+b[2]) - x h = max(a[1]+a[3], b[1]+b[3]) - y return [x, y, w, h] def isIntersect(a,b): x = max(a[0], b[0]) y = max(a[1], b[1]) w = min(a[0]+a[2], b[0]+b[2]) - x h = min(a[1]+a[3], b[1]+b[3]) - y if w<0 or h<0: return False return True def groupRectangles(rec): """ Uion intersecting rectangles Args: rec - list of rectangles in form [x, y, w, h] Return: list of grouped ractangles """ tested = [False for i in range(len(rec))] final = [] i = 0 while i < len(rec): if not tested[i]: j = i+1 while j < len(rec): if not tested[j] and isIntersect(rec[i], rec[j]): rec[i] = union(rec[i], rec[j]) tested[j] = True j = i j += 1 final += [rec[i]] i += 1 return final def textDetect(img, original): """ Text detection using contours """ # Resize image small = resize(img, 2000) image = resize(original, 2000) # Finding contours mask = np.zeros(small.shape, np.uint8) im2, cnt, hierarchy = cv2.findContours(np.copy(small), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE) #implt(img, 'gray') # Variables for contour index and words' bounding boxes index = 0 boundingBoxes = np.array([0,0,0,0]) bBoxes = [] # CCOMP hierarchy: [Next, Previous, First Child, Parent] # cv2.RETR_CCOMP - contours into 2 levels # Go through all contours in first level while (index >= 0): x,y,w,h = cv2.boundingRect(cnt[index]) # Get only the contour cv2.drawContours(mask, cnt, index, (255, 255, 255), cv2.FILLED) maskROI = mask[y:y+h, x:x+w] # Ratio of white pixels to area of bounding rectangle r = float(cv2.countNonZero(maskROI)) / (w * h) # Limits for text (white pixel ratio, width, height) # TODO Test h/w and w/h ratios if r > 0.1 and 1600 > w > 10 and 1600 > h > 10 and h/w < 3 and w/h < 10 and (60 // h) * w < 1000: bBoxes += [[x, y, w, h]] # Index of next contour index = hierarchy[0][index][0] # Group intersecting rectangles bBoxes = groupRectangles(bBoxes) i = 0 f = open("output/words/normal/bounding_boxes_normal.txt","w") for (x, y, w, h) in bBoxes: boundingBoxes = np.vstack((boundingBoxes, np.array([x, y, x+w, y+h]))) cv2.imwrite("output/words/normal/"+str(i)+".jpg",image[y:y+h, x:x+w]) f.write(str(i) + "\t => \t" + "("+str(x)+","+str(y)+")"+","+"("+str(x+w)+","+str(y+h)+")"+"\n") cv2.rectangle(image, (x, y),(x+w,y+h), (0, 255, 0), 2) i = i+1 #implt(image, t='Bounding rectangles') # Recalculate coordinates to original scale bBoxes = boundingBoxes.dot(ratio(image, small.shape[0])).astype(np.int64) return bBoxes[1:] bBoxes = textDetect(bwImage, image)
In [9]:
def textDetectWatershed(thresh, original): """ Text detection using watershed algorithm """ # According to: http://docs.opencv.org/trunk/d3/db4/tutorial_py_watershed.html img = resize(original, 3000) thresh = resize(thresh, 3000) # noise removal kernel = np.ones((3,3),np.uint8) opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 3) # sure background area sure_bg = cv2.dilate(opening,kernel,iterations=3) # Finding sure foreground area dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5) ret, sure_fg = cv2.threshold(dist_transform,0.01*dist_transform.max(),255,0) # Finding unknown region sure_fg = np.uint8(sure_fg) unknown = cv2.subtract(sure_bg,sure_fg) # Marker labelling ret, markers = cv2.connectedComponents(sure_fg) # Add one to all labels so that sure background is not 0, but 1 markers += 1 # Now, mark the region of unknown with zero markers[unknown == 255] = 0 markers = cv2.watershed(img, markers) implt(markers, t='Markers') image = img.copy() gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Creating result array boundingBoxes = np.array([0,0,0,0]) bBoxes = [] for mark in np.unique(markers): # mark == 0 --> background if mark == 0: continue # Draw it on mask and detect biggest contour mask = np.zeros(gray.shape, dtype="uint8") mask[markers == mark] = 255 cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2] c = max(cnts, key=cv2.contourArea) # Draw a bounding rectangle if it contains text x,y,w,h = cv2.boundingRect(c) cv2.drawContours(mask, c, 0, (255, 255, 255), cv2.FILLED) maskROI = mask[y:y+h, x:x+w] # Ratio of white pixels to area of bounding rectangle r = float(cv2.countNonZero(maskROI)) / (w * h) # Limits for text if r > 0.1 and 2000 > w > 15 and 1500 > h > 15: bBoxes += [[x, y, w, h]] # Group intersecting rectangles bBoxes = groupRectangles(bBoxes) i = 0 f = open("output/words/watershed/bounding_boxes_watershed.txt","w") for (x, y, w, h) in bBoxes: boundingBoxes = np.vstack((boundingBoxes, np.array([x, y, x+w, y+h]))) cv2.imwrite("output/words/watershed/"+str(i)+".jpg",image[y:y+h, x:x+w]) f.write(str(i) + "\t => \t" + "("+str(x)+","+str(y)+")"+","+"("+str(x+w)+","+str(y+h)+")"+"\n") cv2.rectangle(image, (x, y),(x+w,y+h), (0, 255, 0), 2) i = i+1 #implt(image) # Recalculate coordinates to original size bBoxes = boundingBoxes.dot(ratio(original, 3000)).astype(np.int64) return bBoxes[1:] wbBoxes = textDetectWatershed(bwImage, image) #print(len(wbBoxes))
Out[9]:
In [ ]: