67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
import cv2
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import numpy as np
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from PIL import Image, ImageDraw
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def dbg(arr2d):
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dbg = {}
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for x in arr2d:
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for y in x:
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dbg[y] = dbg.get(y, 0) + 1
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print(dbg)
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#return
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img = Image.new("1", (max([len(x) for x in arr2d]), len(arr2d)))
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draw = ImageDraw.Draw(img)
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for x in range(len(arr2d)):
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for y in range(len(arr2d[x])):
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draw.point( (y,x), fill=1 if arr2d[x][y] > 100 else 0)
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img.show()
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def draw_boxes(arr2d, boxes):
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img = Image.new("1", (max([len(x) for x in arr2d]), len(arr2d)))
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draw = ImageDraw.Draw(img)
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for x in range(len(arr2d)):
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for y in range(len(arr2d[x])):
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draw.point( (y,x), fill=1 if arr2d[x][y] > 100 else 0)
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for box in boxes:
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x0 = box[0]
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y0 = box[1]
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x1 = box[0] + box[2]
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y1 = box[1] + box[3]
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draw.line( [(x0,y0), (x1,y0)], fill=1)
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draw.line( [(x1,y0), (x1,y1)], fill=1)
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draw.line( [(x1,y1), (x0,y1)], fill=1)
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draw.line( [(x0,y1), (x0,y0)], fill=1)
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img.show()
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# Load image, convert to HSV format, define lower/upper ranges, and perform
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# color segmentation to create a binary mask
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image = cv2.imread('1.png')
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hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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lower = np.array([0, 0, 0])
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upper = np.array([0, 255, 200])
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mask = cv2.inRange(hsv, lower, upper)
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#dbg(mask)
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# Create horizontal kernel and dilate to connect text characters
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,3))
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dilate = cv2.dilate(mask, kernel, iterations=0)
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#dbg(dilate)
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# Find contours and filter using aspect ratio
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# Remove non-text contours by filling in the contour
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cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cnts = cnts[0] if len(cnts) == 2 else cnts[1]
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draw_boxes(dilate, [cv2.boundingRect(c) for c in cnts])
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for c in cnts:
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x,y,w,h = cv2.boundingRect(c)
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ar = w / float(h)
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print(x, y, w, h)
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if ar < 5:
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cv2.drawContours(dilate, [c], -1, (0,0,0), -1)
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#dbg(dilate)
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exit()
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# Bitwise dilated image with mask, invert, then OCR
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result = 255 - cv2.bitwise_and(dilate, mask)
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dbg(result)
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