TextFinder from firs level ROI
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EAST/frozen_east_text_detection.pb
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EAST/frozen_east_text_detection.pb
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main.py
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main.py
@ -4,11 +4,122 @@ except ImportError:
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import Image
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import Image
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from cv2 import cv2
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from cv2 import cv2
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import pytesseract
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import pytesseract
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import numpy
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import numpy as np
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from imutils.object_detection import non_max_suppression
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pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
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pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
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faceCascade = cv2.CascadeClassifier('haarcascade/haarcascade_frontalface_default.xml')
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faceCascade = cv2.CascadeClassifier('haarcascade/haarcascade_frontalface_default.xml')
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def findText(img, i):
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# @TODO
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# make ROI from found texts and return a array of imgs. Then try recon text by pytesseract
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image = img
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orig = image.copy()
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(H, W) = image.shape[:2]
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# set the new width and height and then determine the ratio in change
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# for both the width and height
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(newW, newH) = (320, 320)
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rW = W / float(newW)
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rH = H / float(newH)
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# resize the image and grab the new image dimensions
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image = cv2.resize(image, (newW, newH))
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(H, W) = image.shape[:2]
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# define the two output layer names for the EAST detector model that
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# we are interested -- the first is the output probabilities and the
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# second can be used to derive the bounding box coordinates of text
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layerNames = [
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"feature_fusion/Conv_7/Sigmoid",
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"feature_fusion/concat_3"]
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# load the pre-trained EAST text detector
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print("[INFO] loading EAST text detector...")
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net = cv2.dnn.readNet('./EAST/frozen_east_text_detection.pb')
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# construct a blob from the image and then perform a forward pass of
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# the model to obtain the two output layer sets
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blob = cv2.dnn.blobFromImage(image, 1.0, (W, H),
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(123.68, 116.78, 103.94), swapRB=True, crop=False)
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net.setInput(blob)
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(scores, geometry) = net.forward(layerNames)
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# grab the number of rows and columns from the scores volume, then
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# initialize our set of bounding box rectangles and corresponding
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# confidence scores
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(numRows, numCols) = scores.shape[2:4]
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rects = []
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confidences = []
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# loop over the number of rows
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for y in range(0, numRows):
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# extract the scores (probabilities), followed by the geometrical
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# data used to derive potential bounding box coordinates that
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# surround text
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scoresData = scores[0, 0, y]
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xData0 = geometry[0, 0, y]
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xData1 = geometry[0, 1, y]
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xData2 = geometry[0, 2, y]
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xData3 = geometry[0, 3, y]
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anglesData = geometry[0, 4, y]
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# loop over the number of columns
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for x in range(0, numCols):
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# if our score does not have sufficient probability, ignore it
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if scoresData[x] < 0.5:
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continue
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# compute the offset factor as our resulting feature maps will
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# be 4x smaller than the input image
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(offsetX, offsetY) = (x * 4.0, y * 4.0)
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# extract the rotation angle for the prediction and then
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# compute the sin and cosine
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angle = anglesData[x]
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cos = np.cos(angle)
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sin = np.sin(angle)
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# use the geometry volume to derive the width and height of
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# the bounding box
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h = xData0[x] + xData2[x]
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w = xData1[x] + xData3[x]
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# compute both the starting and ending (x, y)-coordinates for
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# the text prediction bounding box
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endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))
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endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))
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startX = int(endX - w)
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startY = int(endY - h)
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# add the bounding box coordinates and probability score to
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# our respective lists
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rects.append((startX, startY, endX, endY))
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confidences.append(scoresData[x])
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# apply non-maxima suppression to suppress weak, overlapping bounding
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# boxes
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boxes = non_max_suppression(np.array(rects), probs=confidences)
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# loop over the bounding boxes
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for (startX, startY, endX, endY) in boxes:
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# scale the bounding box coordinates based on the respective
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# ratios
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startX = int(startX * rW)
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startY = int(startY * rH)
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endX = int(endX * rW)
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endY = int(endY * rH)
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# draw the bounding box on the image
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cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 2)
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# show the output image
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cv2.imshow("Text Detection_"+str(i), orig)
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# MAIN PROGRAM
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image = cv2.imread('imgs/bib_01.jpg')
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image = cv2.imread('imgs/bib_01.jpg')
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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@ -20,7 +131,7 @@ faces = faceCascade.detectMultiScale(
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)
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)
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faceNumber = len(faces)
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faceNumber = len(faces)
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print('znaleziono '+str(faceNumber)+' twarzy \n')
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# print('znaleziono '+str(faceNumber)+' twarzy \n')
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ROI = [0] * faceNumber
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ROI = [0] * faceNumber
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for (x, y, w, h) in faces:
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for (x, y, w, h) in faces:
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@ -34,9 +145,8 @@ for (x, y, w, h) in faces:
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h = int(3.5*h)
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h = int(3.5*h)
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if x < 0:
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if x < 0:
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x = 1
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x = 1
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cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 2)
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# cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 2)
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# print('y = '+str(y)+', y+h = '+str(y+h)+', x = '+str(x)+', x+w = '+str(x+w)+'\n')
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# print('y = '+str(y)+', y+h = '+str(y+h)+', x = '+str(x)+', x+w = '+str(x+w)+'\n')
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print('wycinam tors nr '+str(i+1)+'.')
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crop_img = image[y:y+h, x:x+w]
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crop_img = image[y:y+h, x:x+w]
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ROI[i] = crop_img
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ROI[i] = crop_img
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# cv2.imshow("cropped_"+str(i), crop_img[i])
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# cv2.imshow("cropped_"+str(i), crop_img[i])
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@ -44,8 +154,18 @@ for (x, y, w, h) in faces:
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i = 0
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i = 0
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for x in ROI:
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for x in ROI:
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print('wyswietlam tors nr '+str(i)+'.')
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findText(x, i)
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cv2.imshow("cropped_"+str(i), x)
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# x = cv2.cvtColor(x,cv2.COLOR_BGR2GRAY)
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# kernel = np.ones((1,1), np.uint8)
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# x = cv2.dilate(x, kernel, iterations = 1)
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# x = cv2.erode(x, kernel, iterations=1)
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# x = cv2.adaptiveThreshold(x, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
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# cv2.imshow("Text Detection_"+str(i), orig)
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# cv2.imshow("cropped_"+str(i), x)
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# cv2.imwrite("cropped_"+str(i)+"_thres.jpg", x)
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# result = pytesseract.image_to_string(Image.open("cropped_"+str(i)+"_thres.jpg"))
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# print(result)
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i = i + 1
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i = i + 1
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cv2.imshow("Faces found", image)
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cv2.imshow("Faces found", image)
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