TextFinder from firs level ROI

main.py

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