face detect and approximate torso area

README.md

@@ -1,5 +1,12 @@
 # BIB Recognition
 
+**Used libs:**
+* OpenCV
+* pynum
+* pytesseract
+* PIL
+* imutils
+
 ## Bib_recognition
 
 ## Photo_finder

main.py

@@ -0,0 +1,54 @@
+try:
+    from PIL import Image
+except ImportError:
+    import Image
+from cv2 import cv2
+import pytesseract 
+import numpy
+
+pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
+faceCascade = cv2.CascadeClassifier('haarcascade/haarcascade_frontalface_default.xml')
+
+image = cv2.imread('imgs/bib_01.jpg')
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+faces = faceCascade.detectMultiScale(
+    gray,
+    scaleFactor=1.1,
+    minNeighbors=5,
+    minSize=(30, 30)
+)
+
+faceNumber = len(faces)
+print('znaleziono '+str(faceNumber)+' twarzy \n')
+ROI = [0] * faceNumber
+
+for (x, y, w, h) in faces:
+    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
+
+i = 0
+for (x, y, w, h) in faces:
+    x = int( x - (3*w/4) )
+    y = int(y + 1.5*h)
+    w = int(2.5*w)
+    h = int(3.5*h)
+    if x < 0:
+        x = 1
+    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])
+    i = i + 1
+
+i = 0
+for x in ROI:
+    print('wyswietlam tors nr '+str(i)+'.')
+    cv2.imshow("cropped_"+str(i), x)
+    i = i + 1
+
+cv2.imshow("Faces found", image)
+cv2.waitKey()
+
+#print(pytesseract.image_to_string(Image.open('imgs/bib_01.jpg')))