import cv2, sys, glob, os

cascPath = "haarcascade_frontalface_default.xml"

# Create the haar cascade
#faceCascade = cv2.CascadeClassifier(cascPath)
faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')


folder = "cache/picsId/Pending/test/"
outfolder = "cache/picsId/Pending/test3/"
files=glob.glob(folder + "*.jpg")   
i = 0

for file in files:

    # Read the image
    fn = file.split("/")[-1]
    print(file)
    print(fn)
    
    image = cv2.imread(file)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    img_size = image.shape
    ww = img_size[0]
    hh = img_size[1]
    
    print("Image size: " + str(img_size))
    
    
    
    # Detect faces in the image
    faces = faceCascade.detectMultiScale(gray, minNeighbors=5, minSize=(70, 70))
    """
        gray,
        scaleFactor=1.1,
        minNeighbors=5,
        minSize=(30, 30),
        flags = cv2.CASCADE_SCALE_IMAGE )
    """
        
    print("Found %d faces!" % len(faces))
    if len(faces)==0: exit()

    # Crop Padding
    left = 10
    right = 10
    top = 10
    bottom = 10

    # Draw a rectangle around the faces
    j = 0
    for (x, y, w, h) in faces:
        k = j
        if k == 0: k = ''
        else: k = str(k) + '_'

        # Dubugging boxes
        # cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
        
        new_x1 = x-left
        new_y1 = y-top
        
        new_x2 = x+w+right
        new_y2 = y+h+bottom
        
        x1 = max(new_x1, 0)
        y1 = max(new_y1, 0)
        
        x2 = min(new_x2, ww)
        y2 = min(new_y2, hh)
        
        xx1 = min(x1,x2)
        xx2 = max(x1,x2)
        
        yy1 = min(y1,y2)
        yy2 = max(y1,y2)
        
        print(x, y, w, h)
        print(ww, hh)
        
        print(xx1,xx2,yy1,yy2)
        
        #image  = image[y-top:y+h+bottom, x-left:x+w+right]
        write_image = image[yy1:yy2, xx1:xx2]
        
        print("Writing: " + outfolder + k + fn)
        try:
            cv2.imwrite(outfolder + k + fn, write_image)
        except:
            print("  (failed. was image too small?)")
        j += 1
        


    

    # print ("cropped_{1}{0}".format(str(file),str(x)))
    
    
    
    
    
    
    

# autocrop
#

"""
from PIL import Image
from autocrop import Cropper

cropper = Cropper()

# Get a Numpy array of the cropped image
cropped_array = cropper.crop('portrait.png')

# Save the cropped image with PIL
cropped_image = Image.fromarray(cropped_array)
cropped_image.save('cropped.png')


--------------

usage: [-h] [-o OUTPUT] [-i INPUT] [-w WIDTH] [-H HEIGHT] [-e EXTENSION] [-v]

Automatically crops faces from batches of pictures

optional arguments:
  -h, --help
  		Show this help message and exit
  -o, --output, -p, --path
		Folder where cropped images will be placed.
		Default: current working directory
  -r, --reject
		Folder where images without detected faces will be placed.
		Default: same as output directory
  -i, --input
		Folder where images to crop are located.
		Default: current working directory
  -w, --width
		Width of cropped files in px. Default=500
  -H, --height
		Height of cropped files in px. Default=500
  --facePercent
  		Zoom factor. Percentage of face height to image height.
  -e, --extension
  		Enter the image extension which to save at output.
  		Default: Your current image extension
  -v, --version
  		Show program's version number and exit
        
        
        
        
autocrop -i . -o test4 -w 250 -H 333        


"""





# smartcrop
#
#
# smartcrop -W 1140 -H 400 -i input.jpg -o output.jpg
#
#




# imagemagick jpeg compress
#
# convert -strip -interlace Plane -gaussian-blur 0.05 -quality 60% -adaptive-resize 60% img_original.jpg img_resize.jpg
#
#
#  convert image.jpg -define jpeg:extent=150kb result.jpg