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import cv2
from collections import defaultdict
from math import log, sqrt
import numpy as np
from PIL import Image, ImageDraw
GREEN = "#0F0"
BLUE = "#00F"
RED = "#F00"
def crop_image(im, settings):
""" Intelligently crop an image to the subject matter """
if im.height > im.width:
im = im.resize((settings.crop_width, settings.crop_height * im.height // im.width))
else:
im = im.resize((settings.crop_width * im.width // im.height, settings.crop_height))
focus = focal_point(im, settings)
# take the focal point and turn it into crop coordinates that try to center over the focal
# point but then get adjusted back into the frame
y_half = int(settings.crop_height / 2)
x_half = int(settings.crop_width / 2)
x1 = focus.x - x_half
if x1 < 0:
x1 = 0
elif x1 + settings.crop_width > im.width:
x1 = im.width - settings.crop_width
y1 = focus.y - y_half
if y1 < 0:
y1 = 0
elif y1 + settings.crop_height > im.height:
y1 = im.height - settings.crop_height
x2 = x1 + settings.crop_width
y2 = y1 + settings.crop_height
crop = [x1, y1, x2, y2]
if settings.annotate_image:
d = ImageDraw.Draw(im)
rect = list(crop)
rect[2] -= 1
rect[3] -= 1
d.rectangle(rect, outline=GREEN)
if settings.destop_view_image:
im.show()
return im.crop(tuple(crop))
def focal_point(im, settings):
corner_points = image_corner_points(im, settings)
entropy_points = image_entropy_points(im, settings)
face_points = image_face_points(im, settings)
total_points = len(corner_points) + len(entropy_points) + len(face_points)
corner_weight = settings.corner_points_weight
entropy_weight = settings.entropy_points_weight
face_weight = settings.face_points_weight
weight_pref_total = corner_weight + entropy_weight + face_weight
# weight things
pois = []
if weight_pref_total == 0 or total_points == 0:
return pois
pois.extend(
[ PointOfInterest( p.x, p.y, weight=p.weight * ( (corner_weight/weight_pref_total) / (len(corner_points)/total_points) )) for p in corner_points ]
)
pois.extend(
[ PointOfInterest( p.x, p.y, weight=p.weight * ( (entropy_weight/weight_pref_total) / (len(entropy_points)/total_points) )) for p in entropy_points ]
)
pois.extend(
[ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
)
if settings.annotate_image:
d = ImageDraw.Draw(im)
average_point = poi_average(pois, settings, im=im)
if settings.annotate_image:
d.ellipse([average_point.x - 25, average_point.y - 25, average_point.x + 25, average_point.y + 25], outline=GREEN)
return average_point
def image_face_points(im, settings):
np_im = np.array(im)
gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
classifier = cv2.CascadeClassifier(f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml')
minsize = int(min(im.width, im.height) * 0.15) # at least N percent of the smallest side
faces = classifier.detectMultiScale(gray, scaleFactor=1.05,
minNeighbors=5, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
if len(faces) == 0:
return []
rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
if settings.annotate_image:
for f in rects:
d = ImageDraw.Draw(im)
d.rectangle(f, outline=RED)
return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2) for r in rects]
def image_corner_points(im, settings):
grayscale = im.convert("L")
# naive attempt at preventing focal points from collecting at watermarks near the bottom
gd = ImageDraw.Draw(grayscale)
gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
np_im = np.array(grayscale)
points = cv2.goodFeaturesToTrack(
np_im,
maxCorners=100,
qualityLevel=0.04,
minDistance=min(grayscale.width, grayscale.height)*0.07,
useHarrisDetector=False,
)
if points is None:
return []
focal_points = []
for point in points:
x, y = point.ravel()
focal_points.append(PointOfInterest(x, y))
return focal_points
def image_entropy_points(im, settings):
landscape = im.height < im.width
portrait = im.height > im.width
if landscape:
move_idx = [0, 2]
move_max = im.size[0]
elif portrait:
move_idx = [1, 3]
move_max = im.size[1]
else:
return []
e_max = 0
crop_current = [0, 0, settings.crop_width, settings.crop_height]
crop_best = crop_current
while crop_current[move_idx[1]] < move_max:
crop = im.crop(tuple(crop_current))
e = image_entropy(crop)
if (e > e_max):
e_max = e
crop_best = list(crop_current)
crop_current[move_idx[0]] += 4
crop_current[move_idx[1]] += 4
x_mid = int(crop_best[0] + settings.crop_width/2)
y_mid = int(crop_best[1] + settings.crop_height/2)
return [PointOfInterest(x_mid, y_mid)]
def image_entropy(im):
# greyscale image entropy
band = np.asarray(im.convert("1"))
hist, _ = np.histogram(band, bins=range(0, 256))
hist = hist[hist > 0]
return -np.log2(hist / hist.sum()).sum()
def poi_average(pois, settings, im=None):
weight = 0.0
x = 0.0
y = 0.0
for pois in pois:
if settings.annotate_image and im is not None:
w = 4 * 0.5 * sqrt(pois.weight)
d = ImageDraw.Draw(im)
d.ellipse([
pois.x - w, pois.y - w,
pois.x + w, pois.y + w ], fill=BLUE)
weight += pois.weight
x += pois.x * pois.weight
y += pois.y * pois.weight
avg_x = round(x / weight)
avg_y = round(y / weight)
return PointOfInterest(avg_x, avg_y)
class PointOfInterest:
def __init__(self, x, y, weight=1.0):
self.x = x
self.y = y
self.weight = weight
class Settings:
def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False):
self.crop_width = crop_width
self.crop_height = crop_height
self.corner_points_weight = corner_points_weight
self.entropy_points_weight = entropy_points_weight
self.face_points_weight = entropy_points_weight
self.annotate_image = annotate_image
self.destop_view_image = False
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