119a945ef7
Fix of the edge case 0 weight that occasionally will pop up in some specific situations. This was crashing the script.
342 lines
11 KiB
Python
342 lines
11 KiB
Python
import cv2
|
|
import requests
|
|
import os
|
|
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 """
|
|
|
|
scale_by = 1
|
|
if is_landscape(im.width, im.height):
|
|
scale_by = settings.crop_height / im.height
|
|
elif is_portrait(im.width, im.height):
|
|
scale_by = settings.crop_width / im.width
|
|
elif is_square(im.width, im.height):
|
|
if is_square(settings.crop_width, settings.crop_height):
|
|
scale_by = settings.crop_width / im.width
|
|
elif is_landscape(settings.crop_width, settings.crop_height):
|
|
scale_by = settings.crop_width / im.width
|
|
elif is_portrait(settings.crop_width, settings.crop_height):
|
|
scale_by = settings.crop_height / im.height
|
|
|
|
im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
|
|
im_debug = im.copy()
|
|
|
|
focus = focal_point(im_debug, 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]
|
|
|
|
results = []
|
|
|
|
results.append(im.crop(tuple(crop)))
|
|
|
|
if settings.annotate_image:
|
|
d = ImageDraw.Draw(im_debug)
|
|
rect = list(crop)
|
|
rect[2] -= 1
|
|
rect[3] -= 1
|
|
d.rectangle(rect, outline=GREEN)
|
|
results.append(im_debug)
|
|
if settings.destop_view_image:
|
|
im_debug.show()
|
|
|
|
return results
|
|
|
|
def focal_point(im, settings):
|
|
corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
|
|
entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
|
|
face_points = image_face_points(im, settings) if settings.face_points_weight > 0 else []
|
|
|
|
pois = []
|
|
|
|
weight_pref_total = 0
|
|
if len(corner_points) > 0:
|
|
weight_pref_total += settings.corner_points_weight
|
|
if len(entropy_points) > 0:
|
|
weight_pref_total += settings.entropy_points_weight
|
|
if len(face_points) > 0:
|
|
weight_pref_total += settings.face_points_weight
|
|
|
|
corner_centroid = None
|
|
if len(corner_points) > 0:
|
|
corner_centroid = centroid(corner_points)
|
|
corner_centroid.weight = settings.corner_points_weight / weight_pref_total
|
|
pois.append(corner_centroid)
|
|
|
|
entropy_centroid = None
|
|
if len(entropy_points) > 0:
|
|
entropy_centroid = centroid(entropy_points)
|
|
entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
|
|
pois.append(entropy_centroid)
|
|
|
|
face_centroid = None
|
|
if len(face_points) > 0:
|
|
face_centroid = centroid(face_points)
|
|
face_centroid.weight = settings.face_points_weight / weight_pref_total
|
|
pois.append(face_centroid)
|
|
|
|
average_point = poi_average(pois, settings)
|
|
|
|
if settings.annotate_image:
|
|
d = ImageDraw.Draw(im)
|
|
max_size = min(im.width, im.height) * 0.07
|
|
if corner_centroid is not None:
|
|
color = BLUE
|
|
box = corner_centroid.bounding(max_size * corner_centroid.weight)
|
|
d.text((box[0], box[1]-15), "Edge: %.02f" % corner_centroid.weight, fill=color)
|
|
d.ellipse(box, outline=color)
|
|
if len(corner_points) > 1:
|
|
for f in corner_points:
|
|
d.rectangle(f.bounding(4), outline=color)
|
|
if entropy_centroid is not None:
|
|
color = "#ff0"
|
|
box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
|
|
d.text((box[0], box[1]-15), "Entropy: %.02f" % entropy_centroid.weight, fill=color)
|
|
d.ellipse(box, outline=color)
|
|
if len(entropy_points) > 1:
|
|
for f in entropy_points:
|
|
d.rectangle(f.bounding(4), outline=color)
|
|
if face_centroid is not None:
|
|
color = RED
|
|
box = face_centroid.bounding(max_size * face_centroid.weight)
|
|
d.text((box[0], box[1]-15), "Face: %.02f" % face_centroid.weight, fill=color)
|
|
d.ellipse(box, outline=color)
|
|
if len(face_points) > 1:
|
|
for f in face_points:
|
|
d.rectangle(f.bounding(4), outline=color)
|
|
|
|
d.ellipse(average_point.bounding(max_size), outline=GREEN)
|
|
|
|
return average_point
|
|
|
|
|
|
def image_face_points(im, settings):
|
|
if settings.dnn_model_path is not None:
|
|
detector = cv2.FaceDetectorYN.create(
|
|
settings.dnn_model_path,
|
|
"",
|
|
(im.width, im.height),
|
|
0.9, # score threshold
|
|
0.3, # nms threshold
|
|
5000 # keep top k before nms
|
|
)
|
|
faces = detector.detect(np.array(im))
|
|
results = []
|
|
if faces[1] is not None:
|
|
for face in faces[1]:
|
|
x = face[0]
|
|
y = face[1]
|
|
w = face[2]
|
|
h = face[3]
|
|
results.append(
|
|
PointOfInterest(
|
|
int(x + (w * 0.5)), # face focus left/right is center
|
|
int(y + (h * 0.33)), # face focus up/down is close to the top of the head
|
|
size = w,
|
|
weight = 1/len(faces[1])
|
|
)
|
|
)
|
|
return results
|
|
else:
|
|
np_im = np.array(im)
|
|
gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
|
|
|
|
tries = [
|
|
[ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
|
|
[ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
|
|
[ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
|
|
[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
|
|
[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
|
|
[ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
|
|
[ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
|
|
[ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
|
|
]
|
|
for t in tries:
|
|
classifier = cv2.CascadeClassifier(t[0])
|
|
minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
|
|
try:
|
|
faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
|
|
minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
|
|
except:
|
|
continue
|
|
|
|
if len(faces) > 0:
|
|
rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
|
|
return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
|
|
return []
|
|
|
|
|
|
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)*0.06,
|
|
useHarrisDetector=False,
|
|
)
|
|
|
|
if points is None:
|
|
return []
|
|
|
|
focal_points = []
|
|
for point in points:
|
|
x, y = point.ravel()
|
|
focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
|
|
|
|
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, size=25, weight=1.0)]
|
|
|
|
|
|
def image_entropy(im):
|
|
# greyscale image entropy
|
|
# band = np.asarray(im.convert("L"))
|
|
band = np.asarray(im.convert("1"), dtype=np.uint8)
|
|
hist, _ = np.histogram(band, bins=range(0, 256))
|
|
hist = hist[hist > 0]
|
|
return -np.log2(hist / hist.sum()).sum()
|
|
|
|
def centroid(pois):
|
|
x = [poi.x for poi in pois]
|
|
y = [poi.y for poi in pois]
|
|
return PointOfInterest(sum(x)/len(pois), sum(y)/len(pois))
|
|
|
|
|
|
def poi_average(pois, settings):
|
|
weight = 0.0
|
|
x = 0.0
|
|
y = 0.0
|
|
for poi in pois:
|
|
weight += poi.weight
|
|
x += poi.x * poi.weight
|
|
y += poi.y * poi.weight
|
|
avg_x = round(weight and x / weight)
|
|
avg_y = round(weight and y / weight)
|
|
|
|
return PointOfInterest(avg_x, avg_y)
|
|
|
|
|
|
def is_landscape(w, h):
|
|
return w > h
|
|
|
|
|
|
def is_portrait(w, h):
|
|
return h > w
|
|
|
|
|
|
def is_square(w, h):
|
|
return w == h
|
|
|
|
|
|
def download_and_cache_models(dirname):
|
|
download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
|
|
model_file_name = 'face_detection_yunet.onnx'
|
|
|
|
if not os.path.exists(dirname):
|
|
os.makedirs(dirname)
|
|
|
|
cache_file = os.path.join(dirname, model_file_name)
|
|
if not os.path.exists(cache_file):
|
|
print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
|
|
response = requests.get(download_url)
|
|
with open(cache_file, "wb") as f:
|
|
f.write(response.content)
|
|
|
|
if os.path.exists(cache_file):
|
|
return cache_file
|
|
return None
|
|
|
|
|
|
class PointOfInterest:
|
|
def __init__(self, x, y, weight=1.0, size=10):
|
|
self.x = x
|
|
self.y = y
|
|
self.weight = weight
|
|
self.size = size
|
|
|
|
def bounding(self, size):
|
|
return [
|
|
self.x - size//2,
|
|
self.y - size//2,
|
|
self.x + size//2,
|
|
self.y + size//2
|
|
]
|
|
|
|
|
|
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, dnn_model_path=None):
|
|
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 = face_points_weight
|
|
self.annotate_image = annotate_image
|
|
self.destop_view_image = False
|
|
self.dnn_model_path = dnn_model_path
|