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import os
from os.path import join
import cv2
from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
def ensure_pil(im):
"""Ensure image is Pillow format"""
try:
im.verify()
return im
except:
return Image.fromarray(im.astype('uint8'), 'RGB')
def ensure_np(im):
"""Ensure image is numpy array"""
if type(im) == np.ndarray:
return im
return np.asarray(im, np.uint8)
def ensure_dir(d):
"""Create directories"""
if not os.path.exists(d):
os.makedirs(d)
def filter_pixellate(src,num_cells):
"""Downsample, then upsample image for pixellation"""
w,h = src.size
dst = src.resize((num_cells,num_cells), Image.NEAREST)
dst = dst.resize((w,h), Image.NEAREST)
return dst
# Plot images inline using Matplotlib
def pltimg(im,title=None,mode='rgb',figsize=(8,12),dpi=160,output=None):
plt.figure(figsize=figsize)
plt.xticks([]),plt.yticks([])
if title is not None:
plt.title(title)
if mode.lower() == 'bgr':
im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB)
f = plt.gcf()
plt.imshow(im)
plt.show()
plt.draw()
if output is not None:
bbox_inches='tight'
ext=osp.splitext(output)[1].replace('.','')
f.savefig(output,dpi=dpi,format=ext)
print('Image saved to: {}'.format(output))
# Define a function to detect faces using OpenCV's haarcascades
def detect_faces(classifier,src,scale_factor=1.1,overlaps=3,
min_size=70, max_size=700,
flags=0):
min_size = (min_size, min_size) # minimum face size
max_size = (max_size, max_size) # maximum face size
# Convert to grayscale
src_gray = cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)
# Run detector
matches = classifier.detectMultiScale(src_gray,
scale_factor,
overlaps,
flags,
min_size,
max_size)
# By default, this returns x,y,w,w
# Modify to return x1,y1,x2,y2
matches = [ (r[0],r[1],r[0]+r[2],r[1]+r[3]) for r in matches]
return matches
def detect_faces_dlib(im,pyramids=0):
rects = detector(im, pyramids)
faces = [ (r.left(),r.top(),r.right(),r.bottom()) for r in rects] #x1,y1,x2,y2
return faces
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