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1
.gitignore vendored
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@ -1,3 +1,2 @@
venv/
*.pyc
saves/

214
face_recognition/comparison.py
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@ -5,26 +5,16 @@ import logging
import argparse
import numpy as np
import time
import datetime
from PIL import ImageFont, ImageDraw, Image
import os
draw_colors = {
'hog': (198,65,124),
'haar': (255,255,255),
'dnn': (251,212,36),
'hog': (255,0,0),
'haar': (0,255,0),
'dnn': (0,0,255),
}
titles = {
'hog' : "Histogram of oriented gradients",
'haar' : "Haar cascades",
'dnn' : "Neural network",
}
fontfile = "SourceSansPro-Regular.ttf"
font = ImageFont.truetype(fontfile, 30)
font_s = ImageFont.truetype(fontfile, 20)
font = ImageFont.truetype("/home/ruben/Documents/Projecten/2018/PATH/presentation/lib/font/source-sans-pro/source-sans-pro-regular.ttf", 30)
class Result():
def __init__(self, algorithm, image, confidence_threshold = 0.5):
@ -43,27 +33,18 @@ class Result():
})
return self
def draw_detections(self, include_title = False):
def draw_detections(self):
color = draw_colors[self.algorithm]
cv2_im_rgb = cv2.cvtColor(self.visualisation,cv2.COLOR_BGR2RGB)
# Pass the image to PIL
pil_im = Image.fromarray(cv2_im_rgb)
draw = ImageDraw.Draw(pil_im, 'RGBA')
self.draw_detections_on(draw)
if include_title:
draw.text((10,10), titles[self.algorithm], fill=draw_colors[self.algorithm], font=font)
return cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)
def draw_detections_on(self, draw: ImageDraw):
'''
Draw on a specified canvas
'''
color = draw_colors[self.algorithm]
for detection in self.detections:
self.draw_detection(draw, detection, color)
self.visualisation = cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)
def draw_detection(self, draw: ImageDraw, detection: dict, color: tuple):
width = 2
@ -87,8 +68,51 @@ class Result():
color = tuple(color)
draw.rectangle((detection['startX'], detection['startY'], detection['endX'], detection['endY']), outline=color, width=width)
# cv2.rectangle(rect_img, (0, 0),
# (sub_img.shape[1]-int(width/2), sub_img.shape[0]-int(width/2)),
# color, width)
def draw_detections_cv2(self):
color = draw_colors[self.algorithm]
for detection in self.detections:
self.draw_detection(detection, color)
def draw_detection_cv2(self, detection, color=(0,0,255)):
# First we crop the sub-rect from the image
sub_img = self.visualisation[detection['startY']:detection['endY'], detection['startX']:detection['endX']]
rect_img = sub_img.copy()
width = 2
cv2.rectangle(rect_img, (0, 0),
(sub_img.shape[1]-int(width/2), sub_img.shape[0]-int(width/2)),
color, width)
# white_rect = np.ones(sub_img.shape, dtype=np.uint8) * 255
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if detection['confidence'] > self.confidence_threshold:
# draw the bounding box of the face along with the associated
# probability
text = "{:.2f}%".format(detection['confidence'] * 100)
y = detection['startY'] - 10 if detection['startY'] - 10 > 10 else detection['startY'] + 10
# cv2.rectangle(image, (startX, startY), (endX, endY),
# color, 2)
cv2.putText(self.visualisation, text, (detection['startX'], y),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2, lineType = cv2.LINE_AA)
alpha = 1
else:
# At least 10% opacity
alpha = max(.3, detection['confidence'])
res = cv2.addWeighted(sub_img, 1-alpha, rect_img, alpha, 1.0)
# Putting the image back to its position
self.visualisation[detection['startY']:detection['endY'], detection['startX']:detection['endX']] = res
def resize(self, width, height):
# TODO resize to new target incl all detections
img = self.visualisation
@ -107,9 +131,6 @@ class Result():
)
return result
def count_detections(self):
detections = [d for d in self.detections if d['confidence'] > self.confidence_threshold]
return len(detections)
def record(device_id, q1,q2, q3, q4, resolution, rotate):
@ -340,17 +361,7 @@ def process3_haar(in_q, out_q, cascade_file):
""")
dir_path = os.path.dirname(os.path.realpath(__file__))
lib_path = os.path.join(dir_path, "..", "visualhaar", "target", "debug")
so_path = os.path.join(lib_path, "libvisual_haarcascades_lib.so")
dll_path = os.path.join(lib_path, "visual_haarcascades_lib.dll")
if os.path.exists(so_path):
C = ffi.dlopen(so_path)
elif os.path.exists(dll_path):
C = ffi.dlopen(dll_path)
else:
raise RuntimeException("Visual haarcascades library is not found")
C = ffi.dlopen(os.path.join(dir_path,"../visualhaar/target/debug/libvisual_haarcascades_lib.so"))
# print(C.test(9))
# i = Image.open("Marjo.jpg")
@ -426,33 +437,11 @@ def process3_haar(in_q, out_q, cascade_file):
# print(img)
out_q.put(result)
def draw_stats(image, results):
pil_im = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(pil_im, 'RGBA')
for i, result in enumerate(results):
if result is None:
continue
c = result.count_detections()
txt = "face" if c == 1 else "faces"
txt = f"{result.algorithm.ljust(5)} {c} {txt}"
draw.text((10, pil_im.size[1] - i*25 - 50), txt, fill=draw_colors[result.algorithm], font=font_s, stroke_width=1, stroke_fill=(0,0,0))
return cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)
def display(image_res, q1, q2, q3, q4, fullscreen, output_dir):
logger = logging.getLogger('display')
empty_image = np.zeros((image_res[1],image_res[0],3), np.uint8)
results = [None, None, None]
result_queues = [q2, q3, q4]
images = [empty_image, empty_image, empty_image, empty_image]
override_image = None
override_until = None
def display(image_res, q1, q2, q3, q4, fullscreen = False):
prev_image1 = np.zeros((image_res[1],image_res[0],3), np.uint8)
prev_image2 = np.zeros((image_res[1],image_res[0],3), np.uint8)
prev_image3 = np.zeros((image_res[1],image_res[0],3), np.uint8)
prev_image4 = np.zeros((image_res[1],image_res[0],3), np.uint8)
if fullscreen:
cv2.namedWindow("output", cv2.WND_PROP_FULLSCREEN)
@ -461,68 +450,57 @@ def display(image_res, q1, q2, q3, q4, fullscreen, output_dir):
while True:
logging.debug('r')
try:
image = q1.get_nowait()
images[0] = cv2.resize(image, (image_res[0], image_res[1]))
image1 = q1.get_nowait()
image1 = cv2.resize(image1, (image_res[0], image_res[1]))
prev_image1 = image1
except Empty as e:
pass
for idx, queue in enumerate(result_queues):
try:
result = queue.get_nowait()
results[idx] = result.resize(image_res[0], image_res[1])
images[idx+1] = results[idx].draw_detections(include_title=True)
except Empty as e:
pass
finally:
pass
if override_image is not None and override_until > time.time():
cv2.imshow("output", override_image)
else:
override_image = None
image1 = prev_image1
try:
result2 = q2.get_nowait()
result2 = result2.resize(image_res[0], image_res[1])
result2.draw_detections()
image2 = result2.visualisation
# image2 = cv2.resize(image2, (image_res[0], image_res[1]))
prev_image2 = image2
except Empty as e:
image2 = prev_image2
try:
result3 = q3.get_nowait()
result3 = result3.resize(image_res[0], image_res[1])
result3.draw_detections()
image3 = result3.visualisation
# image3 = cv2.resize(image3, (image_res[0], image_res[1]))
prev_image3 = image3
except Empty as e:
image3 = prev_image3
try:
result4 = q4.get_nowait()
result4 = result4.resize(image_res[0], image_res[1])
result4.draw_detections()
image4 = result4.visualisation
# image4 = cv2.resize(image4, (image_res[0], image_res[1]))
prev_image4 = image4
except Empty as e:
image4 = prev_image4
images[0] = draw_stats(images[0], results)
img_concate_Verti1 = np.concatenate((images[0],images[1]),axis=0)
img_concate_Verti2 = np.concatenate((images[2],images[3]),axis=0)
grid_img = np.concatenate((img_concate_Verti1,img_concate_Verti2),axis=1)
cv2.imshow("output", grid_img)
img_concate_Verti1 = np.concatenate((image1,image2),axis=0)
img_concate_Verti2 = np.concatenate((image3,image4),axis=0)
grid_img = np.concatenate((img_concate_Verti1,img_concate_Verti2),axis=1)
cv2.imshow("output", grid_img)
# Hit 'q' on the keyboard to quit!
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
if key == ord(' '):
# TODO wait for frame to be processed. Eg. if I move and make a pic, it should use the last frame...
output_res = (image_res[0] *2, image_res[1] * 2)
pil_im = Image.fromarray(cv2.cvtColor(images[0], cv2.COLOR_BGR2RGB))
pil_im = pil_im.resize(output_res)
draw = ImageDraw.Draw(pil_im, 'RGBA')
for result in results:
if result is None:
continue
result.resize(output_res[0], output_res[1]).draw_detections_on(draw)
override_image = cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)
override_until = time.time() + 5
logger.info("Show frame until %f", override_until)
# TODO save frame
pass
# save images:
name = datetime.datetime.now().isoformat(timespec='seconds')
cv2.imwrite(os.path.join(output_dir, f'{name}.png'),override_image)
for result in results:
cv2.imwrite(os.path.join(output_dir, f'{name}-{result.algorithm}.png'),result.visualisation)
def main(camera_id, rotate, fullscreen, cascade_file, output_dir):
def main(camera_id, rotate, fullscreen, cascade_file):
image_size = (int(1920/2), int(1080/2))
if not os.path.exists(cascade_file):
raise RuntimeError(f"Cannot load OpenCV haar-cascade file '{cascade_file}'")
if not os.path.isdir(output_dir):
raise RuntimeError(f"Non-existent directory to store files '{output_dir}'")
is_rotated_90 = rotate in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]
@ -541,7 +519,7 @@ def main(camera_id, rotate, fullscreen, cascade_file, output_dir):
q_process3 = Queue(maxsize=1)
p1 = Process(target=record, args=(camera_id, q_webcam1, q_webcam2,q_webcam3,q_webcam4, image_size, rotate))
p2 = Process(target=display, args=(image_size, q_webcam1, q_process1, q_process2, q_process3, fullscreen, output_dir ))
p2 = Process(target=display, args=(image_size, q_webcam1, q_process1, q_process2, q_process3, fullscreen ))
p3 = Process(target=process1_hog, args=(q_webcam2, q_process1,))
p4 = Process(target=process2_dnn, args=(q_webcam3, q_process2,))
p5 = Process(target=process3_haar, args=(q_webcam4, q_process3,cascade_file))

4
mirror.py
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@ -15,8 +15,6 @@ if __name__ == '__main__':
help='Rotate counter clockwise')
parser.add_argument('--cascade', default='haarcascade_frontalface_alt2.xml',
help='Cascade XML file to use (opencv format)')
parser.add_argument('--output', default='saves',
help='Directory to store images (after pressing spacebar)')
args = parser.parse_args()
@ -26,4 +24,4 @@ if __name__ == '__main__':
if args.counter_clockwise:
rotate = cv2.ROTATE_90_COUNTERCLOCKWISE
face_recognition.comparison.main(args.camera, rotate, args.fullscreen, args.cascade, args.output)
face_recognition.comparison.main(args.camera, rotate, args.fullscreen, args.cascade)

3
requirements.txt
View file

@ -2,6 +2,3 @@ scipy
numpy
dlib
Pillow
opencv-python
cffi
scikit-image

2
visualhaar

@ -1 +1 @@
Subproject commit 928da82d24de1ae2cef268c140f9992b0614806b
Subproject commit 7de5440484842c147944ae123fa689333846dde7