r/face_recognition
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flip image, change colours, add countdown, clickable previews

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Ruben van de Ven 2020-10-01 16:22:15 +02:00
parent a7ea09fb52
commit 8b64e8301b
4 changed files with 150 additions and 21 deletions
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26
.vscode/launch.json vendored Normal file
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@ -0,0 +1,26 @@
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Python: Current File",
"type": "python",
"request": "launch",
"program": "${file}",
"console": "integratedTerminal"
},
{
"name": "Python: Mirror",
"type": "python",
"request": "launch",
"program": "mirror.py",
"args": [
// "--fullscreen",
"--camera", "2",
],
"console": "integratedTerminal"
}
]
}

31
README.md Normal file
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A `mirror` which shows which faces are detected through three different facial detection algorithms:
* OpenCV's deep neural net [face detector](https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector).
* Dlib's default frontal face detector, which is HOG based
* A Viola-Jones Haarcascade detection. Any OpenCV compatible xml file should work. It defaults to the canonical `haarcascade_frontalface_alt2.xml`.
# Installation
## on windows
The installation in Windows can be done, though it is quite elaborate:
* Install rustup-init
* Install VS C++
* Install python3
* Install Cmake (needed for python dlib)
+ make sure to add it to path
* Install git
+ including ssh deploy key
* `git clone https://git.rubenvandeven.com/r/face_detector`
* `cd face_recognition`
* `git submodules init`
* `git submodules update`
* `pip install virtualenv`
* `virtualenv.exe venv`
* `.\venv\Scripts\activate`
* `cd .\dnn\face_detector`
* `python.exe .\download_weights.py`
* `cd .\visualhaar`
* `cargo build --lib`

106
face_recognition/comparison.py
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@ -5,14 +5,15 @@ import logging
import argparse
import numpy as np
import time
import math
import datetime
from PIL import ImageFont, ImageDraw, Image
import os
draw_colors = {
'hog': (198,65,124),
'hog': (255,255,255), #(198,65,124),
'haar': (255,255,255),
'dnn': (251,212,36),
'dnn': (255,255,255) #(251,212,36),
}
titles = {
@ -25,6 +26,7 @@ fontfile = "SourceSansPro-Regular.ttf"
font = ImageFont.truetype(fontfile, 30)
font_s = ImageFont.truetype(fontfile, 20)
countdown_font = ImageFont.truetype(fontfile, 160)
class Result():
def __init__(self, algorithm, image, confidence_threshold = 0.5):
@ -80,7 +82,7 @@ class Result():
alpha = 1
else:
# At least 10% opacity
alpha = max(.3, detection['confidence'])
alpha = max(.2, detection['confidence'])
color = list(color)
color.append(int(alpha*255))
@ -120,6 +122,8 @@ def record(device_id, q1,q2, q3, q4, resolution, rotate):
capture.set(cv2.CAP_PROP_FRAME_WIDTH, resolution[1] if is_rotated_90 else resolution[0])
capture.set(cv2.CAP_PROP_FRAME_HEIGHT, resolution[0] if is_rotated_90 else resolution[1])
gave_camera_warning = False
while True:
ret, image = capture.read()
if image is None:
@ -130,9 +134,13 @@ def record(device_id, q1,q2, q3, q4, resolution, rotate):
if rotate is not None:
image = cv2.rotate(image, rotate)
# Flip image to create the 'mirror' effect.
image = cv2.flip(image, 1)
# print(image.shape[:2], image.shape[1::-1])
if image.shape[1::-1] != resolution:
if image.shape[1::-1] != resolution and not gave_camera_warning:
logging.warning(f"Camera resultion seems wrong: {image.shape[:2]} instead of {resolution}")
gave_camera_warning = True
try:
q1.put_nowait(image)
@ -283,7 +291,7 @@ def process2_dnn(in_q, out_q):
prototxt = "dnn/face_detector/opencv_face_detector.pbtxt"
prototxt = "dnn/face_detector/deploy.prototxt"
model = "dnn/face_detector/res10_300x300_ssd_iter_140000_fp16.caffemodel"
confidence_threshold = 0.5
confidence_threshold = 0.7
logger.info("[INFO] loding model...")
net = cv2.dnn.readNetFromCaffe(prototxt, model)
@ -371,7 +379,7 @@ def process3_haar(in_q, out_q, cascade_file):
frame = in_q.get()
(height_orig, width_orig) = frame.shape[:2]
scale_factor = 3
scale_factor = 4
width = int(width_orig/scale_factor)
height = int(height_orig/scale_factor)
@ -426,7 +434,7 @@ def process3_haar(in_q, out_q, cascade_file):
# print(img)
out_q.put(result)
def draw_stats(image, results):
def draw_stats(image, results, padding):
pil_im = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(pil_im, 'RGBA')
@ -437,7 +445,8 @@ def draw_stats(image, results):
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))
height = padding + 25
draw.text((padding, pil_im.size[1] - i*height - height), 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)
@ -447,19 +456,50 @@ def display(image_res, q1, q2, q3, q4, fullscreen, output_dir):
empty_image = np.zeros((image_res[1],image_res[0],3), np.uint8)
image_ratio = image_res[0] / image_res[1]
results = [None, None, None]
result_queues = [q2, q3, q4]
images = [empty_image, empty_image, empty_image, empty_image]
override_image = None
override_until = None
countdown_until = None
# imageIdx = 0
# grid in the right corner
preview_scale = 10
preview_width = round(image_res[0] / preview_scale)
preview_height = round(preview_width / image_ratio)
padding = round(image_res[0] / 100)
if fullscreen:
cv2.namedWindow("output", cv2.WND_PROP_FULLSCREEN)
cv2.namedWindow("output", cv2.WINDOW_NORMAL)
cv2.setWindowProperty("output",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
else:
cv2.namedWindow("output", cv2.WINDOW_AUTOSIZE)
def selectPreview(event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONDOWN:
if x > image_res[0] - padding or x < image_res[0] - padding - preview_width:
return
preview_images = [idx for idx,image in enumerate(images) if idx != selectPreview.imageIdx]
for offset, image_nr in enumerate(preview_images):
offset_y = (preview_height + padding) * offset
# print(offset, y, image_res[0] - padding - preview_height - offset_y, image_res[0] - padding - offset_y)
if y > image_res[1] - padding - preview_height - offset_y and y < image_res[1] - padding - offset_y:
selectPreview.imageIdx = image_nr
print("Select image", offset, image_nr)
break
selectPreview.imageIdx = 0
cv2.setMouseCallback('output', selectPreview)
while True:
logging.debug('r')
try:
image = q1.get_nowait()
images[0] = cv2.resize(image, (image_res[0], image_res[1]))
@ -481,20 +521,50 @@ def display(image_res, q1, q2, q3, q4, fullscreen, output_dir):
else:
override_image = None
images[0] = draw_stats(images[0], results)
# 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)
# show the selected image:
grid_img = images[selectPreview.imageIdx].copy()
# previews in the right bottom corner
preview_images = [image for idx,image in enumerate(images) if idx != selectPreview.imageIdx]
for idx, image in enumerate(preview_images):
offset_y = (preview_height + padding) * idx
grid_img[
grid_img.shape[0] - padding - preview_height - offset_y:grid_img.shape[0] - padding - offset_y,
grid_img.shape[1] - padding - preview_width:grid_img.shape[1] - padding] = cv2.resize(image, (preview_width, preview_height), cv2.INTER_CUBIC)
# statistics
grid_img = draw_stats(grid_img, results, padding)
pil_im = Image.fromarray(cv2.cvtColor(grid_img, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(pil_im, 'RGBA')
# Draw countdown
if countdown_until:
duration = math.ceil(countdown_until - time.time())
w, h = draw.textsize(f"{duration}", font=countdown_font)
draw.text(((grid_img.shape[1]-w)/2,(grid_img.shape[0]-h)/2), f"{duration}", fill="white", stroke="black", font=countdown_font)
grid_img = cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)
# 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)
# Hit 'q' on the keyboard to quit!
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
if key == ord(' '):
if key == ord(' ') and not override_image:
countdown_until = time.time() + 3 # seconds of countdown
if countdown_until is not None and time.time() > countdown_until:
countdown_until = None
# 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)
# SNAP!
# output_res = (image_res[0] *2, image_res[1] * 2)
output_res = image_res # no scaling needed anyore
pil_im = Image.fromarray(cv2.cvtColor(images[0], cv2.COLOR_BGR2RGB))
pil_im = pil_im.resize(output_res)
draw = ImageDraw.Draw(pil_im, 'RGBA')
@ -516,8 +586,10 @@ def display(image_res, q1, q2, q3, q4, fullscreen, output_dir):
cv2.imwrite(os.path.join(output_dir, f'{name}-{result.algorithm}.png'),result.visualisation)
def main(camera_id, rotate, fullscreen, cascade_file, output_dir):
image_size = (int(1920/2), int(1080/2))
image_size = (1920, 1080) #(int(1920/2), int(1080/2))
if not os.path.exists(cascade_file):
raise RuntimeError(f"Cannot load OpenCV haar-cascade file '{cascade_file}'")

2
visualhaar

@ -1 +1 @@
Subproject commit 928da82d24de1ae2cef268c140f9992b0614806b
Subproject commit 7c7ae29bf9e1390ea304e3708e8f92f6d57f87ff