step-1_find-faces.py

import sys
import dlib
from skimage import io

# Take the image file name from the command line
file_name = sys.argv[1]

# Create a HOG face detector using the built-in dlib class
face_detector = dlib.get_frontal_face_detector()

win = dlib.image_window()

# Load the image into an array
image = io.imread(file_name)

# Run the HOG face detector on the image data.
# The result will be the bounding boxes of the faces in our image.
detected_faces = face_detector(image, 1)

print("I found {} faces in the file {}".format(len(detected_faces), file_name))

# Open a window on the desktop showing the image
win.set_image(image)

# Loop through each face we found in the image
for i, face_rect in enumerate(detected_faces):

	# Detected faces are returned as an object with the coordinates 
	# of the top, left, right and bottom edges
	print("- Face #{} found at Left: {} Top: {} Right: {} Bottom: {}".format(i, face_rect.left(), face_rect.top(), face_rect.right(), face_rect.bottom()))

	# Draw a box around each face we found
	win.add_overlay(face_rect)
	        
# Wait until the user hits <enter> to close the window	        
dlib.hit_enter_to_continue()

@Dankrou yeah it was on a different kernel but thanks for pointing that out.

import os
import sys
import dlib
from skimage import io, exposure
from skimage.feature import hog
import cv2
import matplotlib.pyplot as plt

main = os.path.abspath(sys.argv[0])
file_name = os.path.abspath(os.path.join(os.path.dirname(main), "examples","faces","2.jpg"))

Contruye histograma de un rostros para identificación

image = cv2.imread(file_name)
fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualize=True, channel_axis=2)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)

ax1.axis('off')
ax1.imshow(image, cmap=plt.cm.gray)
ax1.set_title('Input image')

Rescale histogram for better display

hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 20))

ax2.axis('off')
ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)
ax2.set_title('Histogram of Oriented Gradients')
plt.show()

Its working.

…

On Fri, 26 May 2023, 17:18 Daruin Herrera, ***@***.***> wrote: ***@***.**** commented on this gist. ------------------------------ import os import sys import dlib from skimage import io, exposure from skimage.feature import hog import cv2 import matplotlib.pyplot as plt main = os.path.abspath(sys.argv[0]) file_name = os.path.abspath(os.path.join(os.path.dirname(main), "examples","faces","2.jpg")) Contruye histograma de un rostros para identificación image = cv2.imread(file_name) fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualize=True, channel_axis=2) fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True) ax1.axis('off') ax1.imshow(image, cmap=plt.cm.gray) ax1.set_title('Input image') Rescale histogram for better display hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 20)) ax2.axis('off') ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray) ax2.set_title('Histogram of Oriented Gradients') plt.show() — Reply to this email directly, view it on GitHub <https://gist.github.com/ageitgey/1c1cb1c60ace321868f7410d48c228e1#gistcomment-4580744> or unsubscribe <https://github.com/notifications/unsubscribe-auth/AE5NYIOG5RA6PNC4JJ6W4O3XIDCUDBFKMF2HI4TJMJ2XIZLTSKBKK5TBNR2WLJDHNFZXJJDOMFWWLK3UNBZGKYLEL52HS4DFQKSXMYLMOVS2I5DSOVS2I3TBNVS3W5DIOJSWCZC7OBQXE5DJMNUXAYLOORPWCY3UNF3GS5DZVRZXKYTKMVRXIX3UPFYGLK2HNFZXIQ3PNVWWK3TUUZ2G64DJMNZZDAVEOR4XAZNEM5UXG5FFOZQWY5LFVAZTQMJQGQ4TQMFHORZGSZ3HMVZKMY3SMVQXIZI> . You are receiving this email because you commented on the thread. Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub> .