-
-
Save ageitgey/1c1cb1c60ace321868f7410d48c228e1 to your computer and use it in GitHub Desktop.
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() |
from skimage.feature import hog
from skimage import exposure
import cv2
image = cv2.imread('/your/image/path')
fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualize=True, multichannel=True)
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, 10))
ax2.axis('off')
ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)
ax2.set_title('Histogram of Oriented Gradients')
plt.show()
from skimage.feature import hog from skimage import exposure import cv2 image = cv2.imread('/your/image/path') fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualize=True, multichannel=True) 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, 10)) ax2.axis('off') ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray) ax2.set_title('Histogram of Oriented Gradients') plt.show()
Hi, you forgot to add: "from matplotlib import pyplot as plt".
@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()
If you're using OpenCV use this function
import cv2
v1 = cv2.VideoCapture(0) # 0 -- indicates your webcam
print(v1.isOpened()) # Returns bool value based on the video capture object