我想创建一个应用程序，在鱼眼镜头中检测到人脸，但是我不知道如何将其压缩到鱼眼镜头中，但是在普通网络摄像头中它可以检测到人脸，我厌倦了各种编辑方式，例如编辑人脸中的点和甚至无法打印下面我脸上的点是源代码
import face_recognition
import cv2
import numpy as np
import dlib
# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
# 1. Process each video frame at 1/4 resolution (though still display it at full resolution)
# 2. Only detect faces in every other frame of video.
# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("obama.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
# Load a second sample picture and learn how to recognize it.
biden_image = face_recognition.load_image_file("biden.jpg")
biden_face_encoding = face_recognition.face_encodings(biden_image)[0]
# Load a Third sample picture and learn how to recognize it.
Logesh_image = face_recognition.load_image_file("Upside Logesh.jpg")
Logesh_face_encoding = face_recognition.face_encodings(Logesh_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
obama_face_encoding,
biden_face_encoding,
Logesh_face_encoding
]
known_face_names = [
"Barack Obama",
"Joe Biden",
"Logesh"
]
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
face_position = []
process_this_frame = True
face_landmarks = []
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
#Postion Frame
Direction_frame = cv2.resize(frame, (50, 50), fx=1.50, fy=1.50)
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_position = face_recognition.face_landmarks(rgb_small_frame, face_locations)
face_position = face_recognition.face_landmarks(rgb_small_frame, face_locations)
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_landmarks = face_recognition.face_landmarks(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# # If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
face_names.append(name)
process_this_frame = not process_this_frame
def rect_to_bb(rect):
# take a bounding predicted by dlib and convert it
# to the format (x, y, w, h) as we would normally do
# with OpenCV
x = rect.left()
y = rect.top()
w = rect.right() - x
h = rect.bottom() - y
# return a tuple of (x, y, w, h)
return x, y, w, h
# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 10, bottom - 6), font, 1.0, (255, 255, 255), 1)
# Movement of a person
if right < 448:
Right_Command = "You are in the right side"
cv2.putText(frame, Right_Command, (left - 100, bottom - 300), font, 1.0, (255, 255, 255), 1)
if left > 928:
Left_Command = "You are in the left side"
cv2.putText(frame, Left_Command, (left - 100, bottom - 300), font, 1.0, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap = cv2.VideoCapture(0)
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
while True:
_, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = detector(gray)
for face in faces:
x1 = face.left()
y1 = face.top()
x2 = face.right()
y2 = face.bottom()
#cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 3)
landmarks = predictor(gray, face)
for n in range(0, 68):
x = landmarks.part(n).x
y = landmarks.part(n).y
cv2.circle(frame, (x, y), 4, (255, 0, 0), -1)
cv2.imshow("Frame", frame)
key = cv2.waitKey(1)
if key == 27:
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()