这实际上不是我的剧本，但经过与创作者的大量反复，他和我一样难倒，所以我代表他发帖，因为他无法复制问题并进行调试。
该脚本名为faceripper9000（基于GetFaces），它应该占用一个充满MP4的目录和一个目标人物的照片，然后输出它们出现在剪辑中的每一帧。
您可以看到脚本here，但我也会将其添加到此帖的底部。
Dependancies是numpy，opencv，dlib和face_recognition，所有这些都可以正确安装，但是当我们其他人运行脚本时，我们收到了这条消息：
File "demo.py", line 161, in
os.rename(i, too_small + "/too small-" + str(counter) + random_string(15) + ".jpg")
NameError: name 'random_string' is not defined
我们可以通过将print ("Target image loaded" + target_image)替换为print ("Target image loaded" + str(target_image))来解决这个问题，但是当我们再次运行它时，脚本会开始加载目标图像，然后在打印出这样的数字数组之后停止几行：
[ INFO:0] Initialize OpenCL runtime...
Using OpenCL: True.
Output directory: /Users/example/Development/deep/pic/pic_output.
Scanned videos will be moved to: /Users/example/Development/deep/pic/pic_scanned_vids.
Target image loaded[[[199 196 191]
[199 196 191]
[199 196 191]
...
[184 180 177]
[184 180 177]
[184 180 177]]
[[199 196 191]
[199 196 191]
[199 196 191]
...
[184 180 177]
[184 180 177]
[184 180 177]]
[[199 196 191]
[199 196 191]
[200 197 192]
...
[184 180 177]
[184 180 177]
[184 180 177]]
...
[[166 166 166]
[164 164 164]
[165 165 167]
...
[148 149 151]
[146 148 145]
[146 147 142]]
[[166 166 166]
[164 164 164]
[165 165 167]
...
[149 150 152]
[146 148 145]
[146 147 142]]
[[166 166 166]
[164 164 164]
[165 165 167]
...
[150 151 153]
[146 148 145]
[146 147 142]]]
我已经尝试过几个不同版本的python，但似乎没有任何改变。
这是脚本：
import face_recognition
import numpy as np
import cv2
import glob
import random
import string
import os
import math
import argparse
os.system('cls' if os.name=='nt' else 'clear')
parser = argparse.ArgumentParser();
parser.add_argument('-i', type=str, help='Image of target face to scan for.', required=True)
parser.add_argument('-v', type=str, help='Video to process', required=True)
parser.add_argument('-t', type=float, help='Tolerance of face detection, lower is stricter. (0.1-1.0)', default=0.6)
parser.add_argument('-f', type=int, help='Amount of frames per second to extract.', default=25)
parser.add_argument('-n', type=int, help='Number of frames with target face to save from each vid.', default=1000)
parser.add_argument('-s', type=int, help='Minimum KB size of images to keep in the faceset.', default=32)
args = vars(parser.parse_args())
if args['t'] > 1.0:
args['t'] = 1.0
elif args['t'] < 0.1:
args['t'] = 0.1
min_KB = args['s']
tol = args['t']
xfps = args['f']
targfname = args['i']
vid_dir = args['v']
faces_from_each_video = args['n']
if faces_from_each_video < 1:
faces_from_each_video = 1000
if min_KB < 1:
min_KB = 32
print("Target filename: " + targfname + ".")
print("Video input directory: " + vid_dir + ".")
print("Tolerance: " + str(tol) + ".")
print("Number of confirmed faces saved from each video: " + str(faces_from_each_video) + ".")
if(cv2.ocl.haveOpenCL()):
cv2.ocl.setUseOpenCL(True)
print("Using OpenCL: " + str(cv2.ocl.useOpenCL()) + ".")
target_image = face_recognition.load_image_file(targfname)
outdir = str(str(os.path.splitext(targfname)[0]) + "_output");
scanned_vids = str(str(os.path.splitext(targfname)[0]) + "_scanned_vids");
too_small = str(str(os.path.splitext(targfname)[0]) + "_too_small");
#check if output directories already exists, and if not, create it
os.makedirs(outdir, exist_ok=True)
os.makedirs(scanned_vids, exist_ok=True)
os.makedirs(too_small, exist_ok=True)
print("Output directory: " + outdir + ".")
print("Scanned videos will be moved to: " + scanned_vids + ".")
try:
print ("Target image loaded" + str(target_image))
target_encoding = face_recognition.face_encodings(target_image)[0]
except IndexError:
print("No face found in target image.")
raise SystemExit(0)
vid = True
while(vid):
try:
vid = random.choice(glob.glob(vid_dir + '*.mp4'))
print("Now looking at video: " + vid)
input_video = cv2.VideoCapture(vid)
framenum = 0
vidheight = input_video.get(4)
vidwidth = input_video.get(3)
vidfps = input_video.get(cv2.CAP_PROP_FPS)
totalframes = input_video.get(cv2.CAP_PROP_FRAME_COUNT)
outputsize = 256, 256
if xfps > vidfps:
xfps = vidfps
print("Frame Width: " + str(vidwidth) + ", Height: " + str(vidheight) + ".")
known_faces = [
target_encoding
]
def random_string(length):
return ''.join(random.choice(string.ascii_letters) for m in range(length))
#switch to output directory
os.chdir(str(os.path.splitext(targfname)[0]) + "_output")
written = 1
while(input_video.isOpened()):
input_video.set(1, (framenum + (vidfps/xfps)))
framenum += vidfps/xfps
ret, frame = input_video.read()
if not ret:
break
percentage = (framenum/totalframes)*100
print("Checking frame " + str(int(framenum)) + "/" + str(int(totalframes)) + str(" (%.2f%%)" % percentage))
rgb_frame = frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
for fenc, floc in zip(face_encodings, face_locations):
istarget = face_recognition.compare_faces(known_faces, fenc, tolerance=float(tol))
#if the face found matches the target
if istarget[0]:
top, right, bottom, left = floc
facefound = True
#squaring it up
if (bottom - top) > (right - left):
right = left + (bottom - top)
elif (right - left) > (bottom - top):
bottom = top + (right - left)
#calculating the diagonal of the cropped face for rotation purposes
#diagonal = math.sqrt(2*(bottom - top))
#padding = diagonal / 2
#alignment script causes images cropped "too closely" to get a bit fucky, so crop them less severely.
padding = (bottom - top)/2
if((top - padding >= 0) and (bottom + padding <= vidheight) and (left - padding >= 0) and (right + padding <= vidwidth)):
croppedframe = frame[int(top - padding):int(bottom + padding), int(left - padding):int(right + padding)]
#if the image is too small, resize it to outputsize
cheight, cwidth, cchannels = croppedframe.shape
if (cheight < 256) or (cwidth < 256):
croppedframe = cv2.resize(croppedframe, outputsize, interpolation=cv2.INTER_CUBIC)
print('Writing image ' + str(written) + '.')
cv2.imwrite(("vid_" + str(zz) + random_string(15) + ".jpg"), croppedframe, [int(cv2.IMWRITE_JPEG_QUALITY), 98])
written += 1
if percentage > 99.9:
os.rename(vid, scanned_vids + '/vid' + str(zz) + '_' + random_string(5) + '.mp4')
break
if written > faces_from_each_video:
os.rename(vid, scanned_vids + '/vid' + str(zz) + '_' + random_string(5) + '.mp4')
break
input_video.release()
except ValueError:
print ("Scanning videos complete.")
pass
except IndexError:
pass
#Removes images under 32KB
counter = 0
low_quat = min_KB * 1000
for xx in (os.listdir(os.getcwd())):
if(os.path.getsize(xx)) < low_quat:
os.rename(xx, too_small + "/too small-" + str(counter) + random_string(15) + ".jpg")
print ("Moving " + str(xx) + " to the too small folder")
counter += 1
#Remove images with more than one face
print ("Now double checking there is only one face in each photo")
for yy in (os.listdir(os.getcwd())):
# Load the jpg file into a numpy array
image = face_recognition.load_image_file(yy)
# Find all the faces in the image using a pre-trained convolutional neural network.
# This method is more accurate than the default HOG model, but it's slower
# unless you have an nvidia GPU and dlib compiled with CUDA extensions. But if you do,
# this will use GPU acceleration and perform well.
# See also: find_faces_in_picture.py
face_locations = face_recognition.face_locations(image, number_of_times_to_upsample=0, model="cnn")
print("I found {} face(s) in this photograph.".format(len(face_locations)))
if not (len(face_locations)) == 1:
os.remove(yy)
print (str(yy) + ' was removed')
我甚至不知道从哪里开始尝试解决这个问题，所以任何帮助都会非常感激！
答案 0 :(得分：1)
我们可以看到print ("Target image loaded" + str(target_image))正在执行，因为打印了Target image loaded，如果打印出来，那么str(target_image)也是如此，所以接下来必须是这样。 `[[[199 196 191] ...看起来像一个RGB三元组的二维数组。
也许该程序是打印目标图像的名称，而不是其内容。