不知道如何在这里问什么，因为我不知道问题所在。是 1( google.api_core。异常。权限被拒绝： 403 请求的身份验证范围不足。2( 在最后注释掉了 init((。我收到错误：rm：无法删除"tmp"：没有这样的文件或目录
Cleaning up...
whats here????
Finding people...
Traceback (most recent call last):
File "try-me.py", line 227, in <module>
timestamps_to_pull = analyze_labels(MOVIE_TO_PROCESS, BUCKET_NAME)
File "try-me.py", line 40, in analyze_labels
path, features=features, video_context=context)
File "/home/jackwhitely/.local/lib/python2.7/site-packages/google/cloud/videointelligence_v1/gapic/video_intelligence_service_client.py"
, line 202, in annotate_video
operation = self._annotate_video(request, retry=retry, timeout=timeout)
File "/home/jackwhitely/.local/lib/python2.7/site-packages/google/api_core/gapic_v1/method.py", line 139, in __call__
return wrapped_func(*args, **kwargs)
File "/home/jackwhitely/.local/lib/python2.7/site-packages/google/api_core/retry.py", line 260, in retry_wrapped_func
on_error=on_error,
File "/home/jackwhitely/.local/lib/python2.7/site-packages/google/api_core/retry.py", line 177, in retry_target
return target()
File "/home/jackwhitely/.local/lib/python2.7/site-packages/google/api_core/timeout.py", line 206, in func_with_timeout
return func(*args, **kwargs)
File "/home/jackwhitely/.local/lib/python2.7/site-packages/google/api_core/grpc_helpers.py", line 56, in error_remapped_callable
six.raise_from(exceptions.from_grpc_error(exc), exc)
File "/home/jackwhitely/.local/lib/python2.7/site-packages/six.py", line 737, in raise_from
raise value
google.api_core.exceptions.PermissionDenied: 403 Request had insufficient authentication scopes.
#--- The Original Code: http://amunategui.github.io/google-actor-recognition/index.html ---#
# --- Very Basic Instructions ---
# 1 - place a video clip in a bucket on your Google Cloud Storage and set permission to public
# 2 - run the code from the GCP cloud VM
# 3 - run the requirements.txt file (pip install -r requirements.txt)
# 4 - run video_processing.py clip_name bucket_name at the command prompt
# this will create tmp folder and under a series of folders including faces_found and text_found
# where it will store what it learned from your clip
# 5 - Don't forget to delete the clip (or remove public permission at the very least) and turn
# you VM off!
# If you have ffmpeg issues try this:
# sudo apt-get install ffmpeg
import glob, os, sys, io, skvideo.io, argparse, math, datetime, ffmpy, shutil, wikipedia
from google.cloud import videointelligence
from google.cloud import vision
from google.cloud import storage
from google.cloud.vision import types
from PIL import Image, ImageDraw
import numpy as np
def init():
# clean out directory structure
os.system('rm -r tmp')
def analyze_labels(movie_to_process, bucket_name):
path = 'gs://' + bucket_name + '/' + movie_to_process
""" Detects labels given a GCS path. """
video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.enums.Feature.LABEL_DETECTION]
mode = videointelligence.enums.LabelDetectionMode.SHOT_AND_FRAME_MODE
config = videointelligence.types.LabelDetectionConfig(
label_detection_mode=mode)
context = videointelligence.types.VideoContext(
label_detection_config=config)
operation = video_client.annotate_video(
path, features=features, video_context=context)
print('nProcessing video for label annotations:')
result = operation.result(timeout=90)
print('nFinished processing.')
frame_offsets = []
# Process frame level label annotations
frame_labels = result.annotation_results[0].frame_label_annotations
for i, frame_label in enumerate(frame_labels):
#if (frame_label.entity.description == 'person'):
print('Frame label description: {}'.format(
frame_label.entity.description))
for category_entity in frame_label.category_entities:
if (category_entity.description == 'person'):
print('tLabel category description: {}'.format(
category_entity.description))
print(frame_label)
# Each frame_label_annotation has many frames,
# here we print information only about the first frame.
#for frame in frame_label.frames:
frame = frame_label.frames[0]
time_offset = (frame.time_offset.seconds +
frame.time_offset.nanos / 1e9)
print('tFirst frame time offset: {}s'.format(time_offset))
print('tFirst frame confidence: {}'.format(frame.confidence))
print('n')
frame_offsets.append(time_offset)
return(sorted(set(frame_offsets)))
def extract_image_from_video(video_input, name_output, time_stamp):
ret = "Error"
try:
ret = os.system("ffmpeg -i " + video_input + " -ss " + time_stamp + " -frames:v 1 " + name_output)
# if all goes well FFMPEG will return 0
return ret
except ValueError:
return("Oops! error...")
def crop_image(input_image, output_image, start_x, start_y, width, height):
"""Pass input name image, output name image, x coordinate to start croping, y coordinate to start croping, width to crop, height to crop """
input_img = Image.open(input_image)
# give the image some buffer space
start_with_buffer_x = int(start_x - np.ceil(width/2))
start_with_buffer_y = int(start_y - np.ceil(height/2))
width_with_buffer = int(start_x + width + np.ceil(width/2))
height_with_buffer = int(start_y + height + np.ceil(height/2))
box = (start_with_buffer_x, start_with_buffer_y, width_with_buffer, height_with_buffer)
output_img = input_img.crop(box)
output_img.save(output_image +".png")
return (output_image +".png")
def detect_face(face_file, max_results=4):
# can you find a face and return coordinates
client = vision.ImageAnnotatorClient()
content = face_file.read()
image = types.Image(content=content)
# return coords of face
return client.face_detection(image=image).face_annotations
def highlight_faces(image, faces):
# Draws a polygon around the faces, then saves to output_filename.
faces_boxes = []
im = Image.open(image)
draw = ImageDraw.Draw(im)
for face in faces:
box = [(vertex.x, vertex.y)
for vertex in face.bounding_poly.vertices]
draw.line(box + [box[0]], width=5, fill='#00ff00')
faces_boxes.append([box[0][0], box[0][1], box[1][0] - box[0][0], box[3][1] - box[0][1]])
return (faces_boxes)
def annotate(path):
"""Returns web annotations given the path to an image."""
client = vision.ImageAnnotatorClient()
if path.startswith('http') or path.startswith('gs:'):
image = types.Image()
image.source.image_uri = path
else:
with io.open(path, 'rb') as image_file:
content = image_file.read()
image = types.Image(content=content)
web_detection = client.web_detection(image=image).web_detection
return web_detection
def report(annotations, max_report=5):
"""Prints detected features in the provided web annotations."""
names = []
if annotations.web_entities:
print ('n{} Web entities found: '.format(
len(annotations.web_entities)))
count = 0
for entity in annotations.web_entities:
print('Score : {}'.format(entity.score))
print('Description: {}'.format(entity.description))
names.append(entity.description)
count += 1
if count >=max_report:
break;
return names
def get_stills(movie_to_process, bucket_name, timestamps_to_pull):
video_location = 'https://storage.googleapis.com/' + bucket_name + '/' + movie_to_process
storage_client = storage.Client()
max_results = 3
timestamps_to_pull_tmp = timestamps_to_pull + [x + 0.15 for x in timestamps_to_pull[:-1]] + [x - 0.15 for x in timestamps_to_pull[1:]]
# clear out stills folder
if len(timestamps_to_pull_tmp) > 0:
# create directory structure
os.system('mkdir tmp')
os.system('mkdir tmp/faces_found')
os.system('mkdir tmp/text_found')
os.system('mkdir tmp/face_images')
filepath = 'tmp/'
# make stills
cnt_ = 0
for ttp in timestamps_to_pull_tmp:
# get the still image at that timestamp
time_stamp = str(datetime.timedelta(seconds=ttp))
file = "still_" + str(cnt_) + ".png"
filePathAndName = filepath + file
print('filename: ' + time_stamp)
ret = extract_image_from_video(video_input = video_location, name_output = filePathAndName, time_stamp = time_stamp)
cnt_ += 1
# find face on still image
with open(filePathAndName, 'rb') as image:
faces = detect_face(image, max_results)
print('Found {} face{}'.format(
len(faces), '' if len(faces) == 1 else 's'))
print('Looking for a face {}'.format(filePathAndName))
# Reset the file pointer, so we can read the file again
image.seek(0)
faces_boxes = highlight_faces(filePathAndName, faces) #, filePathAndName)
print('faces_boxes:', faces_boxes)
if len(faces_boxes) > 0:
# image had a face
count = 0
for face_box in faces_boxes:
# cv2.imwrite("frame%d.jpg" % count, image) # save frame as JPEG file
saved_name = crop_image(filePathAndName, "tmp/face_images/" + file.split('.')[0] + str(count) + '_faces', face_box[0], face_box[1], face_box[2], face_box[3])
count += 1
# get actors name
potential_names = report(annotate(saved_name),2)
print('potential_names: ', potential_names)
# does the first have two words - as in first and last name?
if (len(potential_names[0].split()) == 2):
# we have a winner
new_name = 'tmp/faces_found/' + potential_names[0] + '.png'
shutil.copy(saved_name,new_name)
# extract wiki bio
rez = wikipedia.page(potential_names[0]).content
# keep only intro paragraph
with open('tmp/text_found/' + potential_names[0] + ".txt", "w") as text_file:
text_file.write(rez.split('nn')[0] + " (Source: Wikipedia.com)")
BUCKET_NAME = ''
MOVIE_TO_PROCESS = ''
if __name__ == "__main__":
if len(sys.argv) == 3:
MOVIE_TO_PROCESS = sys.argv[1]
BUCKET_NAME = sys.argv[2]
# start things off clean
print('Cleaning up...')
print ('whats here????')
# init()
print('Finding people...')
# use video intelligence to find high probability of people being visible
timestamps_to_pull = analyze_labels(MOVIE_TO_PROCESS, BUCKET_NAME)
print('Processing people...')
get_stills(MOVIE_TO_PROCESS, BUCKET_NAME, timestamps_to_pull)
print('All done...')
else:
print('Wrong argument inputs')
针对每个要点的一些建议：
1.- 权限被拒绝：403 请求的身份验证范围不足。
第一步是确认您的 API、视频智能和视觉已启用。
如果错误仍然存在，您可以查看以下建议，具体取决于您对 Google Cloud 的访问权限进行身份验证的方式：
-
使用服务帐户。您需要在 JSON 文件中生成其凭据，然后必须GOOGLE_APPLICATION_CREDENTIALS设置环境变量。从代码中执行此操作的一种简单方法是：os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path/to/imgtotext.json">
-
使用 API 密钥。要使用此机制，您需要在代码中构造一个 REST 请求，因此必须调整代码。
2.- 无法删除"tmp"：没有这样的文件或目录
这是因为该命令在你执行 python 程序的同一目录中查找目录 tmp，确定它不存在。尝试使用绝对路径，例如/home/myuser/ml-cloud/tmp。