可执行文件在 Python 中给出“无法执行”脚本

如何解决可执行文件在 Python 中给出“无法执行”脚本

我使用 ageitgey repo 作为实现 face recognition python 库的参考。这段代码运行得很好,但是在制作可执行文件时,它的抛出错误为无法执行脚本 MainFR。

MainFR.py 是我的主要 python 文件。

这是我正在使用的代码。

import json
import os
import cv2
import face_recognition
import pandas as pd
import numpy as np
from PIL import Image
from io import BytesIO
from simple_salesforce import Salesforce,SalesforceLogin,SFType

----------
code to connect salesforce and fetching images
----------

# In lstRecords1 I am fetching images from 3rd party system(salesforce) and the data is coming
attachs = pd.DataFrame(lstRecords1)

insname = sf.sf_instance
folpath = '.\Attachments Download'
for row in attachs.iterrows():
    recid = row[1]['ParentId']
    fname = row[1]['Name']
    atturl = row[1]['Body']
    if not os.path.exists(os.path.join(folpath)):
        os.mkdir(os.path.join(folpath))
    request = sf.session.get('https://{0}{1}'.format(insname,atturl),headers = sf.headers)
    with open(os.path.join(folpath,fname),'wb') as f:
        f.write(request.content)
        #f.close()

video_capture = cv2.VideoCapture(0)
obama_photo = face_recognition.load_image_file(os.path.abspath("Attachments Download/Obama.jpg"))
obama_face_encoding = face_recognition.face_encodings(Obama_photo)[0]

arnie_image = face_recognition.load_image_file(os.path.abspath("Attachments Download/arnie1.jpg"))
arnie_face_encoding = face_recognition.face_encodings(arnie_image)[0]

known_face_encodings = [
    arnie_face_encoding,Obama_face_encoding
]
known_face_names = [
    "Arnold","Obama"
]

face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

while True:
    # Grab a single frame of video
    ret,frame = video_capture.read()

# 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)
super_small_frame = cv2.resize(frame,fx=0.12,fy=0.12)

# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
#rgb_small_frame = small_frame[:,:,::-1]
rgb_small_frame = super_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_locations = face_recognition.face_locations(rgb_small_frame)
    face_encodings = face_recognition.face_encodings(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]
            print('result is :::',name)
            #os._exit(0)

        # 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


# 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),255),2)

    # Draw a label with a name below the face
    cv2.rectangle(frame,bottom - 35),cv2.FILLED)
    font = cv2.FONT_HERSHEY_DUPLEX
    cv2.putText(frame,name,(left + 6,bottom - 6),font,1.0,(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

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()

谁能建议我在这里做什么?我被困在这将近 2 周

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