Hello everyone i have a python script that has dependency on dlib such as import dlib now i have created an executable out of it (using pyinstaller) and it works fine on my machine but gives ImportError: DLL load failed: A dynamic link library (DLL) initialization routine failed on another machine. and after digging out the line at which this occurs is basically importing dlib which makes me think dlib is not getting properly included in my executable. My dlib version 19.18.0 and the other machine i am trying to run exe on does'nt have python installed.Need help Error on another machine
F:\FaceRecogDemo\FaceRecogDemo\dist>recognizefaces.exe --debug --encodings ../encodings.pickle --image ../example1.jpg
Traceback (most recent call last):
File "D:\FaceRecogDemo\recognizefaces.py", line 2, in <module>
File "c:\programdata\anaconda3\envs\mywindowscv\lib\site-packages\PyInstaller\loader\pyimod03_importers.py", line 627, in exec_module
File "D:\FaceRecogDemo\face_recognition\__init__.py", line 7, in <module>
File "c:\programdata\anaconda3\envs\mywindowscv\lib\site-packages\PyInstaller\loader\pyimod03_importers.py", line 627, in exec_module
File "D:\FaceRecogDemo\face_recognition\api.py", line 4, in <module>
ImportError: DLL load failed: A dynamic link library (DLL) initialization routine failed.
[14720] Failed to execute script recognizefaces
My recognizefaces.py script
import face_recognition
import argparse
import pickle
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-e", "--encodings", required=True,
help="path to serialized db of facial encodings")
ap.add_argument("-i", "--image", required=True,
help="path to input image")
ap.add_argument("-d", "--detection-method", type=str, default="cnn",
help="face detection model to use: either `hog` or `cnn`")
args = vars(ap.parse_args())
# load the known faces and embeddings
print("[INFO] loading encodings...")
data = pickle.loads(open(args["encodings"], "rb").read())
# load the input image and convert it from BGR to RGB
image = cv2.imread(args["image"])
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# detect the (x, y)-coordinates of the bounding boxes corresponding
# to each face in the input image, then compute the facial embeddings
# for each face
print("[INFO] recognizing faces...")
boxes = face_recognition.face_locations(rgb,
model=args["detection_method"])
encodings = face_recognition.face_encodings(rgb, boxes)
# initialize the list of names for each face detected
names = []
# loop over the facial embeddings
for encoding in encodings:
# attempt to match each face in the input image to our known
# encodings
matches = face_recognition.compare_faces(data["encodings"],
encoding)
name = "Unknown"
# check to see if we have found a match
if True in matches:
# find the indexes of all matched faces then initialize a
# dictionary to count the total number of times each face
# was matched
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}
# loop over the matched indexes and maintain a count for
# each recognized face face
for i in matchedIdxs:
name = data["names"][i]
counts[name] = counts.get(name, 0) + 1
# determine the recognized face with the largest number of
# votes (note: in the event of an unlikely tie Python will
# select first entry in the dictionary)
name = max(counts, key=counts.get)
# update the list of names
names.append(name)
print(names)
Both my machines have windows 10 OS
