Hi I try to connect OpenCV, Face recognition Library with my RSTP CCTV. But when i run my code i getting an error like below. Hope someone can help me regarding on this issue. I attach here my code
Error:
[h264 @ 000002a9659dba00] error while decoding MB 10 94, bytestream -5 [h264 @ 000002a953c1e2c0] Invalid NAL unit 8, skipping. Traceback (most recent call last): File "face-present.py", line 125, in cv2.imshow('Video', frame) cv2.error: OpenCV(4.0.0) C:\projects\opencv-python\opencv\modules\highgui\src\window.cpp:350: error: (-215:Assertion failed) size.width>0 && size.height>0 in function 'cv::imshow'
mycode.py
import face_recognition
import cv2
video_capture = cv2.VideoCapture("rtsp://admin:adam12345@192.168.0.158:554/Streaming/channels/101")
roy_image = face_recognition.load_image_file("images/roy1.jpg")
roy_face_encoding = face_recognition.face_encodings(roy_image,num_jitters=100)[0]
# Load a second sample picture and learn how to recognize it.
henrik_image = face_recognition.load_image_file("images/Mr_henrik.jpg")
henrik_face_encoding = face_recognition.face_encodings(henrik_image,num_jitters=100)[0]
stefan_image = face_recognition.load_image_file("images/stefan.jpg")
stefan_face_encoding = face_recognition.face_encodings(stefan_image,num_jitters=100)[0]
hairi_image = face_recognition.load_image_file("images/Hairi.jpeg")
hairi_face_encoding = face_recognition.face_encodings(hairi_image,num_jitters=100)[0]
syam_image = face_recognition.load_image_file("images/syam1.jpeg")
syam_face_encoding = face_recognition.face_encodings(syam_image,num_jitters=100)[0]
#print(syam_face_encoding)
# Create arrays of known face encodings and their names
known_face_encodings = [
roy_face_encoding,
stefan_face_encoding,
henrik_face_encoding,
hairi_face_encoding,
syam_face_encoding
]
known_face_names = [
"roy",
"stefan",
"henrik",
"hairi",
"syam"
]
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
# # Process video frame frequency
# process_frame_freq = 4
# process_this_frame = process_frame_freq
while True:
if video_capture.isOpened():
# Grab a single frame of video
ret, frame = video_capture.read()
if ret:
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, None, 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_locations = face_recognition.face_locations(rgb_small_frame)
if face_locations: # prevent manipulation of null variable
top, right, bottom, left = face_locations[0]
# faces_recognized += 1
# print("[%i] Face recognized..." % faces_recognized)
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
cropped_face = frame[top:bottom, left:right]
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
#print(face_encodings)
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,tolerance=0.5)
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(name)
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), (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 + 6, bottom - 6), 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
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()