Error loading images or encoding faces: Unsupported image type, must be 8bit gray or RGB image.

[nhattym]

• face_recognition version:
• Python version:
• Operating System:

Description

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Tell us what happened, what went wrong, and what you expected to happen.
IMPORTANT: If your issue is related to a specific picture, include it so others can reproduce the issue.

What I Did

Paste the command(s) you ran and the output.
If there was a crash, please include the traceback here.

[mWhrerttttt]

import face_recognition
import cv2

video_capture = cv2.VideoCapture(0)

Load a sample picture and learn how to recognize it.

obama_image = face_recognition.load_image_file('picther\elonmask.jpg')
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]

Load a second sample picture and learn how to recognize it.

biden_image = face_recognition.load_image_file("picther/mohamm.jpg")
biden_face_encoding = face_recognition.face_encodings(biden_image)[0]

Create arrays of known face encodings and their names

known_face_encodings = [
obama_face_encoding,
biden_face_encoding
]
known_face_names = [
"omar",
"Mohamed"
]

Initialize some variables

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

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

# Only process every other frame of video to save time
if process_this_frame:
    # Resize frame of video to 1/4 size for faster face recognition processing

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_small_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    
    # 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(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,0.6)
        name = "Unknown"

        print(matches)
        # # 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]

        # # 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]
        #     print(name)

        face_names.append(name)

process_this_frame = not process_this_frame
clr=(0, 0, 255)
if True in matches:
    clr=(0, 255, 0)

# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
    # Draw a box around the face
    cv2.rectangle(frame, (left, top), (right, bottom),clr , 2)

Draw a label with a name below the face

    cv2.rectangle(frame, (left, bottom - 35), (right, bottom), clr, 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()

[mWhrerttttt]

Traceback (most recent call last):
File "c:\Users\User\Desktop\FACERECORGING\rect.py", line 10, in
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\face_recognition\api.py", line 213, in face_encodings
raw_landmarks = _raw_face_landmarks(face_image, known_face_locations, model)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\face_recognition\api.py", line 156, in _raw_face_landmarks
face_locations = _raw_face_locations(face_image)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\face_recognition\api.py", line 105, in _raw_face_locations
return face_detector(img, number_of_times_to_upsample)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Unsupported image type, must be 8bit gray or RGB image.

[mWhrerttttt]

is there a solution to this error message that appears

[olimattison]

I think downgrading numpy is what got rid of that for me.

[flerken42]

Downgrading Numpy version did the trick for me. I used:

pip install numpy==1.26.4

[lailakarbash]

التتبع (آخر مكالمة أخيرة): ملف "c:\Users\User\Desktop\FACERECORGING\rect.py"، السطر 10، في obama_face_encoding = face_recognition.face_encodings(obama_image)[0] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ الملف "C:\Users\User\AppData\Local\Programs\Python312\Lib\site-packages\face_recognition\api.py"، السطر 213، في face_encodings raw_landmarks = _raw_face_landmarks(face_image، known_face_locations، نموذج) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ملف "C: \ المستخدمون \ المستخدم \ AppData \ المحلية \ البرامج \ Python \ Python \ Python312 \ Lib \ site-packages \ face_recognition \ api.py" ، السطر 156 ، في _raw_face_landmarks face_locations = _raw_face_locations (face_image) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^ ملف "C: \ Users \ User \ AppData \ Local \ Programs \ Python \ Python312 \ Lib \ site-packages \ face_recognition \ api.py" ، السطر 105 ، في raw_face ترجع المواقع face_detector (img، number_of_times_to_upsample) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: نوع الصورة غير المدعوم، يجب أن يكون 8 بت رمادي أو صورة RGB.

مساء الخير