Hi,
I'm using the following code for face recognition. It's working fine for most of the image datasets that I've. But when I'm using this dataset (15.8 MB one), I'm getting the error:
RuntimeError: Unsupported image type, must be 8bit gray or RGB image.
My code is the following:
import numpy as np
import dlib
import cv2
import os
import PIL.Image
import math
import operator
predictor_path = "C:/Users/userpath/shape_predictor_68_face_landmarks.dat"
rootdir = 'C:/Users/userpath/celebrities'
font = cv2.FONT_HERSHEY_SIMPLEX
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
recognizer = cv2.face.createEigenFaceRecognizer()
images = []
labels = []
labels_index = []
def getFaces(frame):
dets = detector(frame, 1)
return dets
def highlightFaces(frame, faces):
for face in faces:
cv2.rectangle(frame, (face.left(), face.top()), (face.right(), face.bottom()), (255,255,255), 1, 0)
return frame
def markFace(frame, shape):
cv2.circle(frame, (shape.parts()[0].x, shape.parts()[0].y), 2, (255,255,255), -1)
cv2.circle(frame, (shape.parts()[8].x, shape.parts()[8].y), 2, (255,255,255), -1)
cv2.circle(frame, (shape.parts()[16].x, shape.parts()[16].y), 2, (255,255,255), -1)
cv2.circle(frame, (shape.parts()[18].x, shape.parts()[18].y), 2, (255,255,255), -1)
cv2.circle(frame, (shape.parts()[24].x, shape.parts()[24].y), 2, (255,255,255), -1)
cv2.circle(frame, (shape.parts()[30].x, shape.parts()[30].y), 2, (255,255,255), -1)
cv2.circle(frame, (shape.parts()[36].x, shape.parts()[36].y), 2, (255,255,255), -1)
cv2.circle(frame, (shape.parts()[39].x, shape.parts()[39].y), 2, (255,255,255), -1)
cv2.circle(frame, (shape.parts()[42].x, shape.parts()[42].y), 2, (255,255,255), -1)
cv2.circle(frame, (shape.parts()[45].x, shape.parts()[45].y), 2, (255,255,255), -1)
return frame
def cropFace(frame, shape):
# points = shape.parts()
# y1 = points[18].y if points[18].y < points[24].y else points[24].y
# y2 = points[8].x
# x1 = points[0].x
# x2 = points[16].x
#
# face = frame[y1:y2, x1:x2]
face = frame[0:200, 0:200]
face = cv2.resize(face, (200, 200))
#cv2.imshow("face", face)
return face
def centerPoint(p1, p2):
x1, y1 = p1.x, p1.y
x2, y2 = p2.x, p2.y
return (x1 + (x2 - x1)* .5, y1 + (y2 - y1) * .5)
# returns the center position of right eye as tuple
def rightEye(shape):
p1 = shape.parts()[36]
p2 = shape.parts()[39]
return centerPoint(p1, p2)
# returns the center position of right eye as tuple
def leftEye(shape):
p1 = shape.parts()[42]
p2 = shape.parts()[46]
return centerPoint(p1, p2)
# Find angle
def faceAngle(shape):
p1 = shape.parts()[0]
p2 = shape.parts()[16]
x1, y1 = p1.x, p1.y
x2, y2 = p2.x, p2.y
dx,dy = x2-x1,y2-y1
rads = -math.atan2(dy, dx)
return math.degrees(rads)
#rotate image around a point
def rotateImage(image, point, angle):
rows,cols = image.shape
M = cv2.getRotationMatrix2D(point,angle * -1,1)
return cv2.warpAffine(image, M, (cols,rows))
def affineTransform(image, shape):
points = shape.parts()
rows,cols = image.shape
x1, y1 = points[36].x, points[36].y
x2, y2 = points[45].x, points[45].y
x3, y3 = points[8].x, points[8].y
pts1 = np.float32([[x3,y3],[x1,y1],[x2,y2]])
pts2 = np.float32([[100,200],[0,70],[200,70]])
M = cv2.getAffineTransform(pts1,pts2)
return cv2.warpAffine(image, M,(cols,rows))
def main():
for subdir, dirs, files in os.walk(rootdir):
for file in files:
image = cv2.imread(os.path.join(subdir, file), 0)
faces = getFaces(image)
if len(faces) > 0:
for face in faces:
shape = predictor(image, face)
#angle = faceAngle(shape)
#righteye = rightEye(shape)
rotatedFace = affineTransform(image, shape)
faceImage = cropFace(rotatedFace, shape)
faceImage = clahe.apply(faceImage)
images.append(faceImage)
labels.append(subdir)
#cv2.imshow(subdir + file, faceImage)
for i, value in enumerate(labels):
labels_index.append(i)
recognizer.train(images, np.array(labels_index))
cap = cv2.VideoCapture('C:/Users/userpath/video.mp4')
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
faces = getFaces(frame)
if len(faces) > 0:
for i, face in enumerate(faces):
shape = predictor(frame, face)
#angle = faceAngle(shape)
#righteye = rightEye(shape)
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray_frame = affineTransform(gray_frame, shape)
faceImage = cropFace(gray_frame, shape)
faceImage = clahe.apply(faceImage)
cv2.imshow(str(i), faceImage)
frame = markFace(frame, shape)
label, conf = recognizer.predict(faceImage)
confPercent = 100 - ((100/100) * float(conf))
if (confPercent > 0):
name = labels[label].split("/")[len(labels[label].split("/")) -1]
cv2.putText(frame, "L: " + name + " conf: " + str(format(confPercent, '.2f')), (face.left() + 30,face.top()), cv2.FONT_HERSHEY_SIMPLEX,.5,(255,255,255),2 )
cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
main()
Can anyone please guide me why I'm getting this error?
Thank you!
