Spaces:
Runtime error
Runtime error
File size: 4,686 Bytes
364ca9d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
import os
import cv2
import numpy as np
import mediapipe as mp
from deepface import DeepFace
class DataProcessing:
def __init__(self):
self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_alt2.xml')
self.mp_face_mesh = mp.solutions.face_mesh
self.mp_drawing = mp.solutions.drawing_utils
def enhance_contrast_histeq(self, image):
img = image.copy()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
resized_gray = cv2.resize(gray, (250, 190))
enhanced_image = cv2.equalizeHist(resized_gray)
cv2.imwrite("./static/result_upload2.jpg", enhanced_image)
cv2.imwrite("./static/temporary/result_upload2.jpg", enhanced_image)
return enhanced_image
def face_cropping_pred(self, img):
for i in range(0, 4):
if os.path.exists(f"./static/result_upload{i}.jpg"):
os.remove(f"./static/result_upload{i}.jpg")
print("File terhapus")
else:
print("File tidak ditemukan.")
# img = cv2.imread(filepath)
cv2.imwrite("./static/result_upload0.jpg", img)
cv2.imwrite('./static/temporary/result_upload0.jpg', img)
try:
DeepFace.extract_faces("./static/result_upload0.jpg")
offset = 40
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = self.face_cascade.detectMultiScale(gray, 1.1, 5)
for (x, y, w, h) in faces:
offset = 80
y_offset = 20
y1 = max(0, y - offset + y_offset)
y2 = min(y + h + offset + y_offset, img.shape[0])
x1 = max(0, x - offset)
x2 = min(x + w + offset, img.shape[1])
# Mengambil area sekitar wajah
faces = img[y1:y2, x1:x2]
cv2.imwrite("./static/result_upload1.jpg", faces)
cv2.imwrite('./static/temporary/result_upload1.jpg', faces)
return faces
except:
print("error")
def detect_landmark(self, img):
print("test")
image1 = img.copy()
image2 = img.copy()
with self.mp_face_mesh.FaceMesh(min_detection_confidence=0.1, min_tracking_confidence=0.6) as face_mesh:
image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)
image2.flags.writeable = False
results = face_mesh.process(image2)
image2.flags.writeable = True
image2 = cv2.cvtColor(image2, cv2.COLOR_RGB2BGR)
if results.multi_face_landmarks:
for face_landmarks in results.multi_face_landmarks:
self.mp_drawing.draw_landmarks(
image=image2,
landmark_list=face_landmarks,
connections=self.mp_face_mesh.FACEMESH_TESSELATION,
landmark_drawing_spec=self.mp_drawing.DrawingSpec(color=(0, 0, 0), thickness=0, circle_radius=0),
connection_drawing_spec=self.mp_drawing.DrawingSpec(color=(0, 0, 255), thickness=1, circle_radius=0))
cv2.imwrite('./static/result_upload2.jpg', image2)
cv2.imwrite('./static/temporary/result_upload2.jpg', image2)
subtracted_img = np.zeros(image1.shape, np.uint8)
for i in range(image1.shape[0]):
for j in range(image1.shape[1]):
subtracted_img[i, j] = abs(int(image1[i, j][0]) - int(image2[i, j][0]))
cv2.imwrite('./static/result_upload3.jpg', subtracted_img)
cv2.imwrite('./static/temporary/result_upload3.jpg', subtracted_img)
def annotate_face_mesh(self, image):
with self.mp_face_mesh.FaceMesh(min_detection_confidence=0.1, min_tracking_confidence=0.6) as face_mesh:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image.flags.writeable = False
results = face_mesh.process(image)
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.multi_face_landmarks:
for face_landmarks in results.multi_face_landmarks:
self.mp_drawing.draw_landmarks(
image=image,
landmark_list=face_landmarks,
connections=self.mp_face_mesh.FACEMESH_TESSELATION,
landmark_drawing_spec=self.mp_drawing.DrawingSpec(color=(0, 0, 0), thickness=0, circle_radius=0),
connection_drawing_spec=self.mp_drawing.DrawingSpec(color=(0, 0, 255), thickness=1, circle_radius=0))
return image
|