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import numpy as np
import cv2
from PIL import Image
import torch
from app.model import pth_model_static, cam, pth_processing
from app.face_utils import get_box
from app.config import DICT_EMO
from pytorch_grad_cam.utils.image import show_cam_on_image
import mediapipe as mp
mp_face_mesh = mp.solutions.face_mesh
def preprocess_image_and_predict(inp):
inp = np.array(inp)
if inp is None:
return None, None, None
try:
h, w = inp.shape[:2]
except Exception:
return None, None, None
with mp_face_mesh.FaceMesh(
max_num_faces=1,
refine_landmarks=False,
min_detection_confidence=0.5,
min_tracking_confidence=0.5,
) as face_mesh:
results = face_mesh.process(inp)
if results.multi_face_landmarks:
for fl in results.multi_face_landmarks:
startX, startY, endX, endY = get_box(fl, w, h)
cur_face = inp[startY:endY, startX:endX]
cur_face_n = pth_processing(Image.fromarray(cur_face))
with torch.no_grad():
prediction = (
torch.nn.functional.softmax(pth_model_static(cur_face_n), dim=1)
.detach()
.numpy()[0]
)
confidences = {DICT_EMO[i]: float(prediction[i]) for i in range(7)}
grayscale_cam = cam(input_tensor=cur_face_n)
grayscale_cam = grayscale_cam[0, :]
cur_face_hm = cv2.resize(cur_face,(224,224))
cur_face_hm = np.float32(cur_face_hm) / 255
heatmap = show_cam_on_image(cur_face_hm, grayscale_cam, use_rgb=True)
return cur_face, heatmap, confidences |