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"""
File: app_utils.py
Author: Elena Ryumina and Dmitry Ryumin
Description: This module contains utility functions for facial expression recognition application.
License: MIT License
"""
import torch
import numpy as np
import mediapipe as mp
from PIL import Image
import cv2
from pytorch_grad_cam.utils.image import show_cam_on_image
# Importing necessary components for the Gradio app
from model import pth_model_static, pth_model_dynamic, cam, pth_processing
from face_utils import get_box, display_info
from config import DICT_EMO, config_data
from plot import statistics_plot
from moviepy.editor import AudioFileClip
mp_face_mesh = mp.solutions.face_mesh
def preprocess_image_and_predict(inp):
return None, None, None
# inp = np.array(inp)
# if inp is None:
# return None, None
# try:
# h, w = inp.shape[:2]
# except Exception:
# return 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
def preprocess_video_and_predict(video):
# cap = cv2.VideoCapture(video)
# w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
# h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# fps = np.round(cap.get(cv2.CAP_PROP_FPS))
# path_save_video_face = 'result_face.mp4'
# vid_writer_face = cv2.VideoWriter(path_save_video_face, cv2.VideoWriter_fourcc(*'mp4v'), fps, (224, 224))
# path_save_video_hm = 'result_hm.mp4'
# vid_writer_hm = cv2.VideoWriter(path_save_video_hm, cv2.VideoWriter_fourcc(*'mp4v'), fps, (224, 224))
# lstm_features = []
# count_frame = 1
# count_face = 0
# probs = []
# frames = []
# last_output = None
# last_heatmap = None
# cur_face = 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:
# while cap.isOpened():
# _, frame = cap.read()
# if frame is None: break
# frame_copy = frame.copy()
# frame_copy.flags.writeable = False
# frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
# results = face_mesh.process(frame_copy)
# frame_copy.flags.writeable = True
# if results.multi_face_landmarks:
# for fl in results.multi_face_landmarks:
# startX, startY, endX, endY = get_box(fl, w, h)
# cur_face = frame_copy[startY:endY, startX: endX]
# if count_face%config_data.FRAME_DOWNSAMPLING == 0:
# cur_face_copy = pth_processing(Image.fromarray(cur_face))
# with torch.no_grad():
# features = torch.nn.functional.relu(pth_model_static.extract_features(cur_face_copy)).detach().numpy()
# grayscale_cam = cam(input_tensor=cur_face_copy)
# grayscale_cam = grayscale_cam[0, :]
# cur_face_hm = cv2.resize(cur_face,(224,224), interpolation = cv2.INTER_AREA)
# cur_face_hm = np.float32(cur_face_hm) / 255
# heatmap = show_cam_on_image(cur_face_hm, grayscale_cam, use_rgb=False)
# last_heatmap = heatmap
# if len(lstm_features) == 0:
# lstm_features = [features]*10
# else:
# lstm_features = lstm_features[1:] + [features]
# lstm_f = torch.from_numpy(np.vstack(lstm_features))
# lstm_f = torch.unsqueeze(lstm_f, 0)
# with torch.no_grad():
# output = pth_model_dynamic(lstm_f).detach().numpy()
# last_output = output
# if count_face == 0:
# count_face += 1
# else:
# if last_output is not None:
# output = last_output
# heatmap = last_heatmap
# elif last_output is None:
# output = np.empty((1, 7))
# output[:] = np.nan
# probs.append(output[0])
# frames.append(count_frame)
# else:
# if last_output is not None:
# lstm_features = []
# empty = np.empty((7))
# empty[:] = np.nan
# probs.append(empty)
# frames.append(count_frame)
# if cur_face is not None:
# heatmap_f = display_info(heatmap, 'Frame: {}'.format(count_frame), box_scale=.3)
# cur_face = cv2.cvtColor(cur_face, cv2.COLOR_RGB2BGR)
# cur_face = cv2.resize(cur_face, (224,224), interpolation = cv2.INTER_AREA)
# cur_face = display_info(cur_face, 'Frame: {}'.format(count_frame), box_scale=.3)
# vid_writer_face.write(cur_face)
# vid_writer_hm.write(heatmap_f)
# count_frame += 1
# if count_face != 0:
# count_face += 1
# vid_writer_face.release()
# vid_writer_hm.release()
# stat = statistics_plot(frames, probs)
# if not stat:
# return None, None, None, None
# # print(type(frames))
# # print(frames)
# # print(type(probs))
# # print(probs)
# return video, path_save_video_face, path_save_video_hm, stat
return None, None, None, None
#to return scores
def preprocess_video_and_rank(video):
cap = cv2.VideoCapture(video)
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = np.round(cap.get(cv2.CAP_PROP_FPS))
path_save_video_face = 'result_face.mp4'
vid_writer_face = cv2.VideoWriter(path_save_video_face, cv2.VideoWriter_fourcc(*'mp4v'), fps, (224, 224))
# path_save_video_hm = 'result_hm.mp4'
# vid_writer_hm = cv2.VideoWriter(path_save_video_hm, cv2.VideoWriter_fourcc(*'mp4v'), fps, (224, 224))
lstm_features = []
count_frame = 1
count_face = 0
probs = []
frames = []
last_output = None
last_heatmap = None
cur_face = 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:
while cap.isOpened():
_, frame = cap.read()
if frame is None: break
frame_copy = frame.copy()
frame_copy.flags.writeable = False
frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
results = face_mesh.process(frame_copy)
frame_copy.flags.writeable = True
if results.multi_face_landmarks:
for fl in results.multi_face_landmarks:
startX, startY, endX, endY = get_box(fl, w, h)
cur_face = frame_copy[startY:endY, startX: endX]
if count_face%config_data.FRAME_DOWNSAMPLING == 0:
cur_face_copy = pth_processing(Image.fromarray(cur_face))
with torch.no_grad():
features = torch.nn.functional.relu(pth_model_static.extract_features(cur_face_copy)).detach().numpy()
# grayscale_cam = cam(input_tensor=cur_face_copy)
# grayscale_cam = grayscale_cam[0, :]
# cur_face_hm = cv2.resize(cur_face,(224,224), interpolation = cv2.INTER_AREA)
# cur_face_hm = np.float32(cur_face_hm) / 255
# heatmap = show_cam_on_image(cur_face_hm, grayscale_cam, use_rgb=False)
# last_heatmap = heatmap
if len(lstm_features) == 0:
lstm_features = [features]*10
else:
lstm_features = lstm_features[1:] + [features]
lstm_f = torch.from_numpy(np.vstack(lstm_features))
lstm_f = torch.unsqueeze(lstm_f, 0)
with torch.no_grad():
output = pth_model_dynamic(lstm_f).detach().numpy()
last_output = output
if count_face == 0:
count_face += 1
else:
if last_output is not None:
output = last_output
# heatmap = last_heatmap
elif last_output is None:
output = np.empty((1, 7))
output[:] = np.nan
probs.append(output[0])
frames.append(count_frame)
else:
if last_output is not None:
lstm_features = []
empty = np.empty((7))
empty[:] = np.nan
probs.append(empty)
frames.append(count_frame)
if cur_face is not None:
# heatmap_f = display_info(heatmap, 'Frame: {}'.format(count_frame), box_scale=.3)
cur_face = cv2.cvtColor(cur_face, cv2.COLOR_RGB2BGR)
cur_face = cv2.resize(cur_face, (224,224), interpolation = cv2.INTER_AREA)
cur_face = display_info(cur_face, 'Frame: {}'.format(count_frame), box_scale=.3)
vid_writer_face.write(cur_face)
# vid_writer_hm.write(heatmap_f)
count_frame += 1
if count_face != 0:
count_face += 1
vid_writer_face.release()
# vid_writer_hm.release()
stat = statistics_plot(frames, probs)
if not stat:
return None, None
#for debug
print(type(frames))
print(frames)
print(type(probs))
print(probs)
# to calculate scores
nan=float('nan')
s1 = 0
s2 = 0
s3 = 0
s4 = 0
s5 = 0
s6 = 0
s7 = 0
frames_len=len(frames)
for i in range(frames_len):
if np.isnan(probs[i][0]):
frames_len=frames_len-1
else:
s1=s1+probs[i][0]
s2=s2+probs[i][1]
s3=s3+probs[i][2]
s4=s4+probs[i][3]
s5=s5+probs[i][4]
s6=s6+probs[i][5]
s7=s7+probs[i][6]
s1=s1/frames_len
s2=s2/frames_len
s3=s3/frames_len
s4=s4/frames_len
s5=s5/frames_len
s6=s6/frames_len
s7=s7/frames_len
scores=[s1,s2,s3,s4,s5,s6,s7]
scores_str=str(scores)
with open("local_data/data.txt",'a', encoding="utf8") as f:
f.write(scores_str+'\n')
with open("local_data/data.txt",'r', encoding="utf8") as f:
for i in f:
print(i)
#trans the audio file
my_audio_clip = AudioFileClip(video)
my_audio_clip.write_audiofile("audio.wav")
return stat,scores_str,"audio.wav" |