app_utils.py

"""
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

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)
    
    return stat,scores_str