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| import cv2 | |
| import torch | |
| from torchvision import transforms | |
| from model import ResnetModel, EffnetModel | |
| from face_module import get_face_coords | |
| from meter import Meter | |
| from utils import download_weights | |
| # statistics of imagenet dataset | |
| mean = [0.485, 0.456, 0.406] | |
| std = [0.229, 0.224, 0.225] | |
| # model wieghts url | |
| effb0_net_url = 'https://github.com/yMayanand/Emotion-Recognition/releases/download/v1.0.0/eff_b0.pt' | |
| res18_net_url = 'https://github.com/yMayanand/Emotion-Recognition/releases/download/v1.0.0/res18.pt' | |
| # transforms for image | |
| val_transform = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Resize((48, 48)), | |
| transforms.Normalize(mean, std) | |
| ]) | |
| def load_model(model_name): | |
| # model for emotion classification | |
| if model_name == 'effb0': | |
| model = EffnetModel() | |
| fname = download_weights(effb0_net_url) | |
| elif model_name == 'res18': | |
| model = ResnetModel | |
| fname = download_weights(res18_net_url) | |
| else: | |
| raise ValueError('Enter correct model_name') | |
| # loading pretrained model | |
| state_dict = torch.load(fname) | |
| model.load_state_dict(state_dict['weights']) | |
| return model | |
| # emotion classes | |
| emotions = ['neutral', 'happy :-)', 'surprise :-O', 'sad', 'angry >:(', | |
| "disgust D-':", 'fear', 'contempt', 'unknown', 'NF'] | |
| # colors for text for each emotion classes | |
| colors = [(0, 128, 255), (255, 0, 255), (0, 255, 255), (255, 191, 0), (0, 0, 255), | |
| (255, 255, 0), (0, 191, 255), (255, 0, 191), (255, 0, 191), (255, 0, 191)] | |
| def predict(image, save_path, model): | |
| image = cv2.imread(image) | |
| h, w, c = image.shape | |
| # meter | |
| m = Meter((w//2, h), w//5, (255, 0, 0)) | |
| # storing orignal image in bgr mode | |
| orig_image = image | |
| image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| coords = get_face_coords(image) | |
| if coords: | |
| # getting bounding box coordinates for face | |
| xmin, ymin, xmax, ymax = coords | |
| model.eval() | |
| image = image[ymin:ymax, xmin:xmax, :] | |
| # check if face detected is not on edge of the screen | |
| h, w, c = image.shape | |
| if not (h and w): | |
| idx = 9 | |
| image_tensor = val_transform(image).unsqueeze(0) | |
| out = model(image_tensor) | |
| # prediction emotion for detected face | |
| pred = torch.argmax(out, dim=1) | |
| idx = pred.item() | |
| pred_emot = emotions[pred.item()] | |
| color = colors[idx] | |
| # drawing annotations on orignal bgr image | |
| orig_image = cv2.rectangle(orig_image, (xmin, ymin), (xmax, ymax), (255, 0, 0), 1) | |
| else: | |
| idx = 9 | |
| pred_emot = 'Face Not Detected' | |
| color = colors[-1] | |
| orig_image = cv2.flip(orig_image, 1) | |
| m.draw_meter(orig_image, idx) | |
| cv2.imwrite(save_path, orig_image) | |
| return orig_image | |
| if __name__ == '__main__': | |
| import argparse | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--image_path', type=str, help='path to image location') | |
| parser.add_argument('--model_name', type=str, default='effb0', help='name of the model') | |
| parser.add_argument('--save_path', type=str, default='./result.jpg', help='path to save image') | |
| args = parser.parse_args() | |
| model = load_model(args.model_name) | |
| predict(args.image_path, args.save_path, model) | |