import gradio as gr import torch from transformers import SwinForImageClassification, AutoFeatureExtractor import mediapipe as mp import cv2 from PIL import Image import numpy as np import os # Face shape descriptions face_shape_descriptions = { "Heart": "dengan dahi lebar dan dagu yang runcing.", "Oblong": "yang lebih panjang dari lebar dengan garis pipi lurus.", "Oval": "dengan proporsi seimbang dan dagu sedikit melengkung.", "Round": "dengan garis rahang melengkung dan pipi penuh.", "Square": "dengan rahang tegas dan dahi lebar." } # Frame images path glasses_images = { "Oval": "glasses/oval.jpg", "Round": "glasses/round.jpg", "Square": "glasses/square.jpg", "Octagon": "glasses/octagon.jpg", "Cat Eye": "glasses/cat eye.jpg", "Pilot (Aviator)": "glasses/aviator.jpg" } # Ensure the 'glasses' directory exists and contains the images if not os.path.exists("glasses"): os.makedirs("glasses") # Create dummy image files if they don't exist for _, path in glasses_images.items(): if not os.path.exists(path): dummy_image = Image.new('RGB', (200, 100), color='gray') dummy_image.save(path) id2label = {0: 'Heart', 1: 'Oblong', 2: 'Oval', 3: 'Round', 4: 'Square'} label2id = {v: k for k, v in id2label.items()} # Load model device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_checkpoint = "microsoft/swin-tiny-patch4-window7-224" feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint) model = SwinForImageClassification.from_pretrained( model_checkpoint, label2id=label2id, id2label=id2label, ignore_mismatched_sizes=True ) # Load your trained weights # Ensure 'LR-0001-adamW-32-64swin.pth' is in the same directory or provide the correct path if os.path.exists('LR-0001-adamW-32-64swin.pth'): state_dict = torch.load('LR-0001-adamW-32-64swin.pth', map_location=device) model.load_state_dict(state_dict, strict=False) model.to(device) model.eval() else: print("Warning: Trained weights file 'LR-0001-adamW-32-64swin.pth' not found. Using pre-trained weights only.") # Initialize Mediapipe mp_face_detection = mp.solutions.face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.5) # --- New: Decision tree function def recommend_glasses_tree(face_shape): face_shape = face_shape.lower() if face_shape == "square": return ["Oval", "Round"] elif face_shape == "round": return ["Square", "Octagon", "Cat Eye"] elif face_shape == "oval": return ["Oval", "Pilot (Aviator)", "Cat Eye", "Round"] elif face_shape == "heart": return ["Pilot (Aviator)", "Cat Eye", "Round"] elif face_shape == "oblong": return ["Square", "Oval", "Pilot (Aviator)", "Cat Eye"] else: return [] # Preprocess function def preprocess_image(image): img = np.array(image, dtype=np.uint8) img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) results = mp_face_detection.process(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) if results.detections: detection = results.detections[0] bbox = detection.location_data.relative_bounding_box h, w, _ = img.shape x1 = int(bbox.xmin * w) y1 = int(bbox.ymin * h) x2 = int((bbox.xmin + bbox.width) * w) y2 = int((bbox.ymin + bbox.height) * h) img = img[y1:y2, x1:x2] else: raise ValueError("Wajah tidak terdeteksi.") img = cv2.resize(img, (224, 224)) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) inputs = feature_extractor(images=img, return_tensors="pt") return inputs['pixel_values'].squeeze(0) # Prediction function def predict(image): try: inputs = preprocess_image(image).unsqueeze(0).to(device) with torch.no_grad(): outputs = model(inputs) probs = torch.nn.functional.softmax(outputs.logits, dim=1) pred_idx = torch.argmax(probs, dim=1).item() pred_label = id2label[pred_idx] pred_prob = probs[0][pred_idx].item() * 100 # --- Use decision tree for recommendations frame_recommendations = recommend_glasses_tree(pred_label) description = face_shape_descriptions.get(pred_label, "tidak dikenali") gallery_items = [] # Load images for all recommended frames for frame in frame_recommendations: frame_image_path = glasses_images.get(frame) if frame_image_path and os.path.exists(frame_image_path): try: frame_image = Image.open(frame_image_path) gallery_items.append((frame_image, frame)) # Tambahkan nama frame except Exception as e: print(f"Error loading image for {frame}: {e}") # Build explanation text if frame_recommendations: recommended_frames_text = ', '.join(frame_recommendations) explanation = (f"Bentuk wajah kamu adalah {pred_label} ({pred_prob:.2f}%). " f"Kamu memiliki bentuk wajah {description} " f"Rekomendasi bentuk kacamata yang sesuai dengan wajah kamu adalah: {recommended_frames_text}.") else: explanation = (f"Bentuk wajah kamu adalah {pred_label} ({pred_prob:.2f}%). " f"Tidak ada rekomendasi frame untuk bentuk wajah ini.") return pred_label, explanation, gallery_items except ValueError as ve: return "Error", str(ve), [] except Exception as e: return "Error", f"Terjadi kesalahan: {str(e)}", [] # Gradio Interface with gr.Blocks(theme=gr.themes.Soft()) as iface: gr.Markdown("# Program Rekomendasi Kacamata Berdasarkan Bentuk Wajah") gr.Markdown("Upload foto wajahmu untuk mendapatkan rekomendasi bentuk kacamata yang sesuai.") with gr.Row(): with gr.Column(): image_input = gr.Image(type="pil") confirm_button = gr.Button("Konfirmasi") restart_button = gr.Button("Restart") with gr.Column(): detected_shape = gr.Textbox(label="Bentuk Wajah Terdeteksi") explanation_output = gr.Textbox(label="Penjelasan") recommendation_gallery = gr.Gallery(label="Rekomendasi Kacamata", columns=3, show_label=False) confirm_button.click(predict, inputs=image_input, outputs=[detected_shape, explanation_output, recommendation_gallery]) restart_button.click(lambda: (None, "", [], []), inputs=None, outputs=[image_input, detected_shape, explanation_output, recommendation_gallery]) # Add source statement under the gallery gr.Markdown("**Sumber gambar kacamata**: Katalog dari [glassdirect.co.uk](https://www.glassdirect.co.uk)") if __name__ == "__main__": iface.launch()