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0000000000000000000000000000000000000000..b362e568a75c8993d338240f98814713e3ba336b --- /dev/null +++ b/inference/forecasting.py @@ -0,0 +1,84 @@ +import numpy as np +import pandas as pd +import joblib +import matplotlib.pyplot as plt +from tensorflow.keras.models import load_model +from datetime import timedelta + +MODEL_PATH = '/home/faizraza/Personal/Projects/SmartWareHouseSystem/models/forecasting_model/Farah_model5.h5' +SCALER_PATH = '/home/faizraza/Personal/Projects/SmartWareHouseSystem/models/forecasting_model/minmax_scaler.pkl' +TIME_STEPS = 30 +DAYS_TO_FORECAST = 30 +PRODUCTS_TO_PLOT = ['Product_1263', 'Product_0374', 'Product_0349', 'Product_0033', 'Product_1341'] +DATA_PATH = '/home/faizraza/Personal/Projects/SmartWareHouseSystem/models/forecasting_model/data_resampled_daily.pkl' +# -------------------------------------------- + +def load_scaler_and_model(): + scaler = joblib.load(SCALER_PATH) + model = load_model(MODEL_PATH) + return scaler, model + +def create_forecast_input(scaled_data, time_steps=30): + return scaled_data[-time_steps:] + +def forecast_future(model, input_seq, time_steps, forecast_days): + forecasted = [] + current_seq = input_seq.copy() + + for _ in range(forecast_days): + pred = model.predict(current_seq.reshape(1, time_steps, current_seq.shape[1]), verbose=0) + forecasted.append(pred[0]) + current_seq = np.append(current_seq[1:], [pred[0]], axis=0) + + return np.array(forecasted) + +def run_inference(data_resampled_daily, forecast_days=DAYS_TO_FORECAST): + scaler, model = load_scaler_and_model() + + # Scale data + scaled_data = scaler.transform(data_resampled_daily) + + # Prepare input for forecasting + last_input = create_forecast_input(scaled_data, time_steps=TIME_STEPS) + + # Forecast + forecast_scaled = forecast_future(model, last_input, TIME_STEPS, forecast_days) + + # Inverse scale + forecast = scaler.inverse_transform(forecast_scaled) + + # Prepare output DataFrame + forecast_dates = pd.date_range(start=data_resampled_daily.index[-1] + timedelta(days=1), periods=forecast_days) + forecast_df = pd.DataFrame(data=forecast, index=forecast_dates, columns=data_resampled_daily.columns) + + return forecast_df + +def plot_forecast_vs_actual(actual_df, forecast_df, selected_products=PRODUCTS_TO_PLOT): + plt.figure(figsize=(14, 6)) + for product in selected_products: + if product in actual_df.columns and product in forecast_df.columns: + plt.plot(actual_df.index, actual_df[product], label=f'{product} (Last 30 days)', linestyle='--') + plt.plot(forecast_df.index, forecast_df[product], label=f'{product} (Forecast)', linewidth=2) + + plt.title("Last 30 Days vs Forecasted Demand (Next 30 Days)") + plt.xlabel("Date") + plt.ylabel("Order Demand") + plt.legend() + plt.tight_layout() + plt.show() + +if __name__ == "__main__": + # Load historical resampled daily data + historical_df = pd.read_pickle(DATA_PATH) + + # Extract last 30 days of actual data + actual_last_30 = historical_df[-30:] + + # Run inference to forecast next 30 days + forecast_df = run_inference(historical_df) + + # Plot both actual and forecast + plot_forecast_vs_actual(actual_last_30, forecast_df) + + # Optional: Save forecast to CSV + forecast_df.to_csv("forecast_next_30_days.csv") diff --git a/inference/rf-detr-base.pth b/inference/rf-detr-base.pth new file mode 100644 index 0000000000000000000000000000000000000000..6e950b0db2b799b424a3bb5259541b7ff2f105e7 --- /dev/null +++ b/inference/rf-detr-base.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8f70210e425a4a4234d547737f57500bcc4ac24a333b99e33d9d5a371e0b80f +size 372578043 diff --git a/inference/rfdetr_inference.py b/inference/rfdetr_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..e378aea8cc510f0a96e44cade4bf62fa2e944cd5 --- /dev/null +++ b/inference/rfdetr_inference.py @@ -0,0 +1,128 @@ +from rfdetr import RFDETRBase +# import supervision as sv +# from PIL import Image + +# # ✅ Load the trained model +# model = RFDETRBase( +# pretrain_weights="/home/faizraza/Personal/Projects/Smart-warehouse-system11/models/rfdetr_model/checkpoint_best_total (1).pth" +# ) + +# # ✅ Path to test image +# image_path = "/home/faizraza/Personal/Projects/Smart-warehouse-system11/dataset/istockphoto-1215484506-612x612.jpg" +# image = Image.open(image_path) + +# # ✅ Run inference +# detections = model.predict(image, threshold=0.5) + +# # ✅ Calculate annotation settings +# text_scale = sv.calculate_optimal_text_scale(resolution_wh=image.size) +# thickness = sv.calculate_optimal_line_thickness(resolution_wh=image.size) + +# # ✅ Create annotators +# bbox_annotator = sv.BoxAnnotator(thickness=thickness) +# label_annotator = sv.LabelAnnotator( +# text_color=sv.Color.BLACK, +# text_scale=text_scale, +# text_thickness=thickness, +# smart_position=True +# ) + +# # ✅ Generate labels +# detections_labels = [ +# f"{model.classes[class_id]} {confidence:.2f}" +# for class_id, confidence in zip(detections.class_id, detections.confidence) +# ] + +# # ✅ Annotate image +# annotated_image = image.copy() +# annotated_image = bbox_annotator.annotate(annotated_image, detections) +# annotated_image = label_annotator.annotate(annotated_image, detections, detections_labels) + +# # ✅ Display result +# # sv.plot_image(annotated_image) +# # ✅ Save result +# output_path = "/home/faizraza/Personal/Projects/Smart-warehouse-system11/dataset/output.jpg" +# annotated_image.save(output_path) + + +# from rfdetr import RFDETRBase + +# model = RFDETRBase(pretrain_weights="/home/faizraza/Personal/Projects/Smart-warehouse-system11/models/rfdetr_model/checkpoint_best_total(2).pth") + +# detections = model.predict("/home/faizraza/Personal/Projects/Smart-warehouse-system11/dataset/box_dataset/test/-2023-01-13-10-45-52_png.rf.cb3b877fe5534c50303eb8d55fd0d861.jpg") + +# print(detections) + +# import supervision as sv +# from rfdetr import RFDETRBase +# from PIL import Image + +# import supervision as sv + + + +# path, image, annotations = ds[i] +# image = Image.open(path) + +# detections = model.predict(image, threshold=0.5) + +# text_scale = sv.calculate_optimal_text_scale(resolution_wh=image.size) +# thickness = sv.calculate_optimal_line_thickness(resolution_wh=image.size) + +# bbox_annotator = sv.BoxAnnotator(thickness=thickness) +# label_annotator = sv.LabelAnnotator( +# text_color=sv.Color.BLACK, +# text_scale=text_scale, +# text_thickness=thickness, +# smart_position=True) + +# detections_labels = [ +# f"{ds.classes[class_id]} {confidence:.2f}" +# for class_id, confidence +# in zip(detections.class_id, detections.confidence) +# ] + +# detections_image = image.copy() +# detections_image = bbox_annotator.annotate(detections_image, detections) +# detections_image = label_annotator.annotate(detections_image, detections, detections_labels) + + +# sv.plot_images_grid(images=detections_image, grid_size=(3, 3), size=(12, 12)) + +from rfdetr import RFDETRBase +import cv2 + +def main(): + # 1. load model + model = RFDETRBase( + pretrain_weights="/home/faizraza/Personal/Projects/Smart-warehouse-system11/models/" + "rfdetr_model/checkpoint_best_total(2).pth" + ) + + # 2. run inference + img_path = "/home/faizraza/Personal/Projects/Smart-warehouse-system11/dataset/box_dataset/test/" \ + "-2023-01-13-10-45-52_png.rf.cb3b877fe5534c50303eb8d55fd0d861.jpg" + detections = model.predict(img_path) + + # 3. load image + img = cv2.imread(img_path) + if img is None: + raise FileNotFoundError(f"Could not read image {img_path}") + + # 4. draw boxes + confidence + for (x1, y1, x2, y2), score in zip(detections.xyxy, detections.confidence): + x1, y1, x2, y2 = map(int, (x1, y1, x2, y2)) + cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2) + label = f"{score:.2f}" + cv2.putText( + img, label, (x1, y1 - 10), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2 + ) + + # 5. save output + out_path = "/home/faizraza/Personal/Projects/Smart-warehouse-system11/detected.jpg" + cv2.imwrite(out_path, img) + print("Saved:", out_path) + +if __name__ == "__main__": + main() \ No newline at end of file