Update app.py

app.py

@@ -1,4 +1,58 @@
 import gradio as gr
+from image_processing import apply_blur, clip_image, wrap_image
+from detection import yolov10_inference, calculate_detection_metrics
+from PIL import Image
+import numpy as np
+import torchvision.transforms as transforms
+import torch
+from utils import *
+#from utils import flip_odd_lines, modulo, center_modulo, unmodulo, hard_thresholding, stripe_estimation, recons
+from utils import modulo
+import cv2
+
+import matplotlib.pyplot as plt
+
+def process_image(image, model_id, image_size, conf_threshold, correction, sat_factor, kernel_size, DO, t, vertical):
+
+    original_image = np.array(image)
+    original_image = original_image - original_image.min()
+    original_image = original_image / original_image.max()
+    original_image = original_image * 255.0
+    original_image = original_image.astype(np.uint8)
+
+    # scaling factor
+    scaling = 1.0
+    original_image = cv2.resize(original_image, (0, 0), fx=scaling, fy=scaling)
+
+
+    blurred_image = apply_blur(original_image / 255.0, kernel_size)
+    clipped_image = clip_image(blurred_image, correction, sat_factor) 
+
+    img_tensor = torch.tensor(blurred_image, dtype=torch.float32 ).permute(2, 0, 1).unsqueeze(0)
+    img_tensor = modulo( img_tensor * sat_factor, L=1.0)
+
+    wrapped_image = img_tensor.squeeze(0).permute(1, 2, 0).numpy()
+    wrapped_image = (wrapped_image*255).astype(np.uint8)
+
+    original_annotated, original_detections = yolov10_inference(original_image, model_id, image_size, conf_threshold)
+    clipped_annotated, clipped_detections = yolov10_inference((clipped_image*255.0).astype(np.uint8), "yolov10n", image_size, conf_threshold)
+    wrapped_annotated, wrapped_detections = yolov10_inference(wrapped_image, model_id, image_size, conf_threshold)
+
+    # Assuming `recons` is a function in `utils.py`
+    recon_image = recons(img_tensor, DO=1, L=1.0, vertical=(vertical == "True"), t=t)
+    recon_image_pil = transforms.ToPILImage()(recon_image.squeeze(0))
+    recon_image_np = np.array(recon_image_pil).astype(np.uint8)
+
+
+    recon_annotated, recon_detections = yolov10_inference(recon_image_np, model_id, image_size, conf_threshold)
+
+    metrics_clip = calculate_detection_metrics(original_detections, clipped_detections)
+    metrics_wrap = calculate_detection_metrics(original_detections, wrapped_detections)
+    metrics_recons = calculate_detection_metrics(original_detections, recon_detections)
+
+    return original_annotated, clipped_annotated, wrapped_annotated, recon_annotated, metrics_clip, metrics_wrap, metrics_recons
+
+
 
 def app():
     # Variables para ajustar el tamaño de las imágenes