Update tempo.txt

tempo.txt

@@ -1,48 +1,64 @@
-import cv2
-import numpy as np
-
-def detect_half_circles(binary_image, threshold=0.8):
-    # Convert binary image to BGR for drawing colored shapes
-    output_image = cv2.cvtColor(binary_image, cv2.COLOR_GRAY2BGR)
-    
-    # Find contours in the binary image
-    contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    
-    detected_shapes = []
-
-    for contour in contours:
-        # Calculate the perimeter of the contour
-        perimeter = cv2.arcLength(contour, True)
-        
-        # Fit an ellipse to the contour
-        if len(contour) >= 5:  # Need at least 5 points to fit an ellipse
-            ellipse = cv2.fitEllipse(contour)
-            (x, y), (MA, ma), angle = ellipse
-            
-            # Check if the major axis is approximately twice the minor axis (indicating a half-circle)
-            if 0.8 < (MA / ma) < 1.2:  # This range can be adjusted based on specific requirements
-                aspect_ratio = float(MA) / ma
-                
-                # Check if the aspect ratio is approximately 2:1 (indicating a half-circle shape)
-                if 0.4 < aspect_ratio < 0.6:  # This range can also be adjusted
-                    detected_shapes.append(contour)
-                    
-                    # Draw the contour with a different color
-                    cv2.drawContours(output_image, [contour], -1, (0, 0, 255), 2)
-
-    return output_image, detected_shapes
-
-# Example usage
-# Load the binary masked image (make sure it's a binary image)
-binary_image = cv2.imread('path_to_your_image.png', cv2.IMREAD_GRAYSCALE)
-
-# Threshold the image to make sure it's binary
-_, binary_image = cv2.threshold(binary_image, 127, 255, cv2.THRESH_BINARY)
-
-# Detect half-circles and draw contours
-output_image, detected_shapes = detect_half_circles(binary_image)
-
-# Show the result
-cv2.imshow('Detected Half-Circles', output_image)
-cv2.waitKey(0)
-cv2.destroyAllWindows()
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+
+# Hyperparameters
+image_size = (224, 224, 3)  # Adjust based on your data
+
+# Define the Generator Network
+class Generator(nn.Module):
+  def __init__(self):
+    super(Generator, self).__init__()
+    # Define convolutional layers with appropriate filters and activations
+    self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
+    # ... Add more convolutional layers as needed
+    self.conv_final = nn.Conv2d(128, 3, kernel_size=3, stride=1, padding=1, activation=nn.Tanh)  # Tanh for shadow intensity
+
+  def forward(self, x):
+    # Define the forward pass through the convolutional layers
+    x = self.conv1(x)
+    # ... Forward pass through remaining convolutional layers
+    return self.conv_final(x)
+
+# Define the Discriminator Network
+class Discriminator(nn.Module):
+  def __init__(self):
+    super(Discriminator, self).__init__()
+    # Define convolutional layers with appropriate filters and activations
+    self.conv1 = nn.Conv2d(6, 32, kernel_size=3, stride=1, padding=1)
+    # ... Add more convolutional layers as needed
+    self.linear = nn.Linear(128, 1)  # Final layer with sigmoid activation
+
+  def forward(self, car, shadow):
+    # Concatenate car and shadow features
+    x = torch.cat([car, shadow], dim=1)
+    # Define the forward pass through the convolutional layers
+    x = self.conv1(x)
+    # ... Forward pass through remaining convolutional layers
+    return torch.sigmoid(self.linear(x))
+
+# Create data loaders for training and validation data
+# ... (Implement data loading logic using PyTorch's DataLoader)
+
+# Create the models
+generator = Generator()
+discriminator = Discriminator()
+
+# Define loss function and optimizer
+criterion = nn.BCELoss()
+g_optimizer = torch.optim.Adam(generator.parameters(), lr=0.0002)
+d_optimizer = torch.optim.Adam(discriminator.parameters(), lr=0.0002)
+
+# Training loop
+for epoch in range(epochs):
+  # Train the Discriminator
+  # ... (Implement discriminator training logic with loss calculation and updates)
+
+  # Train the Generator
+  # ... (Implement generator training logic with loss calculation and updates)
+
+  # Print training progress
+  # ... (Print loss values or other metrics)
+
+# Save the trained generator
+torch.save(generator.state_dict(), 'generator.pt')