Add parse script

code/autoencoder.py

@@ -30,6 +30,10 @@ IMG_H = 160  # On better gpu use 256 and adam optimizer
 IMG_W = IMG_H * 2
 DATASET_PATHS = [
     "../../diplomska/datasets/oj/montreal_trial1/ge_images/images/",
+    #"../../diplomska/datasets/oj/montreal_trial1/teach/images/",
+    #"../../diplomska/datasets/oj/suffield_trial1/teach/images/",
+    #"../../diplomska/datasets/oj/utiascircle_trial1/teach/images/",
+    #"../../diplomska/datasets/oj/utiasday_trial1/teach/images/",
 ]
 
 #  configuring device
@@ -154,7 +158,7 @@ class Encoder(nn.Module):
                 stride=stride,
             ),
             nn.BatchNorm2d(out_channels),
-            nn.Dropout(0.3),
+            nn.Dropout(0.4),
             act_fn,
             nn.Conv2d(
                 in_channels=out_channels,
@@ -163,7 +167,6 @@ class Encoder(nn.Module):
                 stride=stride,
             ),
             nn.BatchNorm2d(out_channels * 2),
-            nn.Dropout(0.2),
             act_fn,
             nn.Conv2d(
                 in_channels=out_channels * 2,
@@ -172,7 +175,7 @@ class Encoder(nn.Module):
                 stride=stride,
             ),
             nn.BatchNorm2d(out_channels * 4),
-            nn.Dropout(0.1),
+            nn.Dropout(0.3),
             act_fn,
             nn.Conv2d(
                 in_channels=out_channels * 4,
@@ -181,6 +184,7 @@ class Encoder(nn.Module):
                 stride=stride,
             ),
             nn.BatchNorm2d(out_channels * 8),
+            nn.Dropout(0.1),
             act_fn,
             nn.Conv2d(
                 in_channels=out_channels * 8,
@@ -371,9 +375,7 @@ class ConvolutionalAutoencoder:
                     #  reconstructing images
                     output = self.network(val_images)
                     #  computing validation loss
-                    val_loss = loss_function(
-                        output, val_images.view(-1, 1, IMG_H, IMG_W)
-                    )
+                    val_loss = loss_function(output.flatten(), val_images.flatten())
 
             # --------------
             # VISUALISATION
@@ -402,9 +404,7 @@ class ConvolutionalAutoencoder:
                     grid = grid.permute(1, 2, 0)
                     plt.figure(dpi=170)
                     plt.title(
-                        f"Original/Reconstructed, training loss: \
-                        {round(loss.item(), 4)} validation loss: \
-                        {round(val_loss.item(), 4)}"
+                        f"Original/Reconstructed, training loss: {round(loss.item(), 4)} validation loss: {round(val_loss.item(), 4)}"
                     )
                     plt.imshow(grid)
                     plt.axis("off")
@@ -504,8 +504,7 @@ def preprocess_data():
         test_images.extend(test)
 
     print(
-        f"Training on {len(training_images)} images, validating on \
-                {len(validation_images)} images, testing on {len(test_images)} images"
+        f"Training on {len(training_images)} images, validating on {len(validation_images)} images, testing on {len(test_images)} images"
     )
     #  creating pytorch datasets
     training_data = GEDataset(
@@ -564,7 +563,7 @@ def main():
         training_data, validation_data, test_data = preprocess_data()
         model = ConvolutionalAutoencoder(Autoencoder(Encoder(), Decoder()))
         model.train(
-            nn.MSELoss(),
+            nn.MSELoss(reduction="sum"),
             epochs=args.epochs,
             batch_size=args.batch_size,
             training_set=training_data,
@@ -576,7 +575,7 @@ def main():
     elif args.test:
         t, v, td = preprocess_data()
         model = ConvolutionalAutoencoder(Autoencoder(Encoder(), Decoder()))
-        model.test(nn.MSELoss(), td)
+        model.test(nn.MSELoss(reduction="sum"), td)
 
     elif args.encode:
         t, v, td = preprocess_data()

data_scrape/.gitignore

@@ -0,0 +1 @@
+dataset/*

data_scrape/bs.py

@@ -0,0 +1,39 @@
+LAT_START = 45.9887
+LAT_END = 46.1339
+LNG_START = 14.4431
+LNG_END = 14.5910
+
+TILTS = [i for i in range(10, 60, 20)]
+HEADINGS = [i for i in range(0, 360, 90)]
+
+import numpy as np
+
+## 3
+## 4
+#driver.save_screenshot('screenshot.png')
+## 5
+#driver.quit()
+
+def generate_coordinates():
+    urls = []
+    latitudes = np.arange(LAT_START, LAT_END, 0.001)
+    longitudes = np.arange(LNG_START, LNG_END, 0.001)
+    for latitude in latitudes:
+        for longitude in longitudes:
+            for tilt in TILTS:
+                params=f'?lng={longitude}&lat={latitude}&tilt={tilt}&heading=0'
+                url = f'http://localhost:8000/index.html{params}'
+                urls.append(url)
+
+    return urls
+
+
+urls = generate_coordinates()
+
+# Write url line by line to text file
+
+with open("urls.txt", "w") as output:
+    for url in urls:
+        output.write(url + "\n")
+
+    output.close()

data_scrape/index.html

@@ -0,0 +1,54 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <script
+        src="#"
+        defer></script>
+    <script src="https://printjs-4de6.kxcdn.com/print.min.js"></script>
+    <script src="https://html2canvas.hertzen.com/dist/html2canvas.min.js"></script>
+</head>
+
+<body>
+    <div id="map" style="width: 2000px; height: 1000px;"></div>
+</body>
+<script>
+    //let lat_start = 45.9887;
+    //let lng_start = 14.4431;
+    //let lat_end = 46.1339;
+    //let lng_end = 14.5910;
+    //tilts = [0, 45]; // Tilt goes from 0 to 90
+    // headings go from 0 to 360
+    //headings = [0, 90, 180, 270];
+    // Dej to nrdi pol s pitonko
+    const queryString = window.location.search;
+    console.log(queryString);
+    const urlParams = new URLSearchParams(queryString);
+    const lt = parseFloat(urlParams.get('lat'));
+    const lg = parseFloat(urlParams.get('lng'));
+    const tilt = parseInt(urlParams.get('tilt'));
+    const heading = parseInt(urlParams.get('heading'));
+    console.log(lt, lg, tilt, heading);
+    window.onload = async () => {
+           map = new google.maps.Map(document.getElementById("map"), {
+               center: {
+                   lat: lt,
+                   lng: lg, 
+               },
+               zoom: 19,
+               mapTypeId: "satellite",
+               // hide the default map controls
+               disableDefaultUI: true,
+               // disable watermark hehe
+               clickableIcons: false,
+               
+           });
+           map.setTilt(tilt); // 0, 45
+           map.setHeading(heading); // 0, 90, 180, 270
+           await new Promise(r => setTimeout(r, 1000));
+    }
+ 
+
+</script>
+
+</html>