adjust image

SolarPanelDetector.py

@@ -0,0 +1,138 @@
+from ultralytics import YOLO
+from PIL import Image
+import requests
+import os
+import random
+
+model = YOLO('detector.pt')
+
+
+def satellite_image_params(address, api_key, zoom, size):
+    """
+    Generate parameters for Google Maps API request based on given address, API key, zoom level, and image size.
+
+    Parameters:
+    address (str): The address to center the map on.
+    api_key (str): Google Maps API key.
+    zoom (int): Zoom level for the map.
+    size (str): Size of the requested map image.
+
+    Returns:
+    dict: A dictionary of parameters for the API request.
+    """
+    params = {
+        "center": address,
+        "zoom": str(zoom),
+        "size": size,
+        "maptype": "satellite",
+        "key": api_key
+    }
+    return params
+
+
+def fetch_satellite_image(address, api_key, zoom=18, size="640x640"):
+    """
+    Fetches a satellite image from Google Maps API based on the given address, api_key, zoom level, and size.
+
+    Parameters:
+    address (str): The address for the satellite image.
+    api_key (str): Google Maps API key.
+    zoom (int): Zoom level for the satellite image.
+    size (str): Size of the satellite image.
+
+    Returns:
+    str: File name of the saved satellite image or None if the request fails.
+    """
+    base_url = "https://maps.googleapis.com/maps/api/staticmap?"
+    params = satellite_image_params(address, api_key, zoom=zoom, size=size)
+    try:
+        response = requests.get(base_url, params=params)
+    except requests.exceptions.RequestException as e:
+        print(e)
+        return None
+    if response.status_code == 200:
+        image_data = response.content
+        img_name = f"{'_'.join(address.split()[-2:])}.jpg"
+        with open(img_name, "wb") as file:
+            file.write(image_data)
+        print("Image was downloaded successfully")
+        return img_name
+
+
+def plot_results(im_array, save_image=False, img_path="results.jpg"):
+    """
+     Converts an image array to a PIL image and optionally saves it.
+
+     Parameters:
+     im_array (numpy.ndarray): The image array to be converted.
+     save_image (bool): Whether to save the image.
+     img_path (str): Path to save the image.
+
+     Returns:
+     PIL.Image: The converted PIL image.
+     """
+    im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+    if save_image:
+        im.save(img_path)  # save image
+    return im
+
+
+def solar_panel_predict(image, conf=0.45):
+    """
+    Analyzes an image to detect solar panels and returns an annotated image along with a relevant message.
+
+    This function uses a model to detect solar panels in the given image. If solar panels are detected with confidence
+    above the specified threshold, it selects a positive sentence; otherwise, it chooses a sentence encouraging
+    solar panel installation. It also annotates the image with detection results.
+
+    Parameters:
+    image: The input image for solar panel detection.
+    conf: Confidence threshold for detection, default is 0.5.
+
+    Returns:
+    Tuple of (annotated image, prediction message)
+    """
+    negative_setences = [
+        "No solar panels yet? Your roof is a blank canvas waiting for a green masterpiece! 🎨🌱",
+        "It's lonely up here without solar panels. Imagine the sun-powered parties you're missing! 🌞🎉",
+        "Your roof could be a superhero in disguise. Just needs its solar cape! 🦸‍♂️☀️",
+        "Clear skies, empty roof. It's the perfect opportunity to harness the sun! 🌤️🔋",
+        "No panels detected – but don't worry, it's never too late to join the solar revolution and be a ray of hope! 🌍💡"]
+
+    positive_sentences = [
+        "Solar panels detected: You're not just saving money, you're also charging up Mother Earth's good vibes! 🌍💚",
+        "Roof status: Sunny side up! Your panels are turning rays into awesome days! ☀️😎",
+        "You've got solar power! Now your roof is cooler than a polar bear in sunglasses. 🐻‍❄️🕶️",
+        "Green alert: Your roof is now a climate hero's cape! Solar panels are saving the day, one ray at a time. 🦸‍♂️🌞",
+        "Solar panels spotted: Your roof is now officially a member of the Renewable Energy Rockstars Club! ⭐🌱"]
+
+    results = model(image, stream=True, conf=conf)
+    for result in results:
+        annotated_image = result.plot()
+        im = plot_results(annotated_image)
+
+        r = result.boxes
+        confi = r.conf.numpy().tolist()
+        if not confi:
+            prediction = random.choice(negative_setences)
+        else:
+            prediction = random.choice(positive_sentences)
+        return im, prediction
+
+
+def detector(address, api_key, zoom=18, size="640x640"):
+    """
+    Detects solar panels in a satellite image fetched based on the given address.
+
+    Parameters:
+    address (str): The address to fetch the satellite image of.
+    api_key (str): Google Maps API key.
+    zoom (int): Zoom level for the image.
+    size (str): Size of the image.
+
+    Returns:
+    tuple: Prediction text and detected image.
+    """
+    img_name = fetch_satellite_image(address, api_key, zoom=zoom, size=size)
+    im, prediction = solar_panel_predict(img_name)
+    return im, prediction

app.py

@@ -1,142 +1,7 @@
 import gradio as gr
-from ultralytics import YOLO
 from PIL import Image
-import requests
 import os
-import random
-
-model = YOLO('detector.pt')
-
-
-def satellite_image_params(address, api_key, zoom, size):
-    """
-    Generate parameters for Google Maps API request based on given address, API key, zoom level, and image size.
-
-    Parameters:
-    address (str): The address to center the map on.
-    api_key (str): Google Maps API key.
-    zoom (int): Zoom level for the map.
-    size (str): Size of the requested map image.
-
-    Returns:
-    dict: A dictionary of parameters for the API request.
-    """
-    params = {
-        "center": address,
-        "zoom": str(zoom),
-        "size": size,
-        "maptype": "satellite",
-        "key": api_key
-    }
-    return params
-
-
-def fetch_satellite_image(address, api_key, zoom=18, size="640x640"):
-    """
-    Fetches a satellite image from Google Maps API based on the given address, api_key, zoom level, and size.
-
-    Parameters:
-    address (str): The address for the satellite image.
-    api_key (str): Google Maps API key.
-    zoom (int): Zoom level for the satellite image.
-    size (str): Size of the satellite image.
-
-    Returns:
-    str: File name of the saved satellite image or None if the request fails.
-    """
-    base_url = "https://maps.googleapis.com/maps/api/staticmap?"
-    params = satellite_image_params(address, api_key, zoom=zoom, size=size)
-    try:
-        response = requests.get(base_url, params=params)
-    except requests.exceptions.RequestException as e:
-        print(e)
-        return None
-    if response.status_code == 200:
-        image_data = response.content
-        img_name = f"{'_'.join(address.split()[-2:])}.jpg"
-        with open(img_name, "wb") as file:
-            file.write(image_data)
-        print("Image was downloaded successfully")
-        return img_name
-
-
-def plot_results(im_array, save_image=False, img_path="results.jpg"):
-    """
-     Converts an image array to a PIL image and optionally saves it.
-
-     Parameters:
-     im_array (numpy.ndarray): The image array to be converted.
-     save_image (bool): Whether to save the image.
-     img_path (str): Path to save the image.
-
-     Returns:
-     PIL.Image: The converted PIL image.
-     """
-    im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
-    if save_image:
-        im.save(img_path)  # save image
-    return im
-
-
-def solar_panel_predict(image, conf=0.45):
-    """
-    Analyzes an image to detect solar panels and returns an annotated image along with a relevant message.
-
-    This function uses a model to detect solar panels in the given image. If solar panels are detected with confidence
-    above the specified threshold, it selects a positive sentence; otherwise, it chooses a sentence encouraging
-    solar panel installation. It also annotates the image with detection results.
-
-    Parameters:
-    image: The input image for solar panel detection.
-    conf: Confidence threshold for detection, default is 0.5.
-
-    Returns:
-    Tuple of (annotated image, prediction message)
-    """
-    negative_setences = [
-        "No solar panels yet? Your roof is a blank canvas waiting for a green masterpiece! 🎨🌱",
-        "It's lonely up here without solar panels. Imagine the sun-powered parties you're missing! 🌞🎉",
-        "Your roof could be a superhero in disguise. Just needs its solar cape! 🦸‍♂️☀️",
-        "Clear skies, empty roof. It's the perfect opportunity to harness the sun! 🌤️🔋",
-        "No panels detected – but don't worry, it's never too late to join the solar revolution and be a ray of hope! 🌍💡"]
-
-    positive_sentences = [
-        "Solar panels detected: You're not just saving money, you're also charging up Mother Earth's good vibes! 🌍💚",
-        "Roof status: Sunny side up! Your panels are turning rays into awesome days! ☀️😎",
-        "You've got solar power! Now your roof is cooler than a polar bear in sunglasses. 🐻‍❄️🕶️",
-        "Green alert: Your roof is now a climate hero's cape! Solar panels are saving the day, one ray at a time. 🦸‍♂️🌞",
-        "Solar panels spotted: Your roof is now officially a member of the Renewable Energy Rockstars Club! ⭐🌱"]
-
-    results = model(image, stream=True, conf=conf)
-    for result in results:
-        annotated_image = result.plot()
-        im = plot_results(annotated_image)
-
-        r = result.boxes
-        confi = r.conf.numpy().tolist()
-        if not confi:
-            prediction = random.choice(negative_setences)
-        else:
-            prediction = random.choice(positive_sentences)
-        return im, prediction
-
-
-def detector(address, api_key, zoom=18, size="640x640"):
-    """
-    Detects solar panels in a satellite image fetched based on the given address.
-
-    Parameters:
-    address (str): The address to fetch the satellite image of.
-    api_key (str): Google Maps API key.
-    zoom (int): Zoom level for the image.
-    size (str): Size of the image.
-
-    Returns:
-    tuple: Prediction text and detected image.
-    """
-    img_name = fetch_satellite_image(address, api_key, zoom=zoom, size=size)
-    im, prediction = solar_panel_predict(img_name)
-    return im, prediction
+from SolarPanelDetector import solar_panel_predict, detector
 
 custom_css = """
 .feedback textarea {font-size: 20px !important;}