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;}