340 lines
No EOL
15 KiB
Python
340 lines
No EOL
15 KiB
Python
#!/usr/bin/env python3
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"""
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Complete Plant Prediction Pipeline with Open-Meteo Official Client
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Open source weather + AI image transformation
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"""
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import openmeteo_requests
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import pandas as pd
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import requests_cache
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from retry_requests import retry
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from datetime import datetime, timedelta
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from PIL import Image
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import torch
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from diffusers import StableDiffusionInstructPix2PixPipeline
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import numpy as np
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import geocoder
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class PlantPredictor:
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def __init__(self):
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"""Initialize the plant prediction pipeline with Open-Meteo client"""
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# Setup the Open-Meteo API client with cache and retry on error
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cache_session = requests_cache.CachedSession('.cache', expire_after=3600)
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retry_session = retry(cache_session, retries=5, backoff_factor=0.2)
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self.openmeteo = openmeteo_requests.Client(session=retry_session)
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def get_current_location(self):
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"""Get current location using IP geolocation"""
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try:
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g = geocoder.ip('me')
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if g.ok:
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print(f" Location detected: {g.city}, {g.country}")
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print(f" Coordinates: {g.latlng[0]:.4f}, {g.latlng[1]:.4f}")
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return g.latlng[0], g.latlng[1] # lat, lon
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else:
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print(" Could not detect location, using default (Milan)")
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self.image_model = None
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except Exception as e:
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print(f" Location detection failed: {e}, using default (Milan)")
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return 45.4642, 9.1900
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def load_image_model(self):
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"""Load the image transformation model"""
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print("Loading Stable Diffusion model...")
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self.image_model = StableDiffusionInstructPix2PixPipeline.from_pretrained(
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"timbrooks/instruct-pix2pix",
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
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)
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if torch.cuda.is_available():
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self.image_model = self.image_model.to("cuda")
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print("Model loaded successfully!")
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def get_weather_forecast(self, lat, lon, days=7):
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"""Get weather forecast from Open-Meteo API using official client"""
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start_date = datetime.now().strftime("%Y-%m-%d")
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end_date = (datetime.now() + timedelta(days=days)).strftime("%Y-%m-%d")
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url = "https://api.open-meteo.com/v1/forecast"
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params = {
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"latitude": lat,
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"longitude": lon,
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"daily": [
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"temperature_2m_max",
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"temperature_2m_min",
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"precipitation_sum",
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"rain_sum",
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"uv_index_max",
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"sunshine_duration"
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],
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"start_date": start_date,
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"end_date": end_date,
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"timezone": "auto"
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}
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try:
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responses = self.openmeteo.weather_api(url, params=params)
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response = responses[0] # Process first location
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print(f"Coordinates: {response.Latitude()}°N {response.Longitude()}°E")
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print(f"Elevation: {response.Elevation()} m asl")
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print(f"Timezone: UTC{response.UtcOffsetSeconds()//3600:+d}")
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# Process daily data
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daily = response.Daily()
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# Extract data as numpy arrays (much faster!)
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daily_data = {
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"date": pd.date_range(
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start=pd.to_datetime(daily.Time(), unit="s", utc=True),
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end=pd.to_datetime(daily.TimeEnd(), unit="s", utc=True),
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freq=pd.Timedelta(seconds=daily.Interval()),
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inclusive="left"
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),
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"temperature_2m_max": daily.Variables(0).ValuesAsNumpy(),
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"temperature_2m_min": daily.Variables(1).ValuesAsNumpy(),
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"precipitation_sum": daily.Variables(2).ValuesAsNumpy(),
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"rain_sum": daily.Variables(3).ValuesAsNumpy(),
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"uv_index_max": daily.Variables(4).ValuesAsNumpy(),
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"sunshine_duration": daily.Variables(5).ValuesAsNumpy()
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}
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# Create DataFrame for easy analysis
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daily_dataframe = pd.DataFrame(data=daily_data)
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return daily_dataframe, response
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except Exception as e:
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print(f"Error fetching weather data: {e}")
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return None, None
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def analyze_weather_for_plants(self, weather_df):
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"""Analyze weather data and create plant-specific metrics"""
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if weather_df is None or weather_df.empty:
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return None
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# Handle NaN values by filling with 0 or mean
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weather_df = weather_df.fillna(0)
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# Calculate plant-relevant metrics using pandas (more efficient)
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plant_conditions = {
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"avg_temp_max": round(weather_df['temperature_2m_max'].mean(), 1),
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"avg_temp_min": round(weather_df['temperature_2m_min'].mean(), 1),
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"total_precipitation": round(weather_df['precipitation_sum'].sum(), 1),
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"total_rain": round(weather_df['rain_sum'].sum(), 1),
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"total_sunshine_hours": round(weather_df['sunshine_duration'].sum() / 3600, 1), # Convert to hours
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"max_uv_index": round(weather_df['uv_index_max'].max(), 1),
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"days_analyzed": len(weather_df),
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"temp_range": round(weather_df['temperature_2m_max'].max() - weather_df['temperature_2m_min'].min(), 1)
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}
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return plant_conditions
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def create_transformation_prompt(self, image_path, plant_conditions):
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"""Create a detailed prompt for image transformation based on weather AND image analysis"""
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if not plant_conditions:
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return "Show this plant after one week of growth"
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# STEP 3A: Analyze original image
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try:
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image = Image.open(image_path).convert("RGB")
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# Basic image analysis
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width, height = image.size
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aspect_ratio = width / height
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# Simple plant type detection based on image characteristics
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plant_type = self.detect_plant_type(image)
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plant_health = self.assess_plant_health(image)
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print(f"📸 Image Analysis:")
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print(f" Plant type detected: {plant_type}")
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print(f" Current health: {plant_health}")
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print(f" Image size: {width}x{height}")
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except Exception as e:
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print(f"Warning: Could not analyze image: {e}")
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plant_type = "generic plant"
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plant_health = "healthy"
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# STEP 3B: Weather analysis with plant-specific logic
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temp_avg = (plant_conditions['avg_temp_max'] + plant_conditions['avg_temp_min']) / 2
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# Temperature effects (adjusted by plant type)
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if plant_type == "basil" or "herb" in plant_type:
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if temp_avg > 25:
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temp_effect = "warm weather promoting vigorous basil growth with larger, aromatic leaves and bushier structure"
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elif temp_avg < 15:
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temp_effect = "cool weather slowing basil growth with smaller, less vibrant leaves"
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else:
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temp_effect = "optimal temperature for basil supporting steady growth with healthy green foliage"
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else:
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if temp_avg > 25:
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temp_effect = "warm weather promoting vigorous growth with larger, darker green leaves"
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elif temp_avg < 10:
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temp_effect = "cool weather slowing growth with smaller, pale leaves"
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else:
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temp_effect = "moderate temperature supporting steady growth with healthy green foliage"
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# Water effects
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if plant_conditions['total_rain'] > 20:
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water_effect = "abundant rainfall keeping leaves lush, turgid and deep green"
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elif plant_conditions['total_rain'] < 5:
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water_effect = "dry conditions causing slight leaf wilting and browning at edges"
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else:
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water_effect = "adequate moisture maintaining crisp, healthy leaf appearance"
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# Sunlight effects
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if plant_conditions['total_sunshine_hours'] > 50:
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sun_effect = "plenty of sunlight encouraging dense, compact foliage growth"
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elif plant_conditions['total_sunshine_hours'] < 20:
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sun_effect = "limited sunlight causing elongated stems and sparse leaf growth"
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else:
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sun_effect = "moderate sunlight supporting balanced, proportional growth"
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# UV effects
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if plant_conditions['max_uv_index'] > 7:
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uv_effect = "high UV causing slight leaf thickening and waxy appearance"
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else:
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uv_effect = "moderate UV maintaining normal leaf texture"
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# STEP 3C: Create comprehensive prompt combining image + weather analysis
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prompt = f"""Transform this {plant_type} showing realistic growth after {plant_conditions['days_analyzed']} days. Current state: {plant_health}. Apply these weather effects: {temp_effect}, {water_effect}, {sun_effect}, and {uv_effect}. Show natural changes in leaf size, color saturation, stem thickness, and overall plant structure while maintaining the original composition and lighting. Weather summary: {plant_conditions['avg_temp_min']}-{plant_conditions['avg_temp_max']}°C, {plant_conditions['total_rain']}mm rain, {plant_conditions['total_sunshine_hours']}h sun"""
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def detect_plant_type(self, image):
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"""Simple plant type detection based on image characteristics"""
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# This is a simplified version - in a real app you'd use a plant classification model
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# For now, we'll do basic analysis
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# Convert to array for analysis
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img_array = np.array(image)
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# Analyze color distribution
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green_pixels = np.sum((img_array[:,:,1] > img_array[:,:,0]) & (img_array[:,:,1] > img_array[:,:,2]))
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total_pixels = img_array.shape[0] * img_array.shape[1]
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green_ratio = green_pixels / total_pixels
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# Simple heuristics (could be improved with ML)
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if green_ratio > 0.4:
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return "basil" # Assume basil for high green content
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else:
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return "generic plant"
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def assess_plant_health(self, image):
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"""Assess basic plant health from image"""
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img_array = np.array(image)
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# Analyze brightness and color vibrancy
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brightness = np.mean(img_array)
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green_channel = np.mean(img_array[:,:,1])
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if brightness > 150 and green_channel > 120:
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return "healthy and vibrant"
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elif brightness > 100 and green_channel > 80:
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return "moderately healthy"
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else:
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return "showing some stress", plant_type, plant_health
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def transform_plant_image(self, image_path, prompt):
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"""STEP 4: Generate new image based on analyzed prompt"""
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if self.image_model is None:
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self.load_image_model()
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try:
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# Load and prepare image
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image = Image.open(image_path).convert("RGB")
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# Resize if too large (for memory efficiency)
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if max(image.size) > 1024:
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image.thumbnail((1024, 1024), Image.Resampling.LANCZOS)
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print(f" STEP 4: Generating transformed image...")
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print(f" Using prompt: {prompt}")
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# Transform image
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result = self.image_model(
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prompt,
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image=image,
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num_inference_steps=20,
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image_guidance_scale=1.5,
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guidance_scale=7.5
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).images[0]
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return result
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except Exception as e:
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print(f"Error transforming image: {e}")
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return None
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def predict_plant_growth(self, image_path, lat=None, lon=None, output_path="predicted_plant.jpg", days=7):
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"""Complete pipeline: weather + image transformation"""
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# Auto-detect location if not provided
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if lat is None or lon is None:
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print("Auto-detecting location...")
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lat, lon = self.get_current_location()
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print(f"Starting plant prediction for coordinates: {lat:.4f}, {lon:.4f}")
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print(f"Analyzing {days} days of weather data...")
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# Step 1: Get weather data using official Open-Meteo client
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print("Fetching weather data with caching and retry...")
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weather_df, response_info = self.get_weather_forecast(lat, lon, days)
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if weather_df is None:
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print("Failed to get weather data")
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return None
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print(f"Weather data retrieved for {len(weather_df)} days")
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print("\nWeather Overview:")
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print(weather_df[['date', 'temperature_2m_max', 'temperature_2m_min', 'precipitation_sum', 'sunshine_duration']].head())
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# Step 2: Analyze weather for plants
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plant_conditions = self.analyze_weather_for_plants(weather_df)
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print(f"\nPlant-specific weather analysis: {plant_conditions}")
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# Step 3: Analyze image + weather to create intelligent prompt
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print("\nSTEP 3: Analyzing image and creating transformation prompt...")
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prompt, plant_type, plant_health = self.create_transformation_prompt(image_path, plant_conditions)
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print(f"Plant identified as: {plant_type}")
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print(f"Current health: {plant_health}")
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print(f"Generated transformation prompt: {prompt}")
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# Step 4: Generate transformed image
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print("\nSTEP 4: Generating prediction image...")
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result_image = self.transform_plant_image(image_path, prompt)
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if result_image:
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result_image.save(output_path)
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print(f"Plant growth prediction saved to: {output_path}")
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return result_image, plant_conditions, weather_df, plant_type, plant_health
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else:
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print("Failed to transform image")
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return None
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# Example usage
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if __name__ == "__main__":
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# Initialize predictor
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predictor = PlantPredictor()
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# Predict plant growth
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result = predictor.predict_plant_growth(
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image_path="./foto/basilico.jpg",
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output_path="predicted_plant_growth.jpg",
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days=7
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)
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if result:
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image, conditions, weather_data = result
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print("\n" + "="*50)
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print("PLANT PREDICTION COMPLETED SUCCESSFULLY!")
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print("="*50)
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print(f"Weather conditions analyzed: {conditions}")
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print(f"Weather data shape: {weather_data.shape}")
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print(f"Temperature range: {conditions['avg_temp_min']}°C to {conditions['avg_temp_max']}°C")
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print(f"Total precipitation: {conditions['total_rain']}mm")
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print(f"Sunshine hours: {conditions['total_sunshine_hours']}h")
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else:
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print("Plant prediction failed.") |