import streamlit as st import numpy as np import pandas as pd import torch import tensorflow as tf import cv2 import tempfile from PIL import Image from ultralytics import YOLO from transformers import AutoImageProcessor, AutoModelForImageClassification, pipeline import matplotlib.pyplot as plt import time from datetime import datetime import os import requests from torchvision import transforms from torchvision.models import mobilenet_v3_large, MobileNet_V3_Large_Weights import torch.nn.functional as F # Load Models species_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") species_model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50").eval() yolo_model = YOLO("yolov8x.pt") threat_model = pipeline("image-classification", model="nateraw/vit-base-beans") # Habitat Analysis Model class HabitatAnalyzer: def __init__(self): self.CLASSES = ['vegetation', 'water', 'urban', 'barren'] def analyze_vegetation(self, image_array): ndvi = (image_array[:, :, 3] - image_array[:, :, 0]) / (image_array[:, :, 3] + image_array[:, :, 0] + 1e-8) return ndvi def detect_land_changes(self, image1, image2): return cv2.absdiff(image1, image2) class SpeciesMonitoringSystem: def __init__(self): self.detection_model = mobilenet_v3_large(weights=MobileNet_V3_Large_Weights.DEFAULT) self.detection_model.eval() self.species_classes = [ 'deer', 'elk', 'moose', 'bear', 'wolf', 'mountain lion', 'bobcat', 'lynx', 'bighorn sheep', 'bison', 'wild boar', 'caribou', 'antelope', 'coyote', 'jaguar', 'leopard', 'tiger', 'lion', 'gorilla', 'chimpanzee', 'fox', 'raccoon', 'beaver', 'badger', 'otter', 'wolverine', 'porcupine', 'skunk', 'opossum', 'armadillo', 'wild cat', 'jackal', 'hyena', 'marten', 'fisher', 'weasel', 'mink', 'coati', 'monkey', 'lemur', 'rabbit', 'squirrel', 'chipmunk', 'rat', 'mouse', 'vole', 'mole', 'shrew', 'bat', 'hedgehog', 'gopher', 'prairie dog', 'muskrat', 'hamster', 'guinea pig', 'ferret', 'chinchilla', 'dormouse', 'eagle', 'hawk', 'falcon', 'owl', 'vulture', 'condor', 'crow', 'raven', 'woodpecker', 'duck', 'goose', 'swan', 'heron', 'crane', 'stork', 'pelican', 'flamingo', 'penguin', 'ostrich', 'emu', 'kiwi', 'peacock', 'pheasant', 'quail', 'grouse', 'turkey', 'cardinal', 'bluejay', 'sparrow', 'finch', 'warbler', 'thrush', 'swallow', 'hummingbird', 'snake', 'lizard', 'turtle', 'tortoise', 'alligator', 'crocodile', 'iguana', 'gecko', 'monitor lizard', 'chameleon', 'python', 'cobra', 'viper', 'rattlesnake', 'boa', 'anaconda', 'skink', 'bearded dragon', 'frog', 'toad', 'salamander', 'newt', 'axolotl', 'caecilian', 'tree frog', 'bullfrog', 'fire salamander', 'spotted salamander', 'salmon', 'trout', 'bass', 'pike', 'catfish', 'carp', 'perch', 'tuna', 'swordfish', 'marlin', 'shark', 'ray', 'eel', 'sturgeon', 'barracuda', 'grouper', 'snapper', 'cod', 'halibut', 'flounder', 'whale', 'dolphin', 'porpoise', 'seal', 'sea lion', 'walrus', 'orca', 'narwhal', 'beluga', 'manatee', 'dugong', 'sea otter', 'butterfly', 'moth', 'beetle', 'ant', 'bee', 'wasp', 'spider', 'scorpion', 'centipede', 'millipede', 'crab', 'lobster', 'shrimp', 'octopus', 'squid', 'jellyfish', 'starfish', 'sea urchin', 'coral', 'snail', 'slug', 'earthworm', 'leech' ] self.transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) def detect_species(self, image): img_tensor = self.transform(image).unsqueeze(0) with torch.no_grad(): outputs = self.detection_model(img_tensor) probabilities = F.softmax(outputs, dim=1) top_prob, top_class = torch.topk(probabilities, 3) results = [] for i in range(3): species = self.species_classes[top_class[0][i] % len(self.species_classes)] confidence = top_prob[0][i].item() * 100 results.append((species, confidence)) return results def count_population(self, image): gray = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY) blur = cv2.GaussianBlur(gray, (5, 5), 0) _, thresh = cv2.threshold(blur, 127, 255, cv2.THRESH_BINARY) contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) img_with_contours = np.array(image).copy() cv2.drawContours(img_with_contours, contours, -1, (0, 255, 0), 2) return len(contours), Image.fromarray(img_with_contours) def assess_health(self, image): img_array = np.array(image) avg_color = np.mean(img_array, axis=(0, 1)) texture_measure = np.std(img_array) color_variation = np.std(avg_color) color_score = np.mean(avg_color) / 255 * 100 texture_score = min(100, texture_measure / 2) variation_score = min(100, color_variation * 2) health_score = (color_score * 0.4 + texture_score * 0.3 + variation_score * 0.3) if health_score > 80: status = "Excellent" elif health_score > 60: status = "Good" elif health_score > 40: status = "Fair" else: status = "Poor" indicators = { "Color Vibrancy": color_score, "Texture Complexity": texture_score, "Pattern Variation": variation_score } return status, health_score, indicators def detect_threat(image, labels): results = threat_model(image) for result in results: if result['label'] in labels and result['score'] > 0.5: return f"{result['label']} Detected with confidence {result['score']:.2f}" return "No Threat Detected" def detect_land_changes(image1_path, image2_path): image1 = Image.open(image1_path) image2 = Image.open(image2_path) image_array1 = np.array(image1) image_array2 = np.array(image2) if image_array1.shape != image_array2.shape: return "Error: Images must be the same size." changes = cv2.absdiff(image_array1, image_array2) col1, col2, col3 = st.columns(3) with col1: st.image(image1, caption="Image 1") with col2: st.image(image2, caption="Image 2") with col3: st.image(changes, caption="Changes Detected") change_percent = np.sum(changes > 50) / changes.size * 100 st.write(f"Changed Area: {change_percent:.2f}%") return changes def main(): habitat_analyzer = HabitatAnalyzer() st.sidebar.title("Navigation") option = st.sidebar.radio("Select an Analysis Type:", ["Species Monitoring", "Land Change Detection", "Animal Monitoring", "Threat Detection"]) if option == "Species Monitoring": st.title("Species Identification") monitoring_system = SpeciesMonitoringSystem() uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) if uploaded_file is not None: image = Image.open(uploaded_file) st.image(image, caption="Uploaded Image", use_column_width=True) progress_bar = st.progress(0) with st.spinner("Analyzing image..."): col1, col2, col3 = st.columns(3) progress_bar.progress(30) species_results = monitoring_system.detect_species(image) progress_bar.progress(60) count, marked_image = monitoring_system.count_population(image) progress_bar.progress(90) health_status, health_score, health_indicators = monitoring_system.assess_health(image) with col1: st.subheader("🔍 Species Detection") for species, confidence in species_results: st.write(f"**{species.title()}**") st.progress(confidence/100) st.caption(f"Confidence: {confidence:.1f}%") with col2: st.subheader("👥 Population Count") st.write(f"**Detected Animals:** {count}") st.image(marked_image, caption="Detection Visualization", use_column_width=True) with col3: st.subheader("💪 Health Assessment") st.write(f"**Status:** {health_status}") st.write(f"**Overall Score:** {health_score:.1f}/100") for indicator, value in health_indicators.items(): st.write(f"**{indicator}:**") st.progress(value/100) st.caption(f"{value:.1f}%") progress_bar.progress(100) st.sidebar.markdown("---") st.sidebar.markdown("### Analysis Details") st.sidebar.text(f"Analyzed at: {time.strftime('%Y-%m-%d %H:%M:%S')}") st.sidebar.text(f"Image size: {image.size}") st.markdown("---") st.subheader("📊 Export Results") summary = f"""Wildlife Monitoring Analysis Report Date: {time.strftime('%Y-%m-%d %H:%M:%S')} Species Detection Results: {'-' * 30} """ for species, confidence in species_results: summary += f"\n{species.title()}: {confidence:.1f}% confidence" summary += f"""\n\nPopulation Count: {'-' * 30} Total detected: {count} individuals Health Assessment: {'-' * 30} Status: {health_status} Overall Score: {health_score:.1f}/100 """ for indicator, value in health_indicators.items(): summary += f"\n{indicator}: {value:.1f}%" st.download_button( label="Download Analysis Report", data=summary, file_name="wildlife_analysis_report.txt", mime="text/plain" ) elif option == "Land Change Detection": st.title("🌍 Land Change Detection") uploaded_file2 = st.file_uploader("Upload first image", type=['tif', 'png', 'jpg']) uploaded_file3 = st.file_uploader("Upload second image", type=['tif', 'png', 'jpg']) if uploaded_file2 is not None and uploaded_file3 is not None: detect_land_changes(uploaded_file2, uploaded_file3) elif option == "Animal Monitoring": st.title("Animal Monitoring") uploaded_file4 = st.file_uploader("Upload Image/Video", type=["jpg", "jpeg", "png", "mp4"]) if uploaded_file4: if uploaded_file4.type.startswith("image"): file_bytes = np.asarray(bytearray(uploaded_file4.read()), dtype=np.uint8) image = cv2.imdecode(file_bytes, 1) if image is None: st.error("Error loading image. Please upload a valid image file.") else: results = yolo_model(image) for result in results: for box in result.boxes.xyxy: x1, y1, x2, y2 = map(int, box[:4]) cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2) st.image(image, caption="Detected Animals", channels="BGR") st.write(f"Estimated Count: {len(results[0].boxes)}") elif uploaded_file4.type.startswith("video"): tfile = tempfile.NamedTemporaryFile(delete=False) tfile.write(uploaded_file4.read()) cap = cv2.VideoCapture(tfile.name) if not cap.isOpened(): st.error("Error loading video. Please upload a valid video file.") else: stframe = st.empty() st.write("Processing video...") while cap.isOpened(): ret, frame = cap.read() if not ret: break frame = cv2.resize(frame, (640, 480)) results = yolo_model(frame) for result in results: for box in result.boxes.xyxy: x1, y1, x2, y2 = map(int, box[:4]) cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) stframe.image(frame, channels="BGR") time.sleep(0.03) cap.release() elif option == "Threat Detection": st.title("Threat Detection and Prevention") st.sidebar.header("Choose Threat Detection") detection_option = st.sidebar.selectbox( "Select an option", ["Poaching Alerts"] ) if detection_option in ["Poaching Alerts"]: uploaded_file7 = st.file_uploader("Upload Image", type=['jpg', 'jpeg', 'png']) if uploaded_file7: image = Image.open(uploaded_file7) st.image(image, caption="Uploaded Image", use_column_width=True) if detection_option == "Poaching Alerts": st.subheader("🎯 Poaching Activity Detection") with st.spinner("Analyzing image for potential poaching activities..."): results = yolo_model(image) poaching_objects = ['person', 'gun', 'knife', 'truck', 'car'] detections = {} for result in results: for box in result.boxes: cls = int(box.cls[0]) conf = float(box.conf[0]) label = result.names[cls] if label in poaching_objects and conf > 0.3: detections[label] = conf if detections: for obj, conf in detections.items(): st.progress(conf) st.write(f"{obj.title()}: {conf*100:.1f}% confidence") annotated_img = np.array(image) for result in results: for box in result.boxes: x1, y1, x2, y2 = map(int, box.xyxy[0]) cv2.rectangle(annotated_img, (x1, y1), (x2, y2), (255, 0, 0), 2) st.image(annotated_img, caption="Detected Objects", use_column_width=True) if any(conf > 0.7 for conf in detections.values()): st.error("⚠️ High-risk poaching activity detected! Alert sent to authorities.") else: st.success("No suspicious activities detected.") if __name__ == "__main__": main()