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Weed_Detector.py

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+import os
+import cv2
+import zipfile
+import numpy as np
+import streamlit as st
+from io import BytesIO
+from PIL import Image
+from ultralytics import YOLO
+from utils import create_shapefile_with_latlon
+
+
+# Define paths
+path_to_store_bounding_boxes = 'detect/'
+path_to_save_shapefile = 'weed_detections.shp'
+
+# Ensure the output directories exist
+os.makedirs(path_to_store_bounding_boxes, exist_ok=True)
+
+# loading a custom model
+model = YOLO('new_yolov8_best.pt')
+
+# Mapping of class labels to readable names (assuming 'weeds' is class 1)
+class_names = ["citrus area", "trees", "weeds", "weeds and trees" ]
+
+
+# Streamlit UI
+st.title("Weed Detection and Shapefile Creation")
+
+# Input coordinates for image corners
+st.sidebar.header("Image Coordinates")
+top_left = st.sidebar.text_input("Top Left (lon, lat)", value="-48.8864783, -20.5906375")
+top_right = st.sidebar.text_input("Top Right (lon, lat)", value="-48.8855653, -20.5906264")
+bottom_right = st.sidebar.text_input("Bottom Right (lon, lat)", value="-48.8855534, -20.5914861")
+bottom_left = st.sidebar.text_input("Bottom Left (lon, lat)", value="-48.8864664, -20.5914973")
+
+# Convert input coordinates to tuples
+image_coords = [
+    tuple(map(float, top_left.split(','))),
+    tuple(map(float, top_right.split(','))),
+    tuple(map(float, bottom_right.split(','))),
+    tuple(map(float, bottom_left.split(',')))
+]
+
+# Upload image
+uploaded_image = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+
+if uploaded_image is not None:
+    # Display uploaded image
+    st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
+    img = Image.open(uploaded_image)
+    img_array = np.array(img)
+    image_height, image_width, _ = img_array.shape
+    temp_image_path = "temp_uploaded_image.png"
+    image = Image.open(uploaded_image)
+    image.save(temp_image_path)
+
+    # Perform weed detection on button click
+    if st.button("Detect Weeds"):
+        # Perform model prediction
+        results = model.predict(temp_image_path, imgsz=640, conf=0.2, iou=0.4)
+        results = results[0]
+        
+        weed_bboxes = []
+
+        for i, box in enumerate(results.boxes):
+            tensor = box.xyxy[0]
+            x1 = int(tensor[0].item())
+            y1 = int(tensor[1].item())
+            x2 = int(tensor[2].item())
+            y2 = int(tensor[3].item())
+            conf = box.conf[0].item()  # Confidence score
+            label = box.cls[0].item()  # Class label
+
+            # Debugging output to ensure boxes are detected
+            print(f"Box {i}: ({x1}, {y1}), ({x2}, {y2}), label: {label}, confidence: {conf}")
+
+            # Only process if the detected class is "weeds"
+            if class_names[int(label)] == "weeds":
+                print("weed detected")
+                # Draw bounding box on the image
+                cv2.rectangle(img_array, (x1, y1), (x2, y2), (255, 0, 255), 3)
+                # Save the bounding box coordinates
+                weed_bboxes.append((x1, y1, x2, y2))
+
+        # Save the image with bounding boxes
+        detected_image_path = os.path.join(path_to_store_bounding_boxes, "detected_image.png")
+        cv2.imwrite(detected_image_path, cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR))
+
+        # Display the image with bounding boxes
+        st.image(img_array, caption="Detected Weeds", use_column_width=True)
+
+        # Create shapefile with bounding boxes
+        create_shapefile_with_latlon(weed_bboxes, (image_width, image_height), image_coords, path_to_save_shapefile)
+
+        # Create ZIP file of the shapefile components
+        zip_buffer = BytesIO()
+        with zipfile.ZipFile(zip_buffer, 'w') as zip_file:
+            for filename in ['weed_detections.shp', 'weed_detections.shx', 'weed_detections.dbf']:
+                zip_file.write(filename, os.path.basename(filename))
+        zip_buffer.seek(0)
+
+        # Download ZIP file
+        st.download_button(
+            label="Download Shapefile ZIP",
+            data=zip_buffer,
+            file_name="weed_detections.zip",
+            mime="application/zip"
+        )

new_yolov8_best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2664fca042b1ead70a9dc27597052b1c137719ab8ae3a870c940bf16cdfc4c0e
+size 22508569

requirements.txt

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+altair==5.3.0
+anyio==4.4.0
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asttokens==2.4.1
+async-lru==2.0.4
+attrs==23.2.0
+Babel==2.15.0
+beautifulsoup4==4.12.3
+bleach==6.1.0
+blinker==1.8.2
+cachetools==5.3.3
+certifi==2024.2.2
+cffi==1.16.0
+charset-normalizer==3.3.2
+click==8.1.7
+click-plugins==1.1.1
+cligj==0.7.2
+colorama==0.4.6
+comm==0.2.2
+contourpy==1.2.1
+cycler==0.12.1
+debugpy==1.8.1
+decorator==5.1.1
+defusedxml==0.7.1
+exceptiongroup==1.2.1
+executing==2.0.1
+fastjsonschema==2.19.1
+filelock==3.14.0
+fiona==1.9.6
+fonttools==4.52.3
+fqdn==1.5.1
+fsspec==2024.5.0
+geopandas==0.14.4
+gitdb==4.0.11
+GitPython==3.1.43
+h11==0.14.0
+httpcore==1.0.5
+httpx==0.27.0
+idna==3.7
+intel-openmp==2021.4.0
+ipykernel==6.29.4
+ipython==8.24.0
+isoduration==20.11.0
+jedi==0.19.1
+Jinja2==3.1.4
+json5==0.9.25
+jsonpointer==2.4
+jsonschema==4.22.0
+jsonschema-specifications==2023.12.1
+jupyter_client==8.6.2
+jupyter_core==5.7.2
+jupyter-events==0.10.0
+jupyter-lsp==2.2.5
+jupyter_server==2.14.0
+jupyter_server_terminals==0.5.3
+jupyterlab==4.2.1
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.27.2
+kiwisolver==1.4.5
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.0
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+mistune==3.0.2
+mkl==2021.4.0
+mpmath==1.3.0
+nbclient==0.10.0
+nbconvert==7.16.4
+nbformat==5.10.4
+nest-asyncio==1.6.0
+networkx==3.3
+notebook==7.2.0
+notebook_shim==0.2.4
+numpy==1.26.4
+opencv-python==4.9.0.80
+overrides==7.7.0
+packaging==24.0
+pandas==2.2.2
+pandocfilters==1.5.1
+parso==0.8.4
+pillow==10.3.0
+pip==24.0
+platformdirs==4.2.2
+prometheus_client==0.20.0
+prompt_toolkit==3.0.44
+protobuf==4.25.3
+psutil==5.9.8
+pure-eval==0.2.2
+py-cpuinfo==9.0.0
+pyarrow==16.1.0
+pycparser==2.22
+pydeck==0.9.1
+Pygments==2.18.0
+pyparsing==3.1.2
+pyproj==3.6.1
+pyshp==2.3.1
+python-dateutil==2.9.0.post0
+python-json-logger==2.0.7
+PyYAML==6.0.1
+pyzmq==26.0.3
+referencing==0.35.1
+requests==2.32.2
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rich==13.7.1
+rpds-py==0.18.1
+scipy==1.13.1
+seaborn==0.13.2
+Send2Trash==1.8.3
+setuptools==69.5.1
+shapely==2.0.4
+six==1.16.0
+smmap==5.0.1
+sniffio==1.3.1
+soupsieve==2.5
+stack-data==0.6.3
+streamlit==1.35.0
+sympy==1.12
+tbb==2021.12.0
+tenacity==8.3.0
+terminado==0.18.1
+thop==0.1.1.post2209072238
+tinycss2==1.3.0
+toml==0.10.2
+tomli==2.0.1
+toolz==0.12.1
+torch==2.3.0
+torchvision==0.18.0
+tornado==6.4
+tqdm==4.66.4
+traitlets==5.14.3
+types-python-dateutil==2.9.0.20240316
+typing_extensions==4.12.0
+tzdata==2024.1
+ultralytics==8.2.23
+uri-template==1.3.0
+urllib3==2.2.1
+watchdog==4.0.1
+wcwidth==0.2.13
+webcolors==1.13
+webencodings==0.5.1
+websocket-client==1.8.0

utils.py

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+import os
+import cv2
+import zipfile
+import shapefile
+import numpy as np
+from io import BytesIO
+from shapely.geometry import Polygon
+import matplotlib.pyplot as plt
+from PIL import Image
+
+
+def convert_pixel_to_latlon(x, y, image_width, image_height, image_coords):
+    top_left, top_right, bottom_right, bottom_left = image_coords
+    
+    lon_top = top_left[0] + (top_right[0] - top_left[0]) * (x / image_width)
+    lon_bottom = bottom_left[0] + (bottom_right[0] - bottom_left[0]) * (x / image_width)
+    lat_left = top_left[1] + (bottom_left[1] - top_left[1]) * (y / image_height)
+    lat_right = top_right[1] + (bottom_right[1] - top_right[1]) * (y / image_height)
+    
+    lon = lon_top + (lon_bottom - lon_top) * (y / image_height)
+    lat = lat_left + (lat_right - lat_left) * (x / image_width)
+    
+    return lon, lat
+
+# Function to create a shapefile with image dimensions and bounding boxes
+def create_shapefile_with_latlon(bboxes, image_shape, image_coords, shapefile_path):
+    w = shapefile.Writer(shapefile_path)
+    w.field('id', 'C')
+
+    img_width, img_height = image_shape
+
+    # Add bounding boxes for weeds
+    for idx, (x1, y1, x2, y2) in enumerate(bboxes):
+        top_left = convert_pixel_to_latlon(x1, y1, img_width, img_height, image_coords)
+        top_right = convert_pixel_to_latlon(x2, y1, img_width, img_height, image_coords)
+        bottom_left = convert_pixel_to_latlon(x1, y2, img_width, img_height, image_coords)
+        bottom_right = convert_pixel_to_latlon(x2, y2, img_width, img_height, image_coords)
+
+        poly = Polygon([top_left, top_right, bottom_right, bottom_left, top_left])
+        w.poly([poly.exterior.coords])
+        w.record(f'weed_{idx}')
+
+    w.close()