| # import streamlit as st | |
| # from ultralytics import YOLO | |
| # import cv2 | |
| # import numpy as np | |
| # import os | |
| # from PIL import Image | |
| # from qualitative_analysis import qualitative_analysis | |
| # from quantitative_analysis import quantitative_analysis | |
| # # ====== Load YOLO Model ====== | |
| # MODEL_PATH = r"D:\MUSIC N JOY\zIIT DELHI\Mtech Project\GUI\Trained_model\weights\best.pt" | |
| # try: | |
| # model = YOLO(MODEL_PATH) | |
| # except Exception as e: | |
| # st.error(f"❌ Error Loading Model: {e}") | |
| # st.stop() | |
| # # ====== Streamlit UI ====== | |
| # st.title("🔬 hCG Test Detection") | |
| # st.write("📤 Upload an image here.") | |
| # uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"]) | |
| # # ====== Resize Helper Function ====== | |
| # def resize_image(image, base_width=200): | |
| # w_percent = base_width / float(image.size[0]) | |
| # new_height = int(float(image.size[1]) * w_percent) | |
| # return image.resize((base_width, new_height), Image.LANCZOS) | |
| # # ====== Helper: Get Latest Prediction Folder ====== | |
| # def get_latest_prediction_folder(): | |
| # detect_path = "runs/detect" | |
| # subdirs = [os.path.join(detect_path, d) for d in os.listdir(detect_path) if os.path.isdir(os.path.join(detect_path, d))] | |
| # if subdirs: | |
| # return max(subdirs, key=os.path.getmtime) | |
| # return None | |
| # # ====== Main Logic ====== | |
| # if uploaded_file: | |
| # # Convert Uploaded File to OpenCV Format | |
| # file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8) | |
| # image_np = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR) | |
| # # Convert BGR to RGB for correct color display | |
| # image_rgb = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) | |
| # uploaded_pil = Image.fromarray(image_rgb) | |
| # # ====== Run YOLO Detection ====== | |
| # results = model.predict(image_np, save=True, save_crop=True) | |
| # detected = any(len(result.boxes) > 0 for result in results) | |
| # if detected: | |
| # img_with_boxes = results[0].plot() | |
| # img_with_boxes_rgb = cv2.cvtColor(img_with_boxes, cv2.COLOR_BGR2RGB) | |
| # detected_pil = Image.fromarray(img_with_boxes_rgb) | |
| # detected_save_path = "runs/detect/latest_prediction.jpg" | |
| # detected_pil.save(detected_save_path) | |
| # col1, col2 = st.columns(2) | |
| # with col1: | |
| # st.image(resize_image(uploaded_pil), caption="📷 Uploaded Image", use_container_width=False) | |
| # with col2: | |
| # st.image(resize_image(detected_pil), caption="✅ Detection Result", use_container_width=False) | |
| # # ====== Locate Latest Cropped C & T Line Paths ====== | |
| # latest_pred_folder = get_latest_prediction_folder() | |
| # if latest_pred_folder: | |
| # roi_path = os.path.join(latest_pred_folder, "crops", "ROI", "image0.jpg") | |
| # c_line_path = os.path.join(latest_pred_folder, "crops", "C", "image0.jpg") | |
| # t_line_path = os.path.join(latest_pred_folder, "crops", "T", "image0.jpg") | |
| # else: | |
| # roi_path = c_line_path = t_line_path = None | |
| # # ====== Display ROI, C-Line, and T-Line ====== | |
| # st.subheader("🔍 Cropped Regions (ROI, C-Line, T-Line)") | |
| # col1, col2, col3 = st.columns(3) | |
| # with col1: | |
| # if roi_path and os.path.exists(roi_path): | |
| # roi_img = Image.open(roi_path).convert("RGB") | |
| # st.image(resize_image(roi_img), caption="📌 ROI") | |
| # with col2: | |
| # if c_line_path and os.path.exists(c_line_path): | |
| # c_img = Image.open(c_line_path).convert("RGB") | |
| # st.image(resize_image(c_img), caption="📌 Control Line (C)") | |
| # with col3: | |
| # if t_line_path and os.path.exists(t_line_path): | |
| # t_img = Image.open(t_line_path).convert("RGB") | |
| # st.image(resize_image(t_img), caption="📌 Test Line (T)") | |
| # # ====== Analysis Buttons ====== | |
| # st.subheader("🧪 Choose Analysis Type") | |
| # col1, col2 = st.columns(2) | |
| # with col1: | |
| # if st.button("📊 Qualitative Analysis"): | |
| # try: | |
| # result_img, intensity_plot = qualitative_analysis(roi_path) | |
| # st.image(resize_image(result_img), caption="🔍 Detected Lines on Strip") | |
| # st.pyplot(intensity_plot) | |
| # except Exception as e: | |
| # st.error(f"⚠️ Error: {e}") | |
| # with col2: | |
| # if st.button("📈 Quantitative Analysis"): | |
| # try: | |
| # if c_line_path and t_line_path: | |
| # predicted_conc = quantitative_analysis(c_line_path, t_line_path) | |
| # st.success(f"🧪 Predicted hCG Concentration: **{predicted_conc} mIU/mL**") | |
| # else: | |
| # st.error("❌ C or T line image not found. Please upload a valid strip image.") | |
| # except Exception as e: | |
| # st.error(f"⚠️ Error in Quantitative Analysis: {e}") | |
| # # ====== Determine Result Based on C & T Visibility ====== | |
| # st.subheader("🔎 Test Result") | |
| # if c_line_path and os.path.exists(c_line_path): | |
| # if t_line_path and os.path.exists(t_line_path): | |
| # st.success("✅ **Positive Result** - hCG detected.") | |
| # else: | |
| # st.warning("❌ **Negative Result** - No hCG detected.") | |
| # else: | |
| # st.error("⚠️ **Invalid Test** - Control Line (C) not detected. Please retry!") | |
| # else: | |
| # st.warning("⚠️ Invalid strip image! Please try reuploading a valid test strip.") | |
| import streamlit as st | |
| from ultralytics import YOLO | |
| import cv2 | |
| import numpy as np | |
| import os | |
| from PIL import Image | |
| from qualitative_analysis import qualitative_analysis | |
| from quantitative_analysis import quantitative_analysis | |
| # ====== Load YOLO Model ====== | |
| MODEL_PATH = "Trained_model/weights/best.pt" | |
| try: | |
| model = YOLO(MODEL_PATH) | |
| except Exception as e: | |
| st.error(f"❌ Error Loading Model: {e}") | |
| st.stop() | |
| # ====== Streamlit UI ====== | |
| st.title("🔬 hCG Test Detection") | |
| st.write("📤 Upload an image here.") | |
| uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"]) | |
| # ====== Resize Helper Function ====== | |
| def resize_image(image, base_width=200): | |
| w_percent = base_width / float(image.size[0]) | |
| new_height = int(float(image.size[1]) * w_percent) | |
| return image.resize((base_width, new_height), Image.LANCZOS) | |
| # ====== Helper: Get Latest Prediction Folder ====== | |
| def get_latest_prediction_folder(): | |
| detect_path = "runs/detect" | |
| subdirs = [os.path.join(detect_path, d) for d in os.listdir(detect_path) if os.path.isdir(os.path.join(detect_path, d))] | |
| if subdirs: | |
| return max(subdirs, key=os.path.getmtime) | |
| return None | |
| # ====== Main Logic ====== | |
| if uploaded_file: | |
| # Convert Uploaded File to OpenCV Format | |
| file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8) | |
| image_np = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR) | |
| # Convert BGR to RGB for correct color display | |
| image_rgb = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) | |
| uploaded_pil = Image.fromarray(image_rgb) | |
| # ====== Run YOLO Detection ====== | |
| results = model.predict(image_np, save=True, save_crop=True) | |
| detected = any(len(result.boxes) > 0 for result in results) | |
| if detected: | |
| img_with_boxes = results[0].plot() | |
| img_with_boxes_rgb = cv2.cvtColor(img_with_boxes, cv2.COLOR_BGR2RGB) | |
| detected_pil = Image.fromarray(img_with_boxes_rgb) | |
| detected_save_path = "runs/detect/latest_prediction.jpg" | |
| detected_pil.save(detected_save_path) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.image(resize_image(uploaded_pil), caption="📷 Uploaded Image", use_container_width=False) | |
| with col2: | |
| st.image(resize_image(detected_pil), caption="✅ Detection Result", use_container_width=False) | |
| # ====== Locate Latest Cropped C & T Line Paths ====== | |
| latest_pred_folder = get_latest_prediction_folder() | |
| if latest_pred_folder: | |
| roi_path = os.path.join(latest_pred_folder, "crops", "ROI", "image0.jpg") | |
| c_line_path = os.path.join(latest_pred_folder, "crops", "C", "image0.jpg") | |
| t_line_path = os.path.join(latest_pred_folder, "crops", "T", "image0.jpg") | |
| else: | |
| roi_path = c_line_path = t_line_path = None | |
| # ====== Display ROI, C-Line, and T-Line ====== | |
| st.subheader("🔍 Cropped Regions (ROI, C-Line, T-Line)") | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| if roi_path and os.path.exists(roi_path): | |
| roi_img = Image.open(roi_path).convert("RGB") | |
| st.image(resize_image(roi_img), caption="📌 ROI") | |
| with col2: | |
| if c_line_path and os.path.exists(c_line_path): | |
| c_img = Image.open(c_line_path).convert("RGB") | |
| st.image(resize_image(c_img), caption="📌 Control Line (C)") | |
| with col3: | |
| if t_line_path and os.path.exists(t_line_path): | |
| t_img = Image.open(t_line_path).convert("RGB") | |
| st.image(resize_image(t_img), caption="📌 Test Line (T)") | |
| # ====== Analysis Buttons ====== | |
| st.subheader("🧪 Choose Analysis Type") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| if st.button("📊 Qualitative Analysis"): | |
| try: | |
| result_img, intensity_plot = qualitative_analysis(roi_path) | |
| st.image(resize_image(result_img), caption="🔍 Detected Lines on Strip") | |
| st.pyplot(intensity_plot) | |
| # ====== Show Test Result after Qualitative Analysis ====== | |
| st.subheader("🔎 Test Result") | |
| if c_line_path and os.path.exists(c_line_path): | |
| if t_line_path and os.path.exists(t_line_path): | |
| st.success("✅ **Positive Result** - hCG detected.") | |
| else: | |
| st.warning("❌ **Negative Result** - No hCG detected.") | |
| else: | |
| st.error("⚠️ **Invalid Test** - Control Line (C) not detected. Please retry!") | |
| except Exception as e: | |
| st.error(f"⚠️ Error: {e}") | |
| with col2: | |
| if st.button("📈 Quantitative Analysis"): | |
| try: | |
| if c_line_path and t_line_path: | |
| predicted_conc = quantitative_analysis(c_line_path, t_line_path) | |
| st.success(f"🧪 Predicted hCG Concentration: **{predicted_conc} mIU/mL**") | |
| else: | |
| st.error("❌ C or T line image not found. Please upload a valid strip image.") | |
| except Exception as e: | |
| st.error(f"⚠️ Error in Quantitative Analysis: {e}") | |
| else: | |
| st.warning("⚠️ Invalid strip image! Please try reuploading a valid test strip.") | |