Added Prediction Functionality.

app.py

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+import os
+import base64
+import streamlit as st
+from streamlit_option_menu import option_menu
+import pandas as pd
+from PIL import Image
+
+import numpy as np
+import torch
+import cv2
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+import bbox_visualizer as bbv
+from st_clickable_images import clickable_images
+from glob import glob
+
+
+MODEL_PATH = "pankaj-munde/FScout_v0.2"
+# image_dir = "./Data/images/"
+detr_preprocessor = AutoImageProcessor.from_pretrained(MODEL_PATH)
+detr_model = AutoModelForObjectDetection.from_pretrained(MODEL_PATH)
+colors = [[236, 112, 99], [165, 105, 189], [ 225, 9, 232], [ 255, 38, 8 ], [ 247, 249, 249 ], [170, 183, 184 ],  [ 247, 249, 249 ],  [ 247, 249, 249 ]]
+
+
+# images_lst = os.listdir(image_dir)
+# images = []
+# for file in images_lst:
+#     ipath = os.path.join(os.path.abspath(image_dir), file)
+#     with open(ipath, "rb") as image:
+#         encoded = base64.b64encode(image.read()).decode()
+#         images.append(f"data:image/jpeg;base64,{encoded}")
+
+
+
+def get_detr_predictions(image, thresh):
+    with torch.no_grad():
+        inputs = detr_preprocessor(images=image, return_tensors="pt")
+        outputs = detr_model(**inputs)
+        target_sizes = torch.tensor([image.size[::-1]])
+        results = detr_preprocessor.post_process_object_detection(
+            outputs, threshold=float(thresh), target_sizes=target_sizes)[0]
+    return results
+
+
+def add_label(img,
+              label,
+              bbox,
+              draw_bg=True,
+              text_bg_color=(255, 255, 255),
+              text_color=(0, 0, 0),
+              top=True):
+    """adds label, inside or outside the rectangle
+
+    Parameters
+    ----------
+    img : ndarray
+        the image on which the label is to be written, preferably the image with the rectangular bounding box drawn
+    label : str
+        the text (label) to be written
+    bbox : list
+        a list containing x_min, y_min, x_max and y_max of the rectangle positions
+    draw_bg : bool, optional
+        if True, draws the background of the text, else just the text is written, by default True
+    text_bg_color : tuple, optional
+        the background color of the label that is filled, by default (255, 255, 255)
+    text_color : tuple, optional
+        color of the text (label) to be written, by default (0, 0, 0)
+    top : bool, optional
+        if True, writes the label on top of the bounding box, else inside, by default True
+
+    Returns
+    -------
+    ndarray
+        the image with the label written
+    """
+
+    text_width = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)[0][0]
+
+    if top:
+        label_bg = [bbox[0], bbox[1], bbox[0] + text_width, bbox[1] + 30]
+        if draw_bg:
+            cv2.rectangle(img, (label_bg[0], label_bg[1]),
+                          (label_bg[2] + 5, label_bg[3]), text_bg_color, -1)
+        cv2.putText(img, label, (bbox[0] + 5, bbox[1] - 5),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, text_color, 2)
+
+    else:
+        label_bg = [bbox[0], bbox[1], bbox[0] + text_width, bbox[1] + 30]
+        if draw_bg:
+            cv2.rectangle(img, (label_bg[0], label_bg[1]),
+                          (label_bg[2] + 5, label_bg[3]), text_bg_color, -1)
+        cv2.putText(img, label, (bbox[0] + 5, bbox[1] - 5 + 30),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, text_color, 2)
+
+    return img
+
+
+def image_checkup_v4(ipath, thresh, show_count):
+    
+    imgOrig = ipath.convert("RGB")
+    image = imgOrig.copy()
+
+    # crop_name, crop_conf, crop_id = get_crop(image)
+
+    detr_results = get_detr_predictions(image, thresh)
+
+    final_results = []
+    all_predictions = {
+        "Name": "Detailed View",
+        "Value": ""
+    }
+
+    # result_data = {crop_name: crop_conf, "Inspection_data": []}
+    img_with_box = np.array(image).copy()
+    for idx, label_id in enumerate(detr_results["labels"].numpy()):
+        pred_score = round(detr_results["scores"].numpy()[idx], 2)
+        predicted_label = detr_model.config.id2label[label_id]
+        # if float(pred_score) > 50:
+
+        bbox = list(np.array(detr_results["boxes"].numpy()[idx], dtype=int))
+        img_with_box = bbv.draw_rectangle(img_with_box, bbox, bbox_color=colors[label_id])
+        # img_with_box = bbv.add_label(img_with_box, label=f"{predicted_label} : {pred_score}", bbox=bbox, top=False)
+        if show_count:
+            img_with_box = bbv.add_label(
+                img_with_box, f"{idx + 1}", bbox, draw_bg=True, top=True)
+        
+        else:
+            img_with_box = bbv.add_label(
+            img_with_box, f"", bbox, draw_bg=False, top=True)
+
+        final_results.append(
+            {"prediction": predicted_label,
+             "confidence": pred_score,
+             "color": colors[label_id]
+             }
+        )
+
+        all_predictions["Value"] += f"\n{idx + 1}. {predicted_label.split('_')[-1]} - {round(pred_score, 2)}%\n"
+    if len(final_results) > 0:
+        df = pd.DataFrame(final_results)
+        info = df["prediction"].value_counts()
+        resized_seg = cv2.resize(img_with_box, imgOrig.size)
+        new_res = []
+
+        for k, v in dict(info).items():
+            tmp = {}
+            prd_id = detr_model.config.label2id[k]
+            tmp["Insect"] = k
+            tmp["Count"] = v
+            tmp["Color"] = colors[int(prd_id)]
+            new_res.append(tmp)
+        
+        return new_res, resized_seg
+    
+    return [], img_with_box
+
+
+
+st.set_page_config(layout="wide")
+
+
+
+def add_logo(logo_path, width, height):
+    """Read and return a resized logo"""
+    logo = Image.open(logo_path)
+    # modified_logo = logo.resize((width, height))
+    return logo
+
+
+# st.write("<hr/>", unsafe_allow_html=True)
+
+with st.sidebar:
+    my_logo = add_logo(logo_path="./static/FarmGyan logo_1.png", width=50, height=60)
+    
+    st.image(my_logo)
+    
+    ucol, bcol = st.columns([3, 2])
+    # st.write("<hr/>", unsafe_allow_html=True)
+
+st.title(":seedling: FarmGyan | Insects Scouting")
+st.write("<hr/>", unsafe_allow_html=True)
+    
+st.write("## 🖖 Upload image for prediction")
+
+uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
+st.write("<hr/>", unsafe_allow_html=True)
+
+with st.spinner(text='In progress'):
+    st.sidebar.write("## ⚙️ Configurations") 
+    st.sidebar.write("<hr/>", unsafe_allow_html=True)
+
+    st.sidebar.write("#### Prediction Threshold") 
+    thresh = st.sidebar.slider("Threshold", 0.0, 1.0, 0.7, 0.1)
+    st.sidebar.write("#### Boxes Count") 
+    show_count = st.sidebar.checkbox("Show Count")
+    if uploaded_file is not None:
+        clicked = None
+        image = Image.open(uploaded_file).convert("RGB")
+        predicted_data, result_image = image_checkup_v4(image, thresh, show_count)
+        # print(predicted_data)
+
+        col, col1 = st.columns([2, 4])
+        feedback_submitted = False  # Initialize the flag
+        with col:
+            st.subheader("💯 Predicted Labels")
+            st.write(f"<h3>Total Count : {sum([d['Count'] for d in predicted_data])}</h3>", unsafe_allow_html=True)
+            for i, d in enumerate(predicted_data):
+                # Create HTML markup with style information
+                
+                html_string = f"""
+                <div style="display: flex; align-items: center;">
+                    <b style="margin-right: 15px">{i + 1}. </b>
+                    <div style="background-color: rgb({d["Color"][0]}, {d["Color"][1]}, {d["Color"][2]}); width: 20px; height: 20px; border: 1px solid black; margin-right: 10px;"></div>
+                    <p style="margin-top: 15px"><b>{d["Insect"]} : {d["Count"]} </b></p>
+                </div>
+              """
+
+                st.markdown(html_string, unsafe_allow_html=True)
+
+            st.write("<hr/>", unsafe_allow_html=True)
+            
+        with col1:
+            st.subheader("🍀 Predicted Image")
+            st.write("<br/>", unsafe_allow_html=True)
+
+            st.image(result_image)
+
+
+

requirements.txt

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+accelerate==0.25.0
+albumentations==1.3.1
+bbox-visualizer==0.1.0
+bitsandbytes==0.41.0
+datasets==2.14.7
+extra-streamlit-components==0.1.60
+huggingface-hub==0.20.1
+Jinja2==3.1.2
+matplotlib==3.7.2
+opencv-python
+openpyxl==3.1.2
+pandas==2.0.3
+Pillow==10.0.0
+plotly==5.17.0
+safetensors==0.3.1
+scikit-image==0.22.0
+scikit-learn==1.3.0
+scipy==1.11.4
+seaborn==0.12.2
+SQLAlchemy==2.0.24
+st-clickable-images==0.0.3
+streamlit==1.29.0
+streamlit-authenticator==0.2.3
+streamlit-option-menu==0.3.6
+timm==0.9.12
+tokenizers==0.15.0
+torch==2.0.1
+torchvision==0.15.2
+tqdm==4.65.0
+transformers==4.36.1