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import os
import base64
import json
import streamlit as st
from streamlit_option_menu import option_menu
from streamlit_authenticator import Authenticate
import yaml
from yaml.loader import SafeLoader
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, token=st.secrets["HF_TOKEN"])
detr_model = AutoModelForObjectDetection.from_pretrained(MODEL_PATH, token=st.secrets["HF_TOKEN"])
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 ]]
# with open('./static/config.yaml') as file:
# config = yaml.load(file, Loader=SafeLoader)
config = json.loads(st.secrets["CONFIG"])
authenticator = Authenticate(
config['credentials'],
config['cookie']['name'],
config['cookie']['key'],
config['cookie']['expiry_days'],
config['preauthorized']
)
name, authentication_status, username = authenticator.login('Login', 'main')
# 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
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)
if st.session_state["authentication_status"]:
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])
ucol.write(f'## Welcome *{st.session_state["name"]}*')
with bcol:
authenticator.logout('Logout', 'main')
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)
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