Merge branch 'main' into Fixes-UppuluriKalyani#762

Computer Vision/Plant_Disease_Detection_System/README.md

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+# Plant-disease-detection
+
+## About the Project
+An application that for farmers to detect the type of plant or crops, detect any kind of diseases in them. The app sends the image of the plant to the server where it is analysed using CNN classifier model. Once detected, the disease and its solutions are displayed to the user
+
+## Model
+
+Trained to identify 5 classes for **Disease Detection** and 24 classes for **Disease Classification**. 
+Dataset can be downloaded form [kaggle](https://www.kaggle.com/abdallahalidev/plantvillage-dataset)
+
+           - Disease Classification Classes
+
+                       - Apple___Apple_scab
+                       - Apple___Black_rot
+			   - Apple___Cedar_apple_rust
+			   - Apple___healthy
+			   - Blueberry___healthy
+			   - Cherry___healthy
+			   - Cherry___Powdery_mildew
+			   - Grape___Black_rot
+			   - Grape___Esca_Black_Measles
+			   - Grape___healthy
+			   - Grape___Leaf_blight_Isariopsis_Leaf_Spot
+			   - Orange___Haunglongbing
+			   - Peach___Bacterial_spot
+			   - Peach___healthy
+			   - Pepper,_bell___Bacterial_spot
+			   - Pepper,_bell___healthy
+			   - Potato___Early_blight
+			   - Potato___healthy
+			   - Raspberry___healthy
+			   - Soybean___healthy
+			   - Squash___Powdery_mildew
+			   - Strawberry___healthy
+			   - Strawberry___Leaf_scorch
+			
+            - Disease Detection Classes
+            
+			   - Cherry___healthy
+			   - Cherry___Powdery_mildew
+			   - Grape___Black_rot
+			   - Grape___Esca_Black_Measles
+			   - Grape___healthy
+			   - Grape___Leaf_blight_Isariopsis_Leaf_Spot 
+---
+## Cloning the project  
+* Run command `git clone "https://github.com/Saideepthi123/Plant-disease-detection.git"` and change into the project folder
+* Create a virtual environment `env` in the repository (use virtualenv, etc)
+*  Activate virtual environment
+* Install the requirements
+
+
+To create virtual environment and install requirements run following commands
+```shell script
+virtualenv env
+```
+
+To activate the environment use following commands:
+Window: 
+```shell script
+.\env\Scripts\activate
+```
+Ubuntu/Linux
+```shell script
+source env/bin/activate
+```
+pip install -r requirements.txt
+
+Command to run the app
+---
+ - streamlit run app.py
+
+## Demo
+
+- About
+
+	![image](https://user-images.githubusercontent.com/52497119/118315341-0a7e7d80-b513-11eb-8565-24da0c206fdb.png)
+
+- Disease Predection
+
+	![image](https://user-images.githubusercontent.com/52497119/118315208-da36df00-b512-11eb-8b3a-4982fe2b3935.png)
+
+	- Image Upload
+
+		![3](https://user-images.githubusercontent.com/52497119/118315820-a01a0d00-b513-11eb-9a49-69176e64ed42.PNG)
+		
+	- Image Detected
+	  	 
+	        
+	![image](https://user-images.githubusercontent.com/52497119/119301851-c01e9e80-bc80-11eb-9e86-c23947307072.png)
+
+		
+- Disease Classification
+
+	- Image Upload
+
+		![5](https://user-images.githubusercontent.com/52497119/118316025-e96a5c80-b513-11eb-8735-866427410077.PNG)
+		
+	- Image Classified
+
+		![6](https://user-images.githubusercontent.com/52497119/118316149-1585dd80-b514-11eb-8c4b-8c9627d44e93.PNG)
+
+- Treatement Page
+
+	![7](https://user-images.githubusercontent.com/52497119/118316232-33534280-b514-11eb-8a71-3922c7e6267e.PNG)
+
+## Required Libraries
+- opencv-contrib-python-headless
+- tensorflow-cpu
+- streamlit
+- numpy
+- pandas
+- pillow
+- keras
+- matplotlib
+

Computer Vision/Plant_Disease_Detection_System/app.py

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+import os
+import time
+import cv2
+import sys
+import csv
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import tensorflow as tf
+import keras_preprocessing
+import streamlit as st
+import numpy as np
+import pandas as pd
+from numpy import argmax
+from PIL import Image, ImageEnhance
+from resizeimage import resizeimage
+from utils import label_map_util
+from utils import visualization_utils as vis_util
+from keras_preprocessing import image
+from keras_preprocessing.image import ImageDataGenerator
+from pathlib import Path
+
+# Ensure compatibility with TensorFlow 2.x
+tf.executing_eagerly()
+
+MODEL_NAME = './object_detection/inference_graph'
+IMAGE_NAME = './object_detection/images/out.jpg'
+
+CWD_PATH = os.getcwd()
+
+PATH_TO_CKPT = os.path.join('./object_detection/inference_graph/frozen_inference_graph.pb')
+PATH_TO_LABELS = os.path.join('./object_detection/training/labelmap.pbtxt')
+PATH_TO_IMAGE = os.path.join('./object_detection/images/out.jpg')
+
+NUM_CLASSES = 6
+
+# Load label map
+label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
+categories = label_map_util.convert_label_map_to_categories(
+    label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
+category_index = label_map_util.create_category_index(categories)
+
+# Load frozen TensorFlow model into memory
+detection_graph = tf.compat.v1.Graph()
+with detection_graph.as_default():
+    od_graph_def = tf.compat.v1.GraphDef()
+    with tf.io.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
+        serialized_graph = fid.read()
+        od_graph_def.ParseFromString(serialized_graph)
+        tf.import_graph_def(od_graph_def, name='')
+
+    sess = tf.compat.v1.Session(graph=detection_graph)
+
+# Input tensor is the image
+image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
+
+# Output tensors are the detection boxes, scores, and classes
+detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
+detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
+detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
+
+# Number of objects detected
+num_detections = detection_graph.get_tensor_by_name('num_detections:0')
+
+# Load image using OpenCV
+in_image = cv2.imread(PATH_TO_IMAGE)
+image_rgb = cv2.cvtColor(in_image, cv2.COLOR_BGR2RGB)
+image_expanded = np.expand_dims(image_rgb, axis=0)
+
+# Perform the actual detection by running the model
+(boxes, scores, classes, num) = sess.run(
+    [detection_boxes, detection_scores, detection_classes, num_detections],
+    feed_dict={image_tensor: image_expanded})
+
+# Draw the results of the detection
+# Updated fix for FreeTypeFont issue using getbbox()
+vis_util.visualize_boxes_and_labels_on_image_array(
+    in_image,
+    np.squeeze(boxes),
+    np.squeeze(classes).astype(np.int32),
+    np.squeeze(scores),
+    category_index,
+    use_normalized_coordinates=True,
+    line_thickness=8,
+    min_score_thresh=0.60)
+
+# Update getsize() to getbbox() inside visualization_utils.py:
+# Replace this line:
+# display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
+# With this:
+# display_str_heights = [font.getbbox(ds)[3] - font.getbbox(ds)[1] for ds in display_str_list]
+
+
+# Streamlit UI components
+def read_markdown_file(markdown_file):
+    return Path(markdown_file).read_text()
+
+def classification():
+    global path
+    global cn
+    model = tf.keras.models.Sequential([
+        tf.keras.layers.Conv2D(64, (3, 3), activation='relu', input_shape=(150, 150, 3)),
+        tf.keras.layers.MaxPooling2D(2, 2),
+        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
+        tf.keras.layers.MaxPooling2D(2, 2),
+        tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
+        tf.keras.layers.MaxPooling2D(2, 2),
+        tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
+        tf.keras.layers.MaxPooling2D(2, 2),
+        tf.keras.layers.Flatten(),
+        tf.keras.layers.Dropout(0.5),
+        tf.keras.layers.Dense(512, activation='relu'),
+        tf.keras.layers.Dense(24, activation='softmax')
+    ])
+
+    LABELS = [
+        'Apple___Apple_scab', 'Apple___Black_rot', 'Apple___Cedar_apple_rust', 'Apple___healthy',
+        'Blueberry___healthy', 'Cherry___healthy', 'Cherry___Powdery_mildew', 'Grape___Black_rot',
+        'Grape___Esca_Black_Measles', 'Grape___healthy', 'Grape___Leaf_blight_Isariopsis_Leaf_Spot',
+        'Orange___Haunglongbing', 'Peach___Bacterial_spot', 'Peach___healthy',
+        'Pepper_bell___Bacterial_spot', 'Pepper_bell___healthy', 'Potato___Early_blight',
+        'Potato___healthy', 'Potato___Late_blight', 'Raspberry___healthy', 'Soybean___healthy',
+        'Squash___Powdery_mildew', 'Strawberry___healthy', 'Strawberry___Leaf_scorch'
+    ]
+
+    model.load_weights("./object_classification/rps.h5")
+    path = './object_classification/images/out.jpg'
+    img = image.load_img(path, target_size=(150, 150))
+    x = image.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    images = np.vstack([x])
+    classes = model.predict(images, batch_size=10)
+    result = argmax(classes)
+    cn = LABELS[result]
+
+classification()
+
+# Main Streamlit app function
+def main():
+    st.markdown('<style>body{-webkit-app-region: drag;}</style>', unsafe_allow_html=True)
+    st.title("Plant Disease Detection & Classification")
+    st.text("Built with Streamlit and TensorFlow")
+
+    activities = ["About", "Plant Disease"]
+    choice = st.sidebar.selectbox("Select Activity", activities)
+    enhance_type = st.sidebar.radio("Type", ["Detection", "Classification", "Treatment"])
+
+    if choice == 'About':
+        intro_markdown = read_markdown_file("./doc/about.md")
+        st.markdown(intro_markdown, unsafe_allow_html=True)
+
+    if choice == 'Plant Disease' and enhance_type == 'Detection':
+        st.header("Plant Disease Detection")
+        image_file = st.file_uploader("Upload Image", type=['jpg'])
+        st.markdown("* * *")
+
+        if image_file is not None:
+            our_image = Image.open(image_file)
+            our_image.save('./object_detection/images/out.jpg')
+
+            if st.button('Process'):
+                st.image(in_image, use_column_width=True, channels='RGB')
+            st.image(our_image, use_column_width=True, channels='RGB')
+            st.balloons()
+
+    if choice == 'Plant Disease' and enhance_type == 'Classification':
+        st.header("Plant Disease Classification")
+        image_input = st.file_uploader("Upload Image", type=['jpg'])
+        st.markdown("* * *")
+
+        if image_input is not None:
+            some_image = Image.open(image_input)
+            some_image.save('./object_classification/images/out.jpg')
+
+            if st.button('Classify'):
+                st.image(path, use_column_width=True)
+                with st.spinner('Your image is processing'):
+                    time.sleep(5)
+                st.success(cn)
+                st.balloons()
+
+    if enhance_type == 'Treatment' and choice == 'Plant Disease':
+        data_markdown = read_markdown_file("./treatment/treatment.md")
+        st.markdown(data_markdown, unsafe_allow_html=True)
+
+main()

Computer Vision/Plant_Disease_Detection_System/core/__init__.py

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+