Merge pull request #2 from Jayshah6699/main

Updated My Fork

Build own facial detection/README.md

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+## FACE RECOGNITION AND CLASSIFICATION
+
+
+## The image used for training
+
+![](face_rec/faces/Bharath%20C%20S.jpg)
+
+
+## The image given input for classification
+![](face_rec/test1.PNG)
+
+
+## The output image with label
+![](face_rec/output_face_rec.PNG)

Build own facial detection/face_rec.py

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+import face_recognition as fr
+import os
+import cv2
+import face_recognition
+import numpy as np
+from time import sleep
+
+
+def get_encoded_faces():
+    """
+    looks through the faces folder and encodes all
+    the faces
+
+    :return: dict of (name, image encoded)
+    """
+    encoded = {}
+
+    for dirpath, dnames, fnames in os.walk("./faces"):
+        for f in fnames:
+            if f.endswith(".jpg") or f.endswith(".png"):
+                face = fr.load_image_file("faces/" + f)
+                encoding = fr.face_encodings(face)[0]
+                encoded[f.split(".")[0]] = encoding
+
+    return encoded
+
+
+def unknown_image_encoded(img):
+    """
+    encode a face given the file name
+    """
+    face = fr.load_image_file("faces/" + img)
+    encoding = fr.face_encodings(face)[0]
+
+    return encoding
+
+
+def classify_face(im):
+    """
+    will find all of the faces in a given image and label
+    them if it knows what they are
+
+    :param im: str of file path
+    :return: list of face names
+    """
+    faces = get_encoded_faces()
+    faces_encoded = list(faces.values())
+    known_face_names = list(faces.keys())
+
+    img = cv2.imread(im, 1)
+    #img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)
+    #img = img[:,:,::-1]
+
+    face_locations = face_recognition.face_locations(img)
+    unknown_face_encodings = face_recognition.face_encodings(img, face_locations)
+
+    face_names = []
+    for face_encoding in unknown_face_encodings:
+        # See if the face is a match for the known face(s)
+        matches = face_recognition.compare_faces(faces_encoded, face_encoding)
+        name = "Unknown"
+
+        # use the known face with the smallest distance to the new face
+        face_distances = face_recognition.face_distance(faces_encoded, face_encoding)
+        best_match_index = np.argmin(face_distances)
+        if matches[best_match_index]:
+            name = known_face_names[best_match_index]
+
+        face_names.append(name)
+
+        for (top, right, bottom, left), name in zip(face_locations, face_names):
+            # Draw a box around the face
+            cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)
+
+            # Draw a label with a name below the face
+            cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)
+            font = cv2.FONT_HERSHEY_DUPLEX
+            cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)
+
+
+    # Display the resulting image
+    while True:
+
+        cv2.imshow('Video', img)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            return face_names 
+
+
+print(classify_face("test1.PNG"))
+
+

Build own facial detection/requirements.txt

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+cmake
+dlib
+face_recognition
+numpy
+opencv-python

Chicago Crime Rate/Readme.md

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+The Chicago Crime dataset contains a summary of the reported crimes occurred in the City of Chicago from 2001 to 2017. 
+
+Dataset has been obtained from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system.
+
+Dataset contains the following columns: 
+
+ID: Unique identifier for the record.
+
+Case Number: The Chicago Police Department RD Number (Records Division Number), which is unique to the incident.
+
+Date: Date when the incident occurred.
+
+Block: address where the incident occurred
+
+IUCR: The Illinois Uniform Crime Reporting code.
+
+Primary Type: The primary description of the IUCR code.
+
+Description: The secondary description of the IUCR code, a subcategory of the primary description.
+
+Location Description: Description of the location where the incident occurred.
+
+Arrest: Indicates whether an arrest was made.
+
+Domestic: Indicates whether the incident was domestic-related as defined by the Illinois Domestic Violence Act.
+
+Beat: Indicates the beat where the incident occurred. A beat is the smallest police geographic area – each beat has a dedicated police beat car. 
+
+District: Indicates police district where the incident occurred. 
+
+Ward: The ward (City Council district) where incident occurred. 
+
+Community Area: Indicates the community area where the incident occurred. Chicago has 77 community areas. 
+
+FBI Code: Indicates the crime classification as outlined in the FBI's National Incident-Based Reporting System (NIBRS). 
+
+X Coordinate: The x coordinate of the location where the incident occurred in State of Illinois.
+
+Y Coordinate: The y coordinate of the location where the incident occurred in State of Illinois.
+
+Year: Year the incident occurred.
+
+Updated On: Date and time the record was last updated.
+
+Latitude: The latitude of the location where the incident occurred. This location is shifted from the actual location for partial redaction but falls on the same block.
+
+Longitude: The longitude of the location where the incident occurred. This location is shifted from the actual location for partial redaction but falls on the same block.
+
+Location: The location where the incident occurred
+
+Prophet is open source software released by Facebook’s Core Data Science team.
+
+Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. 
+
+Prophet works best with time series that have strong seasonal effects and several seasons of historical data. 
+
+For more information, please check this out: 
+
+https://research.fb.com/prophet-forecasting-at-scale/
+https://facebook.github.io/prophet/docs/quick_start.html#python-api
+
+NOTE:
+
+You must install fbprophet package as follows: pip install fbprophet
+
+If you encounter an error, try: conda install -c conda-forge fbprophet
+
+Prophet implements an additive regression model with four elements:
+
+A piecewise linear, Prophet automatically picks up change points in the data and identifies any change in trends.  
+
+A yearly seasonal component modeled using Fourier series.
+
+A weekly seasonal component.
+
+A holiday list that can be manually provided.
+
+Additive Regression model takes the form:   
+  𝑌=𝛽_0+∑2_(𝑗=1)^𝑝▒〖𝑓_𝑗 (𝑋_𝑗 )+𝜖〗
+The functions 𝑓_𝑗 (𝑥_𝑗) are unknown smoothing functions fit from the data 
+
+Reference: https://research.fb.com/prophet-forecasting-at-scale/

Finger Sign Classification WebApp/Procfile

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+web: sh setup.sh && streamlit run app.py

Finger Sign Classification WebApp/README.md

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+# ASL-prediction
+
+![](screenshot.png)

Finger Sign Classification WebApp/ResNetModel.json

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+{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "batch_input_shape": [null, 50, 50, 3], "dtype": "float32", "filters": 5, "kernel_size": [5, 5], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": [4, 4], "padding": "valid", "strides": [4, 4], "data_format": "channels_last"}}, {"class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 15, "kernel_size": [5, 5], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "dtype": "float32", "pool_size": [4, 4], "padding": "valid", "strides": [4, 4], "data_format": "channels_last"}}, {"class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 3, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.3.1", "backend": "tensorflow"}

Finger Sign Classification WebApp/app.py

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+import streamlit as st
+from tensorflow.keras.models import model_from_json
+from numpy import asarray 
+import numpy as np
+import base64 
+from PIL import Image
+
+
+json_file = open("ResNetModel.json","r")
+loaded_json_model = json_file.read()
+json_file.close()  
+
+
+model = model_from_json(loaded_json_model)
+model.load_weights("ResNetModelWeights.h5")
+labels = list("ABCDEF")
+st.title("Finger Sign Classification")
+
+st.markdown("""
+<style>
+body {
+    color: #000;
+    background-color:white;
+    
+
+    
+</style>
+    """, unsafe_allow_html=True)
+
+st.set_option('deprecation.showfileUploaderEncoding', False)
+uploaded_file = st.file_uploader("Upload image ", type=["png","jpg","jpeg"])
+
+if st.button('Predict'):
+
+    if uploaded_file is None:
+        st.error("Please Upload Image !!")
+    else:
+        img = Image.open(uploaded_file)
+        img = img.resize((50,50))
+        img = asarray(img)
+
+        print(img.shape)
+        img = img.reshape((1,img.shape[0],img.shape[1],img.shape[2]))
+
+        pred = labels[np.argmax(model.predict(img))]
+        st.image(img, use_column_width=True)
+        st.header("The uploaded image indicates sign of alphabet "+ pred)
+
+
+

Finger Sign Classification WebApp/setup.sh

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+mkdir -p ~/.streamlit/
+
+echo "\
+[server]\n\
+port = $PORT\n\
+enableCORS = false\n\
+headless = true\n\
+\n\
+" > ~/.streamlit/config.toml