Add Python-AI-ML

Author: harshsinghvi

.DS_Store

@@ -1,4 +1,5 @@
 from flask import Flask, request
+from datetime import datetime
 
 app = Flask(__name__)
 app.secret_key = 'Superrandom'
@@ -14,7 +15,7 @@ def file():
 
 
     print(a)
-    a.save('./store/'+a.filename)
+    a.save('./data/'+str(datetime.now())+"-" +  a.filename)
 
     # f = open("test.jpg", 'wb')
     # f.write(a.read())

media-files/.DS_Store

@@ -0,0 +1,7 @@
+from datetime import datetime 
+
+while True: 
+    d = datetime.now()
+    if (d.second == 2 or d.second == 34 ) and  d.microsecond == 0:
+        print(d.time())
+

media-files/api.py

@@ -0,0 +1,131 @@
+.vscode
+imageai/model.h5
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

media-files/data/.DS_Store

@@ -0,0 +1 @@
+# python-ai-ml

media-files/data/2021-02-20 10:40:11.320140

@@ -0,0 +1,11 @@
+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[dev-packages]
+
+[packages]
+
+[requires]
+python_version = "3.8"

media-files/data/2021-02-20 10:40:43.192883.jpg

@@ -0,0 +1,21 @@
+# Gesture Recognition
+
+Recognizing "Hand Gestures" using OpenCV and Python.
+
+#### Libraries needed
+
+* cv2
+* imutils
+* numpy
+* sklearn
+
+#### Usage
+
+1. `python segment.py`
+2. `python recognize.py`
+3. `python recognize-image.py`
+
+To understand how this works, please read the blog posts given below.
+
+* [Hand Gesture Recognition using Python and OpenCV - Part 1](https://gogul09.github.io/software/hand-gesture-recognition-p1)
+* [Hand Gesture Recognition using Python and OpenCV - Part 2](https://gogul09.github.io/software/hand-gesture-recognition-p2)

media-files/data/2021-02-20 10:42:31.401477-dog.jpg.jpg

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2017 Gogul
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

media-files/data/2021-02-20 10:42:49.009304-dog.jpg.jpg

@@ -0,0 +1,130 @@
+#------------------------------------------------------------
+# SEGMENT, RECOGNIZE and COUNT fingers from a single frame
+#------------------------------------------------------------
+
+# organize imports
+import cv2
+import imutils
+import numpy as np
+from sklearn.metrics import pairwise
+
+#---------------------------------------------
+# To segment the region of hand in the image
+#---------------------------------------------
+def segment(image, grayimage, threshold=75):
+    # threshold the image to get the foreground which is the hand
+    thresholded = cv2.threshold(grayimage, threshold, 255, cv2.THRESH_BINARY)[1]
+
+    # get the contours in the thresholded image
+    (_, cnts, _) = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    # return None, if no contours detected
+    if len(cnts) == 0:
+        return
+    else:     
+        # based on contour area, get the maximum contour which is the hand
+        segmented = max(cnts, key=cv2.contourArea)
+        
+        return (thresholded, segmented)
+
+#--------------------------------------------------------------
+# To count the number of fingers in the segmented hand region
+#--------------------------------------------------------------
+def count(image, thresholded, segmented):
+    # find the convex hull of the segmented hand region
+    # which is the maximum contour with respect to area
+    chull = cv2.convexHull(segmented)
+
+    # find the most extreme points in the convex hull
+    extreme_top    = tuple(chull[chull[:, :, 1].argmin()][0])
+    extreme_bottom = tuple(chull[chull[:, :, 1].argmax()][0])
+    extreme_left   = tuple(chull[chull[:, :, 0].argmin()][0])
+    extreme_right  = tuple(chull[chull[:, :, 0].argmax()][0])
+
+    # find the center of the palm
+    cX = int((extreme_left[0] + extreme_right[0]) / 2)
+    cY = int((extreme_top[1] + extreme_bottom[1]) / 2)
+
+    # find the maximum euclidean distance between the center of the palm
+    # and the most extreme points of the convex hull
+    distances = pairwise.euclidean_distances([(cX, cY)], Y=[extreme_left, extreme_right, extreme_top, extreme_bottom])[0]
+    max_distance = distances[distances.argmax()]
+
+    # calculate the radius of the circle with 80% of the max euclidean distance obtained
+    radius = int(0.8 * max_distance)
+
+    # find the circumference of the circle
+    circumference = (2 * np.pi * radius)
+
+    # initialize circular_roi with same shape as thresholded image
+    circular_roi = np.zeros(thresholded.shape[:2], dtype="uint8")
+
+    # draw the circular ROI with radius and center point of convex hull calculated above
+    cv2.circle(circular_roi, (cX, cY), radius, 255, 1)
+
+    # take bit-wise AND between thresholded hand using the circular ROI as the mask
+    # which gives the cuts obtained using mask on the thresholded hand image
+    circular_roi = cv2.bitwise_and(thresholded, thresholded, mask=circular_roi)
+
+    # compute the contours in the circular ROI
+    (_, cnts, _) = cv2.findContours(circular_roi.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+
+    count = 0
+    # approach 1 - eliminating wrist
+    #cntsSorted = sorted(cnts, key=lambda x: cv2.contourArea(x))
+    #print(len(cntsSorted[1:])) # gives the count of fingers
+
+    # approach 2 - eliminating wrist
+    # loop through the contours found
+    for i, c in enumerate(cnts):
+
+        # compute the bounding box of the contour
+        (x, y, w, h) = cv2.boundingRect(c)
+
+        # increment the count of fingers only if -
+        # 1. The contour region is not the wrist (bottom area)
+        # 2. The number of points along the contour does not exceed
+        #     25% of the circumference of the circular ROI
+        if ((cY + (cY * 0.25)) > (y + h)) and ((circumference * 0.25) > c.shape[0]):
+            count += 1
+
+    return count
+
+#-----------------
+# MAIN FUNCTION
+#-----------------
+if __name__ == "__main__":
+    # get the current frame
+    frame = cv2.imread("resources/hand-sample.jpg")
+
+    # resize the frame
+    frame = imutils.resize(frame, width=700)
+
+    # clone the frame
+    clone = frame.copy()
+
+    # get the height and width of the frame
+    (height, width) = frame.shape[:2]
+
+    # convert the frame to grayscale and blur it
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    gray = cv2.GaussianBlur(gray, (7, 7), 0)
+
+    # segment the hand region
+    hand = segment(clone, gray)
+
+    # check whether hand region is segmented
+    if hand is not None:
+        # if yes, unpack the thresholded image and segmented contour
+        (thresholded, segmented) = hand
+
+        # count the number of fingers
+        fingers = count(clone, thresholded, segmented)
+
+        cv2.putText(clone, "This is " + str(fingers), (70, 45), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
+        
+    # display the frame with segmented hand
+    cv2.imshow("Image", clone)
+
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()