reinitialzing library for computer vision essesntials

Author: codeperfectplus

01. Basic operations/drawing function in opencv.py renamed to 01. Basic operations/drawingOnImage.py

@@ -1,11 +1,17 @@
+""" Common object detection using CvLib and yolo3 """
+
 import cv2
 import matplotlib.pyplot as plt
 import cvlib as cv
 from cvlib.object_detection import draw_bbox
 
-im = cv2.imread('apple.jpeg')
-bbox, label, conf = cv.detect_common_objects(im)
+img = cv2.imread('./Media/apple.jpeg')
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+bbox, label, conf = cv.detect_common_objects(gray)
+
 output_image = draw_bbox(im, bbox, label, conf)
+
 plt.imshow(output_image)
-plt.savefig("apple-detected.jpeg")
+plt.savefig("./Media/apple-detected.jpeg")
 plt.show()

01. Basic operations/getting start with image.py renamed to 01. Basic operations/gettingStarted.py

@@ -0,0 +1,92 @@
+import os
+import face_recognition
+import cv2
+import numpy as np
+
+video_capture = cv2.VideoCapture(0)
+
+
+# Create arrays of known face encodings and their names
+known_face_encodings = []
+known_face_names = []
+
+root_dir = os.path.dirname(os.path.abspath(os.path.abspath(__file__)))
+image_dir = os.path.join(root_dir, "images")
+
+for file in os.listdir(image_dir):
+    if file.endswith == ".jpeg" or ".jpg":
+        input_face_name = file.split('.')[0]
+        input_face = face_recognition.load_image_file(os.path.join(image_dir, file))
+        input_face_encoding = face_recognition.face_encodings(input_face)[0]
+        known_face_names.append(input_face_name)
+        known_face_encodings.append(input_face_encoding)
+
+# Initialize some variables
+face_locations = []
+face_encodings = []
+face_names = []
+process_this_frame = True
+
+while True:
+    # Grab a single frame of video
+    ret, frame = video_capture.read()
+
+    # Resize frame of video to 1/4 size for faster face recognition processing
+    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
+
+    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
+    rgb_small_frame = small_frame[:, :, ::-1]
+
+    # Only process every other frame of video to save time
+    if process_this_frame:
+        # Find all the faces and face encodings in the current frame of video
+        face_locations = face_recognition.face_locations(rgb_small_frame)
+        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
+
+        face_names = []
+        for face_encoding in face_encodings:
+            # See if the face is a match for the known face(s)
+            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+            name = "Unknown"
+
+            # # If a match was found in known_face_encodings, just use the first one.
+            # if True in matches:
+            #     first_match_index = matches.index(True)
+            #     name = known_face_names[first_match_index]
+
+            # Or instead, use the known face with the smallest distance to the new face
+            face_distances = face_recognition.face_distance(known_face_encodings, 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)
+    process_this_frame = not process_this_frame
+
+
+    # Display the results
+    for (top, right, bottom, left), name in zip(face_locations, face_names):
+        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
+        top *= 4
+        right *= 4
+        bottom *= 4
+        left *= 4
+
+        # Draw a box around the face
+        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
+
+        # Draw a label with a name below the face
+        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
+        font = cv2.FONT_HERSHEY_DUPLEX
+        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
+
+    # Display the resulting image
+    cv2.imshow('Video', frame)
+
+    # Hit 'q' on the keyboard to quit!
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# Release handle to the webcam
+video_capture.release()
+cv2.destroyAllWindows()

01. Basic operations/getting start with videos.py renamed to 01. Basic operations/gettingStartedWithVideos.py

@@ -0,0 +1,21 @@
+## Usgaes
+
+1. Add Your Image in `Images` folder. exmaple >> name.jpeg
+
+2. Install the requirements file
+
+```bash
+python -m pip install -r requirements.txt
+```
+
+3. Run the Script
+
+```bash
+python FaceRec.py
+```
+
+NOTE:- incase installtation stuck on dlib
+
+```bash
+python -m pip install dlib -vvv 
+```

01. Basic operations/saving Video from camera.py renamed to 01. Basic operations/savingVideosFromCamera.py

@@ -1,9 +1,8 @@
-# OpenCv Tutorial
+# Computer Vision Essentials
 
-<img align="right" src="Media/opencv-logo-white.png">
-
-- [OpenCv Tutorial](#opencv-tutorial)
+- [Computer Vision Essentials](#computer-vision-essentials)
   - [Introduction](#introduction)
+  - [Used Libraries/Packages](#used-librariespackages)
   - [How To Run](#how-to-run)
   - [Usage](#usage)
   - [Support](#support)
@@ -16,19 +15,42 @@
 
 ## Introduction
 
-OpenCV is a cross-platform library using which we can develop real-time computer vision applications. It mainly focuses on image processing, video capture and analysis including features like face detection and object detection. In this tutorial, we explain how you can use OpenCV in your applications.
+According to [wikipedia](https://en.wikipedia.org/wiki/Computer_vision, "computer_vision-Wikipedia") -
+
+Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.
+
+Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions.
+
+[Read More ...](https://en.wikipedia.org/wiki/Computer_vision, "computer_vision-Wikipedia")
+
+## Used Libraries/Packages
+
+- **OpenCV** - OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. 
+- **CVLib** - A simple, high level, easy-to-use open source Computer Vision library for Python.
+- **Dlib** - Dlib is a general purpose cross-platform software library written in the programming language C++. 
+- **PIL/Pillow** - Python Imaging Library is a free and open-source additional library for the Python programming language that adds support for opening, manipulating, and saving many different image file formats
+- **Keras** - Keras is the most used deep learning framework among top-5 winning teams on Kaggle. 
+- **Tensorflow** - TensorFlow is a free and open-source software library for machine learning.
+- **Pytessarct** - Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and “read” the text embedded in images.
+- **IPSDK** - IPSDK offers a comprehensive and optimized range of functionalities for 2D and 3D image processing.
+- **scikit-image** - scikit-image is an open-source image processing library for the Python programming language. It includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more.
+- **Matplotlib** - Matplotlib is a cross-platform, data visualization and graphical plotting library for Python and its numerical extension NumPy.
+- **mahotas** - Mahotas is a computer vision and image processing library for Python.
 
 ## How To Run
 
 - Install python 3.6+
 
 Create virtual envionment with `pipenv`.
+
 ```bash
 python -m pip install pipenv
 pipenv install -r requirements.txt
 pipenv shell
 ```
 
+NOTE- check the [guide](https://www.tensorflow.org/install) for tenosflow installation for your CPU/GPU. for using tensorflow-gpu install the CUDA-11.0 and necessary libraries.
+
 ## Usage
 
 Computer vision allows the computer to perform the same kind of tasks as humans with the same efficiency. There are a two main task which are defined below:
@@ -75,7 +97,7 @@ For open source projects,Under MIT License.
 
 ## Author
 
-- Project : OpenCv Tutorial
+- Project : Computer Vision Essentials
 - Language : Python
 - Github : <https://github.com/codePerfectPlus>
 - Website : <http://codeperfectplus.herokuapp.com>