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Author: JKL404

Diff for: README.md

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+# Detect an object with OpenCV-Python
+## _JKL404_
+
+
+[![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger)
+
+OpenCV is the huge open-source library for computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems. By using it, one can process images and videos to identify objects, faces, or even the handwriting of a human. This article focuses on detecting objects.
+![Cloud Vision](https://nanonets.com/blog/content/images/2021/04/cloud--1--1.gif)
+
+## Features
+
+- Use machine learning to understand your images with industry-leading prediction accuracy
+- Train machine learning models that classify images by your custom labels
+- Detect objects and faces
+
+
+## Installation
+
+
+![google cloud](https://images.idgesg.net/images/article/2018/04/google-vision-api-screen-1-100755937-medium.jpg?auto=webp&quality=85,70)
+
+
+
+
+Install the dependencies and devDependencies and start the server.
+
+```sh
+pip install flask, python, opencv-python, numpy, matplotlib
+python main.py
+```
+
+Verify the deployment by navigating to your server address in
+your preferred browser.
+
+```sh
+127.0.0.1:8000
+```
+## Demo
+
+## License
+
+MIT
+
+**Free Software, Hell Yeah!**

Diff for: app.py

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+from flask import Flask
+
+UPLOAD_FOLDER = 'static/uploads/'
+
+app = Flask(__name__)
+app.secret_key = "secret key"
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024
+
+#export GOOGLE_APPLICATION_CREDENTIALS=

Diff for: main.py

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+import os
+from app import app
+import urllib.request
+from werkzeug.utils import secure_filename
+from flask import Flask, flash, request, redirect, url_for, render_template
+
+
+@app.route('/')
+def home():
+	return render_template('index.html')
+
+
+@app.route('/textdetect/')
+def upload_form():
+	return render_template('upload.html')
+
+@app.route('/textdetect/', methods=['POST'])
+def upload_image():
+	#Code to run main scan file
+    import cv2
+    import numpy as np
+    import matplotlib.pyplot as plt
+    import time
+    # Load webcam
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    starting_time = time.time()
+    frame_id = 0
+    net = cv2.dnn.readNet("./weights/yolov3-tiny.weights", "./configuration/yolov3-tiny.cfg")
+	### Change here for custom classes for trained model
+    classes = []
+    mylist = []
+    flag = 0
+    with open("./configuration/coco.names", "r") as f:
+	     classes = [line.strip() for line in f.readlines()]
+    # Load webcam
+    cap = cv2.VideoCapture(0)
+    colors = np.random.uniform(0, 255, size=(len(classes), 3))
+    while 1:
+        _, img = cap.read()
+        frame_id += 1
+        img = cv2.resize(img,(1280,720))
+        hight,width,_ = img.shape
+        blob = cv2.dnn.blobFromImage(img, 1/255,(416,416),(0,0,0),swapRB = True,crop= False)
+
+        net.setInput(blob)
+
+        output_layers_name = net.getUnconnectedOutLayersNames()
+
+        layerOutputs = net.forward(output_layers_name)
+
+        boxes =[]
+        confidences = []
+        class_ids = []
+
+
+        for output in layerOutputs:
+            for detection in output:
+                score = detection[5:]
+                class_id = np.argmax(score)
+                confidence = score[class_id]
+                if confidence > 0.1:
+                    center_x = int(detection[0] * width)
+                    center_y = int(detection[1] * hight)
+                    w = int(detection[2] * width)
+                    h = int(detection[3]* hight)
+                    x = int(center_x - w/2)
+                    y = int(center_y - h/2)
+                    boxes.append([x,y,w,h])
+                    confidences.append((float(confidence)))
+                    class_ids.append(class_id)
+
+        indexes = cv2.dnn.NMSBoxes(boxes,confidences, 0.8, 0.3)
+        for i in range(len(boxes)):
+            if i in indexes:
+                x, y, w, h = boxes[i]
+                label = str(classes[class_ids[i]])
+                confidence = confidences[i]
+                color = colors[class_ids[i]]
+                cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
+                cv2.putText(img, label + " " + str(round(confidence, 2)), (x, y + 30), font, 3, color, 3)
+                flag=0
+                for ls in mylist:
+                    if ls is label:
+                       flag=1
+                if flag != 1:
+                    mylist.append(label)
+        elapsed_time = time.time() - starting_time
+        fps = frame_id / elapsed_time
+        cv2.putText(img, "FPS: " + str(round(fps, 2)), (40, 670), font, .7, (0, 255, 255), 1)
+        cv2.putText(img, "press [esc] to exit", (40, 690), font, .45, (0, 255, 255), 1)
+        cv2.imshow("Image", img)
+        key = cv2.waitKey(1)
+        if key == 27:
+            print("[button pressed] ///// [esc].")
+            print("[feedback] ///// Videocapturing succesfully stopped")
+            break
+    cap.release()
+    cv2.destroyAllWindows()
+    return render_template('message.html' , itemss=mylist )
+
+if __name__ == "__main__":
+    app.run()