Create README.md

Computer Vision/Object Size Detection/README.md

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+
+# Object Size Detection
+
+This Project creates an app where users can upload an image to measure the size of objects in it. After uploading, the app detects objects, measures their height and width based on a reference size, and displays the image with dimensions marked on each object. It also shows the total number of objects found and lists each object’s size.
+
+
+## Tech Stack
+
+**Languages:** Python
+
+**Libraries:**  opencv,numpy etc
+
+**Framework:** Streamlit
+
+## Project Structure
+
+Here's an overview of the main files and folders in this project:
+
+```plaintext
+├── app.py              
+├── requirements.txt     
+├── README.md         
+└── Results
+    ├── res1.png
+    ├── res2.png
+    └── res3.png   
+
+```
+## Screenshots
+
+![App Screenshot](Results/res1.png)
+![App Screenshot](Results/res2.png)
+
+## Run Locally
+
+Install dependencies
+
+```bash
+  pip install -r requirements.txt
+```
+
+Start the server
+
+```bash
+  streamlit run app.py
+```
+
+
+
+

Computer Vision/Object Size Detection/app.py

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+import streamlit as st
+from scipy.spatial import distance as dist
+from imutils import perspective
+from imutils import contours
+import numpy as np
+import imutils
+import cv2
+
+def midpoint(ptA, ptB):
+    return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
+
+def process_image(image, width):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    gray = cv2.GaussianBlur(gray, (7, 7), 0)
+
+    edged = cv2.Canny(gray, 50, 100)
+    edged = cv2.dilate(edged, None, iterations=1)
+    edged = cv2.erode(edged, None, iterations=1)
+
+    cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    cnts = imutils.grab_contours(cnts)
+    (cnts, _) = contours.sort_contours(cnts)
+
+    pixelsPerMetric = None
+    results = []
+
+    for c in cnts:
+        if cv2.contourArea(c) < 100:
+            continue
+
+        box = cv2.minAreaRect(c)
+        box = cv2.boxPoints(box)
+        box = np.array(box, dtype="int")
+        box = perspective.order_points(box)
+
+        (tl, tr, br, bl) = box
+        (tltrX, tltrY) = midpoint(tl, tr)
+        (blbrX, blbrY) = midpoint(bl, br)
+        (tlblX, tlblY) = midpoint(tl, bl)
+        (trbrX, trbrY) = midpoint(tr, br)
+
+        dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
+        dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
+
+        if pixelsPerMetric is None:
+            pixelsPerMetric = dB / width
+
+        dimA = dA / pixelsPerMetric
+        dimB = dB / pixelsPerMetric
+
+        results.append((box, dimA, dimB))
+
+    return results
+
+def draw_results(image, results):
+    for (box, dimA, dimB) in results:
+        cv2.drawContours(image, [box.astype("int")], -1, (0, 255, 0), 2)
+
+        (tl, tr, br, bl) = box
+        (tltrX, tltrY) = midpoint(tl, tr)
+        (blbrX, blbrY) = midpoint(bl, br)
+        (tlblX, tlblY) = midpoint(tl, bl)
+        (trbrX, trbrY) = midpoint(tr, br)
+
+        cv2.circle(image, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
+        cv2.circle(image, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
+        cv2.circle(image, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
+        cv2.circle(image, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
+
+        cv2.line(image, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)), (255, 0, 255), 2)
+        cv2.line(image, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)), (255, 0, 255), 2)
+
+        cv2.putText(image, "{:.1f}in".format(dimA), (int(tltrX - 15), int(tltrY - 10)),
+            cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 255), 2)
+        cv2.putText(image, "{:.1f}in".format(dimB), (int(trbrX + 10), int(trbrY)),
+            cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 255), 2)
+
+
+    return image
+
+st.title("Object Size Measurement")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+width = st.number_input("Width of the left-most object (in inches)", value=0.955, step=0.001)
+
+if uploaded_file is not None:
+    image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), 1)
+    results = process_image(image, width)
+
+    output_image = draw_results(image.copy(), results)
+
+    st.image(output_image, channels="BGR", caption="Processed Image")
+
+    st.write(f"Number of objects detected: {len(results)}")
+
+    for i, (_, dimA, dimB) in enumerate(results, 1):
+        st.write(f"Object {i}: {dimA:.1f}in x {dimB:.1f}in")

Computer Vision/Object Size Detection/requirements.txt

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+imutils==0.5.4
+numpy==2.1.2
+opencv_contrib_python==4.10.0.84
+opencv_python==4.10.0.82
+opencv_python_headless==4.10.0.84
+scipy==1.11.4
+streamlit==1.30.0
+

Project-Structure.md

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     * [Project](Computer%20Vision/Ball-Tracking/Project.ipynb)
   * Drowsiness Detection System
     * [Main](Computer%20Vision/Drowsiness%20Detection%20System/main.py)
+  * Object Size Detection
+    * [App](Computer%20Vision/Object%20Size%20Detection/app.py)
   * Volume Control Using Hand Gesture
     * [Gesturevolume](Computer%20Vision/Volume%20Control%20Using%20Hand%20Gesture/GestureVolume.py)
     * [Handtrackingmodule](Computer%20Vision/Volume%20Control%20Using%20Hand%20Gesture/HandTrackingModule.py)