Add files via upload

Computer Vision/Object Size Detection/app.py

@@ -0,0 +1,97 @@
+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

@@ -0,0 +1,8 @@
+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
+