vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

modules/julia/samples/chessboard_corners.jl

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+using OpenCV
+
+const cv = OpenCV
+
+
+# chess1.png is at https://raw.githubusercontent.com/opencv/opencv_extra/master/testdata/cv/cameracalibration/chess1.png
+img = cv.imread("chess1.png",cv.IMREAD_GRAYSCALE)
+climg = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
+
+# Find the chess board corners
+ret, corners = cv.findChessboardCorners(img, cv.Size{Int32}(7,5))
+
+# If found, add object points, image points (after refining them)
+if ret
+    climg = cv.drawChessboardCorners(climg, cv.Size{Int32}(7,5), corners,ret)
+    cv.imshow("img",climg)
+    cv.waitKey(Int32(0))
+
+    cv.destroyAllWindows()
+end

modules/julia/samples/face_detect.jl

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+using OpenCV
+
+function detect(img::OpenCV.InputArray, cascade)
+    rects = OpenCV.detectMultiScale(cascade, img, scaleFactor=1.3, minNeighbors=Int32(4), minSize=OpenCV.Size{Int32}(30, 30), flags=OpenCV.CASCADE_SCALE_IMAGE)
+    processed_rects = []
+    for rect in rects
+        push!(processed_rects, (rect.x, rect.y, rect.width+rect.x, rect.height+rect.y))
+    end
+    return processed_rects
+end
+
+function draw_rects(img, rects, color)
+    for x in rects
+        OpenCV.rectangle(img, OpenCV.Point{Int32}(x[1], x[2]), OpenCV.Point{Int32}(x[3], x[4]), color, thickness = Int32(2))
+    end
+end
+
+cap = OpenCV.VideoCapture(Int32(0))
+
+# Replace the paths for the classifiers before running
+
+cascade = OpenCV.CascadeClassifier("haarcascade_frontalface_alt.xml")
+nested = OpenCV.CascadeClassifier("haarcascade_eye.xml")
+
+OpenCV.namedWindow("facedetect")
+
+while true
+    ret, img = OpenCV.read(cap)
+    if ret==false
+        print("Webcam stopped")
+        break
+    end
+    gray = OpenCV.cvtColor(img, OpenCV.COLOR_BGR2GRAY)
+    gray = OpenCV.equalizeHist(gray)
+
+    rects = detect(gray, cascade)
+    vis = copy(img)
+    draw_rects(vis, rects, (0.0, 255.0, 0.0))
+
+    if ~OpenCV.empty(nested)
+        for x in rects
+            roi = view(gray, :, Int(x[1]):Int(x[3]), Int(x[2]):Int(x[4]))
+            subrects = detect(roi, nested)
+            draw_view = view(vis, :, Int(x[1]):Int(x[3]), Int(x[2]):Int(x[4]))
+            draw_rects(draw_view, subrects, (255.0, 0.0, 0.0))
+        end
+    end
+
+    OpenCV.imshow("facedetect", vis)
+    if OpenCV.waitKey(Int32(5))==27
+        break
+    end
+end
+
+OpenCV.release(cap)
+
+OpenCV.destroyAllWindows()
+
+print("Stopped")

modules/julia/samples/face_detect_dnn.jl

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+using OpenCV
+const cv = OpenCV
+size0 = Int32(300)
+# take the model from https://github.com/opencv/opencv_extra/tree/master/testdata/dnn
+net = cv.dnn_DetectionModel("opencv_face_detector.pbtxt", "opencv_face_detector_uint8.pb")
+
+cv.dnn.setPreferableTarget(net, cv.dnn.DNN_TARGET_CPU)
+cv.dnn.setInputMean(net, (104, 177, 123))
+cv.dnn.setInputScale(net, 1.)
+cv.dnn.setInputSize(net, size0, size0)
+
+cap = cv.VideoCapture(Int32(0))
+while true
+    ok, frame = cv.read(cap)
+    if ok == false
+        break
+    end
+    classIds, confidences, boxes = cv.dnn.detect(net, frame, confThreshold=Float32(0.5))
+
+    for i in 1:size(boxes,1)
+        confidence = confidences[i]
+        x0 = Int32(boxes[i].x)
+        y0 = Int32(boxes[i].y)
+        x1 = Int32(boxes[i].x+boxes[i].width)
+        y1 = Int32(boxes[i].y+boxes[i].height)
+        cv.rectangle(frame, cv.Point{Int32}(x0, y0), cv.Point{Int32}(x1, y1), (100, 255, 100); thickness = Int32(5))
+        label = "face: " * string(confidence)
+        lsize, bl = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, Int32(1))
+        cv.rectangle(frame, cv.Point{Int32}(x0,y0), cv.Point{Int32}(x0+lsize.width, y0+lsize.height+bl), (100,255,100); thickness = Int32(-1))
+        cv.putText(frame, label, cv.Point{Int32}(x0, y0 + lsize.height),
+        cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0); thickness = Int32(1), lineType = cv.LINE_AA)
+    end
+
+
+    cv.imshow("detections", frame)
+    if cv.waitKey(Int32(30)) >= 0
+        break
+    end
+end

modules/julia/samples/filter2d.jl

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+using OpenCV
+const cv = OpenCV
+
+img = rand(UInt8, 3, 500, 500)
+filter = rand(Float32, 1, 5, 5)/25
+
+out = OpenCV.filter2D(img, Int32(-1), filter)
+
+cv.namedWindow("orig")
+cv.namedWindow("out")
+
+cv.imshow("orig", img)
+cv.imshow("out", out)
+
+cv.waitKey(Int32(0))

modules/julia/samples/simpleBlobDetector_test.jl

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+using OpenCV
+
+println("")
+println("This is a simple exmample demonstrating the use of SimpleBlobDetector")
+println("")
+print("Path to image: ")
+img_dir = readline()
+
+img = OpenCV.imread(img_dir)
+img_gray = OpenCV.cvtColor(img, OpenCV.COLOR_BGR2GRAY)
+
+OpenCV.namedWindow("Img - Color")
+OpenCV.namedWindow("Img - Gray")
+
+
+OpenCV.imshow("Img - Color", img)
+OpenCV.imshow("Img - Gray", img_gray)
+
+OpenCV.waitKey(Int32(0))
+
+OpenCV.destroyAllWindows()
+
+detector = OpenCV.SimpleBlobDetector_create()
+kps = OpenCV.detect(detector, img_gray)
+
+println("Number of keypoints: ", size(kps))
+for kp in kps
+    println(kp.pt, "\t", kp.size)
+end