erste test für initial commit

src_folder/BackEnd/CameraDetection/CameraDetection.py

@@ -0,0 +1,137 @@
+import cv2
+import numpy as np
+#  from Track import nothing
+# Farbwerte für die Erkennung (Beispiel: Rot, Grün, Blau)
+colors = [(0, 0, 255), (0, 255, 0), (255, 0, 0)]
+color_names = ["Rot", "Gruen", "Blau"]
+
+# Farbgrenzen für die Erkennung
+lower_red = np.array([0, 100, 100])
+upper_red = np.array([10, 255, 255])
+
+lower_green = np.array([40, 100, 100])
+upper_green = np.array([70, 255, 255])
+
+lower_blue = np.array([90, 100, 100])
+upper_blue = np.array([130, 255, 255])
+
+# Funktion zur Farberkennung
+def erkennung_farben(img):
+    hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    results = []
+
+    count_red = 0
+    count_green = 0
+    count_blue = 0
+
+    for i, color in enumerate(colors):
+        if i == 0:
+            lower = lower_red
+            upper = upper_red
+        elif i == 1:
+            lower = lower_green
+            upper = upper_green
+        elif i == 2:
+            lower = lower_blue
+            upper = upper_blue
+
+        mask = cv2.inRange(hsv_img, lower, upper)
+
+        # Farbfläche finden
+        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        center = None
+        count = 0
+
+        for contour in contours:
+            if count < 3:
+                if cv2.contourArea(contour) > 100:
+                    # Schwerpunkt der Kontur berechnen
+                    M = cv2.moments(contour)
+                    if M["m00"] > 0:
+                        cX = int(M["m10"] / M["m00"])
+                        cY = int(M["m01"] / M["m00"])
+                        center = (cX, cY)
+                        count += 1
+                        # Rechteck zeichnen
+                        x, y, w, h = cv2.boundingRect(contour)
+                        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
+                        cv2.putText(img, color_names[i], (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color, 2)
+
+                        # Farbanzahl erhöhen
+                        if i == 0:
+                            count_red += 1
+                        elif i == 1:
+                            count_green += 1
+                        elif i == 2:
+                            count_blue += 1
+
+
+
+        results.append(center)
+
+    return img, results, count_red, count_green, count_blue
+
+# Funktion zur Positionsermittlung
+def ermittle_position(results, img_width):
+    positions = []
+
+    for result in results:
+        if result is None:
+            position = "Nicht gefunden"
+        else:
+            x = result[0]
+            if x < img_width / 3:
+                position = "Rechts"
+            elif x < 2 * img_width / 3:
+                position = "Mitte"
+            else:
+                position = "Links"
+
+        positions.append(position)
+
+    return positions
+
+# Hauptprogramm
+if __name__ == "__main__":
+    # Videoquelle öffnen (kann auch eine Bilddatei sein)
+    video = cv2.VideoCapture(1)  # Hier "0" für die Kamera verwenden
+
+    while True:
+        # Einzelbild von der Videoquelle lesen
+        ret, frame = video.read()
+
+        # Fehlerbehandlung, wenn kein Bild gelesen werden kann
+        if not ret:
+            break
+
+        # Farben erkennen
+        farben_img, ergebnisse, count_red, count_green, count_blue = erkennung_farben(frame)
+
+        # Anzahl der Farben anzeigen
+        cv2.putText(farben_img, f"Rot: {count_red}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, colors[0], 2)
+        cv2.putText(farben_img, f"Gruen: {count_green}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, colors[1], 2)
+        cv2.putText(farben_img, f"Blau: {count_blue}", (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 1, colors[2], 2)
+
+        # Positionen ermitteln
+        img_width = frame.shape[1]
+        positionen = ermittle_position(ergebnisse, img_width)
+
+        # Positionen anzeigen
+        for i, position in enumerate(positionen):
+            cv2.putText(farben_img, f"{color_names[i]}: {position}", (10, 150 + 30 * i),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1, colors[i], 2)
+
+        # Linien zeichnen
+        cv2.line(farben_img, (img_width // 3, 0), (img_width // 3, frame.shape[0]), (0, 0, 0), 2)
+        cv2.line(farben_img, (2 * img_width // 3, 0), (2 * img_width // 3, frame.shape[0]), (0, 0, 0), 2)
+
+        # Bild anzeigen
+        cv2.imshow("Farberkennung", farben_img)
+
+        # Auf "q" drücken, um die Schleife zu beenden
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    # Videoquelle und Fenster schließen
+    video.release()
+    cv2.destroyAllWindows()

src_folder/BackEnd/CameraDetection/Track.py

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+import time
+import numpy as np
+import cv2
+
+cap = cv2.VideoCapture(0)
+
+def nothing(x):
+    pass
+
+
+cv2.namedWindow("Trackbars")
+cv2.createTrackbar("L - H", "Trackbars", 0, 255, nothing)
+cv2.createTrackbar("L - S", "Trackbars", 0, 255, nothing)
+cv2.createTrackbar("L - V", "Trackbars", 0, 255, nothing)
+cv2.createTrackbar("U - H", "Trackbars", 255, 255, nothing)
+cv2.createTrackbar("U - S", "Trackbars", 255, 255, nothing)
+cv2.createTrackbar("U - V", "Trackbars", 255, 255, nothing)
+
+while True:
+    ret, frame = cap.read()
+    frame = cv2.resize(frame, (640, 480))
+    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+
+    l_h = cv2.getTrackbarPos("L - H", "Trackbars")
+    l_s = cv2.getTrackbarPos("L - S", "Trackbars")
+    l_v = cv2.getTrackbarPos("L - V", "Trackbars")
+    u_h = cv2.getTrackbarPos("U - H", "Trackbars")
+    u_s = cv2.getTrackbarPos("U - S", "Trackbars")
+    u_v = cv2.getTrackbarPos("U - V", "Trackbars")
+    lower_range = np.array([l_h, l_s, l_v])
+    upper_range = np.array([u_h, u_s, u_v])
+    mask = cv2.inRange(hsv, lower_range, upper_range)
+    result = cv2.bitwise_and(frame, frame, mask=mask)
+
+    # show thresholded image
+    cv2.imshow("mask", mask)
+    cv2.imshow("result", result)
+
+    key = cv2.waitKey(1) & 0xFF
+    if key == ord("q"):
+        break
+cap.release()
+cv2.destroyAllWindows()
+