algaedetectFRC.py

# For usage with robot external webcam
import cv2
import numpy as np
import depthai as dai
import time

def robot():
    # Create pipeline
    pipeline = dai.Pipeline()
    cam = pipeline.create(dai.node.ColorCamera)
    cam.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
    cam.setPreviewSize(1280,720)
    xout = pipeline.create(dai.node.XLinkOut)
    xout.setStreamName("rgb")
    cam.preview.link(xout.input)

    # Connect to device and start pipeline
    with dai.Device(pipeline) as device:
        qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
            
    #cam = cv2.VideoCapture(0)
        while True:
            start = time.perf_counter()
            frame = qRgb.get().getFrame()
            #ret, frame = cam.read()
        
            hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) 
            
            # Threshold of blue in HSV space 
            lower_blue = np.array([60, 88, 18])
            upper_blue = np.array([93, 255, 255])
        
            # preparing the mask to overlay 
            mask = cv2.inRange(hsv, lower_blue, upper_blue) 
            blurry_mask = cv2.GaussianBlur(mask, (21,21), 0) 
            
        # Apply Hough transform on the blurred image. 
            detected_circles = cv2.HoughCircles(blurry_mask,  
                            cv2.HOUGH_GRADIENT, 1, 70, param1 = 260, 
                        param2 = 30, minRadius = 50, maxRadius = 280) 
            
            # Draw circles that are detected and the frame rate. 
            detections = {}

            if detected_circles is not None: 
                # Convert the circle parameters a, b and r to integers. 
                detected_circles = np.uint16(np.around(detected_circles)) 
                count = 0
                for pt in detected_circles[0, :]: 
                    cx, cy, r = pt[0], pt[1], pt[2] 
                    #detections.update()
                    algae = {}
                    algae["cx"] = int(cx)
                    algae["cy"] = int(cy)
                    algae["r"] = int(r)
                    
                    detections[f"algae{count}"] = algae
                    count += 1
                    # Draw the circumference of the circle. 
                    cv2.circle(frame, (cx, cy), r, (0, 255, 0), 2)
                    cv2.circle(blurry_mask, (cx, cy), r, (0, 255, 255), 2) 
                    # Draw a small circle (of radius 1) to show the center. 
                    cv2.circle(frame, (cx, cy), 1, (0, 255, 0), 3) 
                    cv2.circle(blurry_mask, (cx, cy), 1, (0, 0, 255), 3) 
            #Draw the frame rate onto the frame
            end = time.perf_counter()
            fps = 1/(end-start)
            cv2.putText(blurry_mask, f"Frame Rate: {int(fps)}",(7,70), cv2.FONT_HERSHEY_SIMPLEX , 3, (255,255,255), 2, cv2.LINE_AA)

            cv2.imshow("Full Color Detection", frame) 
            cv2.imshow("Masked Detection", blurry_mask) 
            time.sleep(0.002)
            print(detections)
            if cv2.waitKey(1) == ord("q"):
                break

    cv2.destroyAllWindows()



def webcam():
    # For usage with computer webcam
    import cv2
    import numpy as np
    import time

    cam = cv2.VideoCapture(0)
    while True:
        start = time.perf_counter()
        ret, frame = cam.read()
        
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) 
        
        # Threshold of blue in HSV space 
        lower_blue = np.array([60, 88, 18])
        upper_blue = np.array([93, 255, 255])

        # preparing the mask to overlay 
        mask = cv2.inRange(hsv, lower_blue, upper_blue) 
        blurry_mask = cv2.GaussianBlur(mask, (21,21), 0) 
        
    # Apply Hough transform on the blurred image. 
        detected_circles = cv2.HoughCircles(blurry_mask,  
                        cv2.HOUGH_GRADIENT, 1, 70, param1 = 260, 
                    param2 = 30, minRadius = 50, maxRadius = 280) 
        
        # Draw circles that are detected and the frame rate. 
        detections = {}

        if detected_circles is not None: 
            # Convert the circle parameters a, b and r to integers. 
            detected_circles = np.uint16(np.around(detected_circles)) 
            count = 0
            for pt in detected_circles[0, :]: 
                cx, cy, r = pt[0], pt[1], pt[2] 
                #detections.update()
                algae = {}
                algae["cx"] = int(cx)
                algae["cy"] = int(cy)
                algae["r"] = int(r)
                
                detections[f"algae{count}"] = algae
                count += 1
                # Draw the circumference of the circle. 
                cv2.circle(frame, (cx, cy), r, (0, 255, 0), 2)
                cv2.circle(blurry_mask, (cx, cy), r, (0, 255, 255), 2) 
                # Draw a small circle (of radius 1) to show the center. 
                cv2.circle(frame, (cx, cy), 1, (0, 255, 0), 3) 
                cv2.circle(blurry_mask, (cx, cy), 1, (0, 0, 255), 3) 

        #Draw the frame rate onto the frame
        end = time.perf_counter()
        fps = 1/(end-start)
        cv2.putText(blurry_mask, f"Frame Rate: {int(fps)}",(7,70), cv2.FONT_HERSHEY_SIMPLEX , 3, (255,255,255), 2, cv2.LINE_AA)

        cv2.imshow("Full Color Detection", frame) 
        cv2.imshow("Masked Detection", blurry_mask) 
        time.sleep(0.002)
        print(detections)
        if cv2.waitKey(1) == ord("q"):
            break

    cv2.destroyAllWindows()