cattori-codes/hand_detect.py at main · Dancing-Dog69/cattori-codes · GitHub

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cam = cv2.VideoCapture(0)
num_frames =0
while True:
    ret, frame = cam.read()

    # flipping the frame to prevent inverted image of captured
    frame...

    frame = cv2.flip(frame, 1)

    frame_copy = frame.copy()

    # ROI from the frame
    roi = frame[ROI_top:ROI_bottom, ROI_right:ROI_left]

    gray_frame = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
    gray_frame = cv2.GaussianBlur(gray_frame, (9, 9), 0)


    if num_frames < 70:

        cal_accum_avg(gray_frame, accumulated_weight)

        cv2.putText(frame_copy, "FETCHING BACKGROUND...PLEASE WAIT",
  (80, 400), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0,0,255), 2)

    else:
        # segmenting the hand region
        hand = segment_hand(gray_frame)

        # Checking if we are able to detect the hand...
        if hand is not None:

            thresholded, hand_segment = hand

            # Drawing contours around hand segment
            cv2.drawContours(frame_copy, [hand_segment + (ROI_right,
      ROI_top)], -1, (255, 0, 0),1)

            cv2.imshow("Thesholded Hand Image", thresholded)

            thresholded = cv2.resize(thresholded, (64, 64))
            thresholded = cv2.cvtColor(thresholded,
 cv2.COLOR_GRAY2RGB)
            thresholded = np.reshape(thresholded,
(1,thresholded.shape[0],thresholded.shape[1],3))

            pred = model.predict(thresholded)
            cv2.putText(frame_copy, word_dict[np.argmax(pred)],
(170, 45), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)

    # Draw ROI on frame_copy
    cv2.rectangle(frame_copy, (ROI_left, ROI_top), (ROI_right,
    ROI_bottom), (255,128,0), 3)

    # incrementing the number of frames for tracking
    num_frames += 1

    # Display the frame with segmented hand
    cv2.putText(frame_copy, "DataFlair hand sign recognition_ _ _",
    (10, 20), cv2.FONT_ITALIC, 0.5, (51,255,51), 1)
    cv2.imshow("Sign Detection", frame_copy)


    # Close windows with Esc
    k = cv2.waitKey(1) & 0xFF

    if k == 27:
        break

# Release the camera and destroy all the windows
cam.release()
cv2.destroyAllWindows()