You may use Use cv2.THRESH_BINARY_INV instead of cv2.THRESH_BINARY for finding pixels below thresh instead of pixels above thresh.
The reason you are detecting the floor, is that cv2.threshold marks (with 255 value), the pixels above thresh.
You want to mark the dark pixels below thresh.
You can compute: thresh_img = 255 - thresh_img, or use cv2.THRESH_BINARY_INV (result is the same).
I also recommend using closing morphological operation, for removing clutter.
Code for finding the tape:
import cv2
import numpy as np
img = cv2.imread('tape4.jpeg', cv2.IMREAD_UNCHANGED)
#convert img to grey
img_grey = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#set a thresh
thresh = 20 # Set thresh to very low value (the tape is almost black).
#get threshold image
# Use cv2.THRESH_BINARY_INV instead of cv2.THRESH_BINARY for finding pixels below thresh instead of pixels above thresh
ret,thresh_img = cv2.threshold(img_grey, thresh, 255, cv2.THRESH_BINARY_INV)
# Apply closing morphological operation
thresh_img = cv2.morphologyEx(thresh_img, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (21,21)));
#find contours
contours, hierarchy = cv2.findContours(thresh_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
#create an empty image for contours
img_contours = np.zeros(img.shape)
# draw the contours on the empty image
cv2.drawContours(img_contours, contours, -1, (0,255,0), 3)
#save image
cv2.imwrite('contours.png',img_contours)
Result:
For finding the tape, you can look for the largest contour within contours:
# Get contour with maximum area
c = max(contours, key=cv2.contourArea)
img_contours = np.zeros(img.shape)
cv2.drawContours(img_contours, [c], -1, (0,255,0), 3)
Result:
