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Import cv2import numpy as np# algorithm extraction work Gaussian algorithm class gaussian: def _ _ init__ (self): self.mean = np.zeros ((1,3)) self.covariance = 0 self.weight = 0 Self.Next = None self.Previous = Noneclass Node: def _ _ init__ (self): self.pixel_s = None self.pixel_r = None self.no_of_components = 0 self.Next = Noneclass Node1: def _ init__ (self): self.gauss = None self.no_of_comp = 0 self.Next = Nonecovariance0 = 11.0def Create_gaussian (info1, info2 Info3): ptr = gaussian () if (ptr is not None): ptr.mean [1,1] = info1 ptr.mean [1,2] = info2 ptr.mean [1,3] = info3 ptr.covariance = covariance0 ptr.weight = 0.002 ptr.Next = None ptr.Previous = None return ptrdef Create_Node (info1, info2 Info3): N_ptr = Node () if (N_ptr is not None): N_ptr.Next = None N_ptr.no_of_components = 1 N_ptr.pixel_s = N_ptr.pixel_r = Create_gaussian (info1, info2) Info3) return N_ptrList_node = [] def Insert_End_Node (n): List_node.append (n) List_gaussian = [] def Insert_End_gaussian (n): List_gaussian.append (n) def Delete_gaussian (n): List_gaussian.remove (n) Class Process: def _ init__ (self, alpha, firstFrame): self.alpha = alpha self.background = firstFrame def get_value (self, frame): self.background = frame * self.alpha + self.background * (1-self.alpha) return cv2.absdiff (self.background.astype (np.uint8), frame) def denoise (frame): frame = cv2.medianBlur (frame, 5) frame = cv2.GaussianBlur (frame, (5,5) 0) return framecapture = cv2.VideoCapture ('1.mp4') ret, orig_frame = capture.read () if ret is True: value1 = Process (0.1, denoise (orig_frame)) run = Trueelse: run = Falsewhile (run): ret, frame = capture.read () value = False If ret is True: cv2.imshow ('input', denoise (frame)) grayscale = value1.get_value (denoise (frame)) ret, mask = cv2.threshold (grayscale, 15,255, cv2.THRESH_BINARY) cv2.imshow (' mask') Mask) key = cv2.waitKey (10) & 0xFF else: break if key = = 27: break if value = = True: orig_frame = cv2.resize (orig_frame, (340,260), interpolation=cv2.INTER_CUBIC) orig_frame = cv2.cvtColor (orig_frame Cv2.COLOR_BGR2GRAY) orig_image_row = len (orig_frame) orig_image_col = orig_frame [0] bin_frame = np.zeros ((orig_image_row, orig_image_col)) value = [] for i in range (0, orig_image_row): for j in range (0) Orig_image_col): N_ptr = Create_Node (orig_ framework [I] [0], orig_ framework [I] [1] Orig_ frame [I] [2]) if N_ptr is not None: N_ptr.pixel_s.weight = 1.0Insert_End_Node (N_ptr) else: print ("error") exit (0) nL = orig_image_row NC = orig_image_col dell = np.array ((1 3)) Mal_dist = 0; temp_cov = 0; alpha = 0.002; cT = 0.05; cf = 0.1; cfbar = 1.0-cf; alpha_bar = 1.0-alpha; prune =-alpha * cT; cthr = 0.00001; var = 0.0 muG = 0.0; muR = 0.0 MuB = 0; dR = 0; dB = 0; dG = 0; rval = 0; gval = 0; bval = 0; while (1): duration3 = 0; count = 0; count1 = 0; List_node1 = List_node Counter = 0; duration = cv2.getTickCount (); for i in range (0, nL): r_ptr = orig_ framework [I] b_ptr = bin_ framework [I] for j in range (0, nC): sum = 0.0; sum1 = 0.0 Close = False; background = 0; rval = r_ptr [0] [0]; gval = r_ptr [0] [0]; bval = r_ptr [0] [0]; start = List_ Node1 [counter] .pixel _ s Rear = List_ Node1 [counter] .pixel _ r; ptr = start; temp_ptr = None; if (List_ Node1 [counter] .no _ of_component > 4): Delete_gaussian (rear) List_ Node1 [counter] .no _ of_component = List_ Node1 [counter] .no _ of_component-1; for k in range (0, List_ Node1 [counter] .no _ of_component): weight = List_ Node1 [counter] .weight; mult = alpha / weight Weight = weight * alpha_bar + prune; if (close = = False): muR = ptr.mean [0]; muG = ptr.mean [1]; muB = ptr.mean [2]; dR = rval-muR DG = gval-muG; dB = bval-muB; var = ptr.covariance; mal_dist = (dR * dR + dG * dG + dB * dB); if ((sum

< cfbar) and (mal_dist < 16.0 * var * var)): background = 255; if (mal_dist < (9.0 * var * var)): weight = weight + alpha; if mult < 20.0 * alpha: mult = mult; else: mult = 20.0 * alpha; close = True; ptr.mean[0] = muR + mult * dR; ptr.mean[1] = muG + mult * dG; ptr.mean[2] = muB + mult * dB; temp_cov = var + mult * (mal_dist - var); if temp_cov < 5.0: ptr.covariance = 5.0 else: if (temp_cov >

20): ptr.covariance = 20.0 else: ptr.covariance = temp_cov; temp_ptr = ptr If (weight <-prune): ptr = Delete_gaussian (ptr); weight = 0; List_ Node1 [counter] .no _ of_component = List_ node1 [counter] .no _ of_component-1 Else: sum + = weight; ptr.weight = weight; ptr = ptr.Next; if (close = = False): ptr = gaussian (); ptr.weight = alpha Ptr.mean [0] = rval; ptr.mean [1] = gval; ptr.mean [2] = bval; ptr.covariance = covariance0; ptr.Next = None; ptr.Previous = None Insert_End_gaussian (ptr); List_gaussian.append (ptr); temp_ptr = ptr; List_ Node1 [counter] .no _ of_components = List_ Node1 [counter] .no _ of_components + 1; ptr = start While (ptr! = None): ptr.weight = ptr.weight / sum; ptr = ptr.Next; while (temp_ptr! = None and temp_ptr.Previous! = None): if (temp_ptr.weight

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