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240
semester_4/osnove_racunalniskega_vida/Naloga_4/implement.py
Normal file
240
semester_4/osnove_racunalniskega_vida/Naloga_4/implement.py
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@@ -0,0 +1,240 @@
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import cv2
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import numpy as np
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locations = np.empty((0,2), dtype=np.int32)
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def click_event(event, x, y, flags, params):
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global locations
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if event == cv2.EVENT_LBUTTONDOWN:
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print(img[y][x])
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locations = np.append(locations, [[y,x]] , axis=0)
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def selectPointsM(img):
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global locations
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cv2.namedWindow('Select color')
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cv2.setMouseCallback('Select color', click_event)
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while True:
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cv2.imshow('Select color', img)
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if cv2.waitKey(1) & 0xFF == 27:
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break
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cv2.destroyAllWindows()
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return locations
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def MeanShift(src, window, criteria):
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for _ in range(criteria[1]):
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ret, window = Iteration(src, window, criteria[2])
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if ret:
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return (True, window)
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return (False, window)
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def Iteration(src, window, criteria):
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if window[0] < 0:
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window = (0, window[1], window[2], window[3])
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if window[1] < 0:
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window = (window[0], 0, window[2], window[3])
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roi = src[window[1]:window[1]+window[3], window[0]:window[0]+window[2]]
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#iteracija 1
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# x = 0
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# y = 0
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# num = 0
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# for i in range(roi.shape[0]):
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# for j in range(roi.shape[1]):
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# if roi[i][j] > 0:
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# x += j
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# y += i
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# num += 1
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#iteracija 2
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# nonzero_indices = np.nonzero(roi)
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# nonzero_values = roi[nonzero_indices]
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# x = np.sum(nonzero_indices[1])
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# y = np.sum(nonzero_indices[0])
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# num = np.count_nonzero(nonzero_values)
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#iteracija 3
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mom = cv2.moments(roi)
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x = 0
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y = 0
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if mom['m00'] != 0:
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x = int(mom['m10'] / mom['m00'])
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y = int(mom['m01'] / mom['m00'])
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x = int(x - window[2] / 2)
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y = int(y - window[3] / 2)
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window = (window[0] + x, window[1] + y, window[2], window[3])
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if x < criteria and y < criteria:
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return (True, window)
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return (False, window)
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def CamShift(src, window, criteria):
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ret = False
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for _ in range(criteria[1]):
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ret, window = Iteration(src, window, criteria[2])
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window = changeSize(src, window)
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return (ret, window)
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def changeSize(src, window):
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roi = src[window[1]:window[1]+window[3], window[0]:window[0]+window[2]]
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mom = cv2.moments(roi)
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ratio = window[2] / window[3]
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if mom['m00'] != 0:
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w = 2 * np.sqrt(mom['m00'] / 256)
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h = w / ratio
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window = (window[0], window[1], int(w), int(h))
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if window[0] < 0:
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window = (0, window[1], window[2], window[3])
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if window[1] < 0:
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window = (window[0], 0, window[2], window[3])
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if window[0] + window[2] > src.shape[1]:
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window = (src.shape[1] - window[2], window[1], window[2], window[3])
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if window[1] + window[3] > src.shape[0]:
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window = (window[0], src.shape[0] - window[3], window[2], window[3])
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if window[2] < 10:
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window = (window[0], window[1], 10, window[3])
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if window[3] < 10:
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window = (window[0], window[1], window[2], 10)
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return window
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def selectObjectH(frame, target_hist):
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target_hist = target_hist.astype(np.float32)
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cv2.normalize(target_hist, target_hist, 0, 1, cv2.NORM_MINMAX)
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hsvImg = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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h_channel = hsvImg[:, :, 0]
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#s_channel = hsvImg[:, :, 1]
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#probabilityImg = np.zeros(shape=(hsvImg.shape[0], hsvImg.shape[1], 1) , dtype=np.float32)
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# Stack the H and S channels
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# hs_img = np.stack((h_channel, s_channel), axis=-1)
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# for i in range(hs_img.shape[0]):
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# for j in range(hs_img.shape[1]):
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# probabilityImg[i][j] = target_hist[int(h_channel[i][j])]
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# satProp = target_hist[int(hs_img[i][j][1])]
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# probabilityImg[i][j] = hueProp * satProp
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probabilityImg = target_hist[h_channel.astype(np.uint8)]
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cv2.normalize(probabilityImg, probabilityImg, 0, 255, cv2.NORM_MINMAX)
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probabilityImg = probabilityImg.astype(np.uint8)
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mask = cv2.inRange(probabilityImg, 100, 255)
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contour, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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maxContour = max(contour, key=cv2.contourArea)
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x, y, w, h = cv2.boundingRect(maxContour)
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return (x, y, w, h)
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def selectObjectHS(frame, target_hist):
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target_hist = target_hist.astype(np.float32)
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cv2.normalize(target_hist, target_hist, 0, 1, cv2.NORM_MINMAX)
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hsvImg = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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# probabilityImg = np.zeros(shape=(hsvImg.shape[0], hsvImg.shape[1], 1) , dtype=np.float32)
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# for i in range(hsvImg.shape[0]):
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# for j in range(hsvImg.shape[1]):
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# hueProp = target_hist[int(hsvImg[i][j][0])][0]
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# satProp = target_hist[int(hsvImg[i][j][1])][1]
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# probabilityImg[i][j] = hueProp * satProp
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hue_values = hsvImg[..., 0].astype(np.uint8)
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sat_values = hsvImg[..., 1].astype(np.uint8)
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hue_props = target_hist[hue_values.flatten(), 0].reshape(hsvImg.shape[:2])
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sat_props = target_hist[sat_values.flatten(), 1].reshape(hsvImg.shape[:2])
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probabilityImg = (hue_props * sat_props).astype(np.float32)
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cv2.normalize(probabilityImg, probabilityImg, 0, 255, cv2.NORM_MINMAX)
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probabilityImg = probabilityImg.astype(np.uint8)
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mask = cv2.inRange(probabilityImg, 100, 255)
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contour, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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maxContour = max(contour, key=cv2.contourArea)
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x, y, w, h = cv2.boundingRect(maxContour)
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return (x, y, w, h)
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def selectObject(frame, target_hist):
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if target_hist.shape[0] == 180:
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return selectObjectH(frame, target_hist)
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else:
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return selectObjectHS(frame, target_hist)
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def calcHistH(roi):
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histogram = np.zeros(180, dtype=int)
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for i in range(roi.shape[0]):
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for j in range(roi.shape[1]):
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histogram[roi[i][j][0]] += 1
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return histogram
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def calcHistHS(roi):
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histogramH = np.zeros(256, dtype=int)
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histogramS = np.zeros(256, dtype=int)
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for i in range(roi.shape[0]):
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for j in range(roi.shape[1]):
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histogramH[roi[i][j][0]] += 1
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histogramS[roi[i][j][1]] += 1
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histogram = np.stack(arrays=(histogramH, histogramS), axis=-1)
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return histogram
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def calcHist(roi, numOfchannels=1):
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if numOfchannels == 1:
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return calcHistH(roi)
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else:
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return calcHistHS(roi)
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def calcBackProjectH(frame, target_hist):
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target_hist = target_hist.astype(np.float32)
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cv2.normalize(target_hist, target_hist, 0, 1, cv2.NORM_MINMAX)
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hsvImg = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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h_channel = hsvImg[:, :, 0]
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probabilityImg = target_hist[h_channel.astype(np.uint8)]
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cv2.normalize(probabilityImg, probabilityImg, 0, 255, cv2.NORM_MINMAX)
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return probabilityImg.astype(np.uint8)
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def calcBackProjectHS(frame, target_hist):
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target_hist = target_hist.astype(np.float32)
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cv2.normalize(target_hist, target_hist, 0, 1, cv2.NORM_MINMAX)
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hsvImg = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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hue_values = hsvImg[..., 0].astype(np.uint8)
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sat_values = hsvImg[..., 1].astype(np.uint8)
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hue_props = target_hist[hue_values.flatten(), 0].reshape(hsvImg.shape[:2])
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sat_props = target_hist[sat_values.flatten(), 1].reshape(hsvImg.shape[:2])
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probabilityImg = (hue_props * sat_props).astype(np.float32)
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cv2.normalize(probabilityImg, probabilityImg, 0, 255, cv2.NORM_MINMAX)
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return probabilityImg.astype(np.uint8)
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def calcBackProject(frame, target_hist):
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if target_hist.shape[0] == 180:
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return calcBackProjectH(frame, target_hist)
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else:
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return calcBackProjectHS(frame, target_hist)
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122
semester_4/osnove_racunalniskega_vida/Naloga_4/main.py
Normal file
122
semester_4/osnove_racunalniskega_vida/Naloga_4/main.py
Normal file
@@ -0,0 +1,122 @@
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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from implement import MeanShift, CamShift, selectObject, calcHist
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if True:
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capture = cv2.VideoCapture(0)
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flip = True
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else:
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capture = cv2.VideoCapture("colors_Trim.mp4")
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flip = False
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track_window = None
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global xLZ
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global yLZ
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global xLast
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global yLast
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global frame
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xLZ = 0
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yLZ = 0
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def clickOnImage(event, x, y, flags, param):
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global frame, xLZ, yLZ, xLast, yLast
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if event == cv2.EVENT_LBUTTONUP:
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#print("EVENT_LBUTTONUP ({},{})".format(x,y))
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xLZ = 0
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yLZ = 0
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if event == cv2.EVENT_LBUTTONDOWN:
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#print("EVENT_LBUTTONDOWN ({},{})".format(x,y))
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xLZ = x
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yLZ = y
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if event == cv2.EVENT_MOUSEMOVE:
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#print("EVENT_MOUSEMOVE ({},{})".format(x,y))
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xLast = x
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yLast = y
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cv2.namedWindow("Slika")
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cv2.setMouseCallback("Slika", clickOnImage)
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target_hist = None
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while(1):
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ret, frame = capture.read()
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if ret == True:
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if flip:
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frame = cv2.flip(frame, 1)
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frameCopy = frame.copy()
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if xLZ != 0 or yLZ != 0:
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cv2.rectangle(frameCopy,(xLZ,yLZ),(xLast,yLast),(0,255,0),2)
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cv2.imshow('Slika',frameCopy)
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if cv2.waitKey(100) & 0xFF == ord("q"):
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miniFrame = frame[yLZ:yLast,xLZ:xLast]
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miniFrame = cv2.cvtColor(miniFrame, cv2.COLOR_BGR2HSV)
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target_hist = calcHist(miniFrame,2)
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cv2.destroyAllWindows()
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break
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if target_hist is None:
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print("No target selected")
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exit(1)
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track_window = selectObject(frame, target_hist)
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cv2.rectangle(frame, (track_window[0], track_window[1]), (track_window[0]+track_window[2], track_window[1]+track_window[3]), (0, 255, 0), 2)
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cv2.imshow("Frame", frame)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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firstFrame = None
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while True:
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ret, frame = capture.read()
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if not ret:
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break
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if flip:
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frame = cv2.flip(frame, 1)
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if firstFrame is None:
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firstFrame = frame.copy()
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frameCopy = frame.copy()
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# prevFrame is not none
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if firstFrame is not None:
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diff = cv2.absdiff(firstFrame, frame)
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gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
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blur = cv2.GaussianBlur(gray, (5, 5), 0)
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tres = cv2.threshold(blur, 25, 255, cv2.THRESH_BINARY)[1]
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ret, track_window = CamShift(tres, track_window, (3, 10, 1))
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cv2.rectangle(frameCopy, (track_window[0], track_window[1]), (track_window[0]+track_window[2], track_window[1]+track_window[3]), (0, 255, 0), 2)
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cv2.imshow("Frame", frameCopy)
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cv2.imshow("ALG", tres)
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if cv2.waitKey(10) & 0xFF == ord("q"):
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capture.release()
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cv2.destroyAllWindows()
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break
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cv2.destroyAllWindows()
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12
semester_4/osnove_racunalniskega_vida/Naloga_4/test1.py
Normal file
12
semester_4/osnove_racunalniskega_vida/Naloga_4/test1.py
Normal file
@@ -0,0 +1,12 @@
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import cv2
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import numpy as np
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img = np.zeros((3,3))
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img[1,1] = 2
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img[0,2] = 2
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print(img)
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mom = cv2.moments(img)
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print (mom['m00'])
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print (mom['m01'])
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print (mom['m10'])
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91
semester_4/osnove_racunalniskega_vida/Naloga_4/work.py
Normal file
91
semester_4/osnove_racunalniskega_vida/Naloga_4/work.py
Normal file
@@ -0,0 +1,91 @@
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import cv2
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import numpy as np
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events = [i for i in dir(cv2) if 'EVENT' in i]
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from implement import MeanShift, CamShift
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global xLZ
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global yLZ
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global xLast
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global yLast
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global frame
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xLZ = 0
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yLZ = 0
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frame = None
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def clickOnImage(event, x, y, flags, param):
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global frame, xLZ, yLZ, xLast, yLast
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if event == cv2.EVENT_LBUTTONUP:
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#print("EVENT_LBUTTONUP ({},{})".format(x,y))
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xLZ = 0
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yLZ = 0
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if event == cv2.EVENT_LBUTTONDOWN:
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#print("EVENT_LBUTTONDOWN ({},{})".format(x,y))
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xLZ = x
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yLZ = y
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if event == cv2.EVENT_MOUSEMOVE:
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#print("EVENT_MOUSEMOVE ({},{})".format(x,y))
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xLast = x
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yLast = y
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capture = cv2.VideoCapture(0)
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cv2.namedWindow("Slika")
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cv2.setMouseCallback("Slika", clickOnImage)
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miniFrame = None
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track_window = None
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while(1):
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ret, frame = capture.read()
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if ret == True:
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if xLZ != 0 or yLZ != 0:
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cv2.rectangle(frame,(xLZ,yLZ),(xLast,yLast),(0,255,0),2)
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||||
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cv2.imshow('Slika',frame)
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if cv2.waitKey(100) & 0xFF == ord("q"):
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cv2.destroyAllWindows()
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break
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||||
if cv2.waitKey(100) & 0xFF == ord("r"):
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miniFrame = frame[yLZ:yLast,xLZ:xLast]
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track_window = (xLZ, yLZ, xLast-xLZ, yLast-yLZ)
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cv2.imshow("Mini",miniFrame)
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||||
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if miniFrame is not None:
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||||
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||||
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roi = miniFrame
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||||
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
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mask = cv2.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
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||||
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||||
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roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180])
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||||
cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)
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||||
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||||
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
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||||
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||||
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||||
while(1):
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||||
ret, frame = capture.read()
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||||
frame = cv2.flip(frame,1)
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||||
if ret == True:
|
||||
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
|
||||
dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)
|
||||
dst = cv2.threshold(dst, 2, 255, cv2.THRESH_BINARY)[1]
|
||||
cv2.imshow("dst",dst)
|
||||
# apply meanshift to get the new location
|
||||
#ret, track_window = MeanShift(dst, track_window, term_crit)
|
||||
|
||||
ret, track_window = CamShift(dst, track_window, term_crit)
|
||||
|
||||
# Draw it on image
|
||||
x,y,w,h = track_window
|
||||
img2 = cv2.rectangle(frame, (x,y), (x+w,y+h), 255,2)
|
||||
cv2.imshow('img2',img2)
|
||||
k = cv2.waitKey(30) & 0xff
|
||||
if k == 27:
|
||||
break
|
||||
else:
|
||||
break
|
||||
|
||||
cv2.destroyAllWindows()
|
||||
capture.release()
|
||||
Reference in New Issue
Block a user