diff --git a/Diagramme/Pipeline.drawio b/Diagramme/Pipeline.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..cdc88514e9190ffad97ff7910fa82b00ea351986
--- /dev/null
+++ b/Diagramme/Pipeline.drawio
@@ -0,0 +1,61 @@
+<mxfile host="65bd71144e" scale="2" border="0">
+ <diagram id="lPL2x5aQqY1zRR2xWvco" name="Seite-1">
+ <mxGraphModel dx="1326" dy="740" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="1169" pageHeight="827" math="0" shadow="0">
+ <root>
+ <mxCell id="0"/>
+ <mxCell id="1" parent="0"/>
+ <mxCell id="2" value="Bildframe" style="rounded=0;whiteSpace=wrap;html=1;" parent="1" vertex="1">
+ <mxGeometry x="70" y="180" width="120" height="60" as="geometry"/>
+ </mxCell>
+ <mxCell id="3" value="Bildframe" style="rounded=0;whiteSpace=wrap;html=1;" parent="1" vertex="1">
+ <mxGeometry x="80" y="190" width="120" height="60" as="geometry"/>
+ </mxCell>
+ <mxCell id="12" style="edgeStyle=none;html=1;entryX=0;entryY=0.5;entryDx=0;entryDy=0;fontColor=#000000;" parent="1" source="4" target="5" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="4" value="<font style="font-size: 14px;">Bildframe<br><b><font style="font-size: 14px;" color="#b3b3b3">(1280, 720, 3)</font></b></font>" style="rounded=0;whiteSpace=wrap;html=1;" parent="1" vertex="1">
+ <mxGeometry x="90" y="200" width="120" height="60" as="geometry"/>
+ </mxCell>
+ <mxCell id="13" style="edgeStyle=none;html=1;entryX=0;entryY=0.5;entryDx=0;entryDy=0;fontColor=#000000;" parent="1" source="5" target="6" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="5" value="<font style="font-size: 14px;" color="#000000">Filter A</font>" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#dae8fc;strokeColor=#6c8ebf;" parent="1" vertex="1">
+ <mxGeometry x="260" y="200" width="120" height="60" as="geometry"/>
+ </mxCell>
+ <mxCell id="14" style="edgeStyle=none;html=1;fontColor=#000000;" parent="1" source="6" target="7" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="6" value="<font style="font-size: 14px;" color="#000000">Filter B</font>" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#d5e8d4;strokeColor=#82b366;" parent="1" vertex="1">
+ <mxGeometry x="410" y="200" width="120" height="60" as="geometry"/>
+ </mxCell>
+ <mxCell id="18" style="edgeStyle=none;html=1;fontColor=#000000;" parent="1" source="7" target="17" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="7" value="<font style="font-size: 14px;" color="#000000">Filter C</font>" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#e1d5e7;strokeColor=#9673a6;" parent="1" vertex="1">
+ <mxGeometry x="560" y="200" width="120" height="60" as="geometry"/>
+ </mxCell>
+ <mxCell id="9" style="edgeStyle=none;html=1;entryX=0.5;entryY=0;entryDx=0;entryDy=0;fontColor=#000000;" parent="1" source="8" target="5" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="10" style="edgeStyle=none;html=1;fontColor=#000000;" parent="1" source="8" target="6" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="11" style="edgeStyle=none;html=1;entryX=0.5;entryY=0;entryDx=0;entryDy=0;fontColor=#000000;" parent="1" source="8" target="7" edge="1">
+ <mxGeometry relative="1" as="geometry"/>
+ </mxCell>
+ <mxCell id="8" value="<font style="font-size: 14px;">Zustand</font>" style="shape=cylinder3;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;size=15;fontColor=#000000;" parent="1" vertex="1">
+ <mxGeometry x="440" y="80" width="60" height="80" as="geometry"/>
+ </mxCell>
+ <mxCell id="15" value="Bildframe" style="rounded=0;whiteSpace=wrap;html=1;" parent="1" vertex="1">
+ <mxGeometry x="730" y="180" width="120" height="60" as="geometry"/>
+ </mxCell>
+ <mxCell id="16" value="Bildframe" style="rounded=0;whiteSpace=wrap;html=1;" parent="1" vertex="1">
+ <mxGeometry x="740" y="190" width="120" height="60" as="geometry"/>
+ </mxCell>
+ <mxCell id="17" value="<font style="font-size: 14px;">Resultat</font>" style="rounded=0;whiteSpace=wrap;html=1;" parent="1" vertex="1">
+ <mxGeometry x="750" y="200" width="120" height="60" as="geometry"/>
+ </mxCell>
+ </root>
+ </mxGraphModel>
+ </diagram>
+</mxfile>
\ No newline at end of file
diff --git a/Diagramme/Pipeline.png b/Diagramme/Pipeline.png
new file mode 100644
index 0000000000000000000000000000000000000000..689527df410d3248378170470b7e1e0f4c37bd1d
Binary files /dev/null and b/Diagramme/Pipeline.png differ
diff --git a/Filter/IMG_23_05_2023_17_26_27.png b/Filter/IMG_23_05_2023_17_26_27.png
deleted file mode 100644
index 451667bb69242f5c119c764d3d6fda3ff8561646..0000000000000000000000000000000000000000
Binary files a/Filter/IMG_23_05_2023_17_26_27.png and /dev/null differ
diff --git a/Filter/IMG_23_05_2023_17_32_16.png b/Filter/IMG_23_05_2023_17_32_16.png
deleted file mode 100644
index 2a71bd5eab336e954f0b998de6543b52b084e651..0000000000000000000000000000000000000000
Binary files a/Filter/IMG_23_05_2023_17_32_16.png and /dev/null differ
diff --git a/Screenshots/Fenster_OpenCV_06_09_2023.png b/Screenshots/Fenster_OpenCV_06_09_2023.png
new file mode 100644
index 0000000000000000000000000000000000000000..42163ace0aa60006930b1d002ff57110d0fa60ee
Binary files /dev/null and b/Screenshots/Fenster_OpenCV_06_09_2023.png differ
diff --git a/Screenshots/Filter_3_06_09_2023.png b/Screenshots/Filter_3_06_09_2023.png
new file mode 100644
index 0000000000000000000000000000000000000000..cb9aef8f8ce5301219864ac8cdbfe29165c61eff
Binary files /dev/null and b/Screenshots/Filter_3_06_09_2023.png differ
diff --git a/Screenshots/IMG_07_09_2023_16_08_04.png b/Screenshots/IMG_07_09_2023_16_08_04.png
new file mode 100644
index 0000000000000000000000000000000000000000..91eca2a0ad887bd84f0f7bd18fe6652afbbe5c8e
Binary files /dev/null and b/Screenshots/IMG_07_09_2023_16_08_04.png differ
diff --git a/Screenshots/IMG_07_09_2023_16_09_08.png b/Screenshots/IMG_07_09_2023_16_09_08.png
new file mode 100644
index 0000000000000000000000000000000000000000..0ae4803f760deca6a70637c61ef6c89da6be2b58
Binary files /dev/null and b/Screenshots/IMG_07_09_2023_16_09_08.png differ
diff --git a/Filter/IMG_23_05_2023_17_16_31.png b/Screenshots/IMG_23_05_2023_17_16_31.png
similarity index 100%
rename from Filter/IMG_23_05_2023_17_16_31.png
rename to Screenshots/IMG_23_05_2023_17_16_31.png
diff --git a/Filter/IMG_28_04_2023_15_27_08.png b/Screenshots/IMG_28_04_2023_15_27_08.png
similarity index 100%
rename from Filter/IMG_28_04_2023_15_27_08.png
rename to Screenshots/IMG_28_04_2023_15_27_08.png
diff --git a/filter_pipeline.py b/detect_waterstream.py
similarity index 69%
rename from filter_pipeline.py
rename to detect_waterstream.py
index c2a9a591d699035a0a67833542c9b2d8c0d41a7f..f6e8af03ecfc736a86cfa82b7cc102dc835bd97d 100644
--- a/filter_pipeline.py
+++ b/detect_waterstream.py
@@ -1,29 +1,36 @@
+##############################################
+# Modul: Computer Vision (SoSe23)
+# Dozent: Prof. Dr-Ing. Gerhardt
+#
+# Erkennung eines Wasserstrahls
+#
+# Autoren: - Joel Steffens
+# - Mohammend Ka
+# - Midras Lappe
+##############################################
import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-from datetime import datetime
-import pandas as pd
import filters
+import sys
+from datetime import datetime
+
+argc = len(sys.argv)
+if argc < 2 or argc > 3:
+ print("Syntax: ./detect_waterstream.py <Video> [Seconds]")
+ exit(1)
-VIDEO = 'Videomaterial/WIN_20230602_14_45_52_Pro.mp4'
-#VIDEO = 'Videomaterial/WIN_20230414_13_41_55_Pro.mp4'
-capture = cv2.VideoCapture(VIDEO)
-capture.set(cv2.CAP_PROP_POS_FRAMES, 30 * 0) # skip 40 seconds
+videoFile = sys.argv[1]
+skipSeconds = 0
+if argc == 3:
+ skipSeconds = int(sys.argv[2])
+
+capture = cv2.VideoCapture(videoFile)
+capture.set(cv2.CAP_PROP_POS_FRAMES, 30 * skipSeconds)
last = None
frame = None
diff = None
window = 'Filter'
-# Einstellungen
-min_threshold = 30
-max_threshold = 110
-img_threshold = 100
-line_threshold = 30
-
-spanne = 2
-
def overlay_imgs(base, top):
topgray = cv2.cvtColor(top, cv2.COLOR_RGB2GRAY)
@@ -36,41 +43,41 @@ def overlay_imgs(base, top):
return cv2.add(img1_bg, img2_fg)
-
def nothing_cb(val):
pass
-# To control the Size of the Disply
+# Neues OpenCV-Fenster erzeugen
cv2.namedWindow(window, cv2.WINDOW_NORMAL)
+# Trackbar für Filter-Parameter anlegen
cv2.createTrackbar('Canny_Min', window, 90, 255, nothing_cb)
cv2.createTrackbar('Canny_Max', window, 150, 255, nothing_cb)
cv2.createTrackbar('Diff_Mix', window, 40, 100, nothing_cb)
cv2.createTrackbar('HSV_Min', window, 50, 180, nothing_cb)
cv2.createTrackbar('HSV_Max', window, 100, 180, nothing_cb)
-
+# Definiton der Filter-Stufen
pipeline = [
('Original', filters.none),
#('GreenChannel', filters.greenfilter),
('Gray scale', filters.grayscale),
- ('Contrast and Blur', filters.medianBlur),
+ ('Contrast and Blur', filters.median_blur),
('Video Diff Multiple', filters.video_absdiff2),
('Green filter abs', filters.green_absfilter),
('Canny edge detection', filters.filter_canny),
('Morph close', filters.filter_close),
- # ('gitter_filter', filters.gitter),
- #('Morph open', filters.filter_open),
+ # ('Gitter Filter', filters.gitter),
+ # ('Morph open', filters.filter_open),
('Einzelne Rauswerfen', filters.sortOut),
('Point extraction', filters.points_extract),
('Polyfit lines', filters.points_overlay)
]
-state = {} # Empty dictionary to store filters state
+state = {} # Leeres Dictionary als Zustandsobjekt
info = {
- 'abs_diff': 2, # 3 images for difference,
- 'dim': (1920, 1080), #
- 'params': {}
+ 'abs_diff': 2, # Standard: 2+1 Bilder
+ 'dim': (1920, 1080), # Dimension des Videos
+ 'params': {} # Leeres Dictionary für Filter-Parameter
}
result = None
@@ -81,7 +88,7 @@ while capture.isOpened():
if ret == True:
frame, _ = filters.resize(None, frame, None)
- # Apply
+ # Trackbar-Parameter auslesen und setzen
info['params']['mix'] = cv2.getTrackbarPos('Diff_Mix', window) / 100.0
info['params']['canny_min'] = cv2.getTrackbarPos('Canny_Min', window)
info['params']['canny_max'] = cv2.getTrackbarPos('Canny_Max', window)
@@ -89,6 +96,7 @@ while capture.isOpened():
info['params']['hsv_max'] = cv2.getTrackbarPos('HSV_Max', window)
result = frame
+ image = result
for i, (name, filter) in enumerate(pipeline):
# Apply each filter
result, overlay = filter(info, result, state)
@@ -107,26 +115,29 @@ while capture.isOpened():
cv2.imshow('Filter', image)
-
+ # Tastatur abfragen
code = cv2.waitKey(33) & 0xFF
+ # Option 1: Speichern (s)
if code == ord('s'):
now = datetime.now()
str = now.strftime("%d_%m_%Y_%H_%M_%S")
- cv2.imwrite(f'Filter/IMG_{str}.png', result)
+ cv2.imwrite(f'Screenshots/IMG_{str}.png', image)
+ # Option 2: Nächster Filter (d)
elif code == ord('d'):
visible_filter_idx += 1
if visible_filter_idx >= len(pipeline):
visible_filter_idx = len(pipeline) - 1
+ # Option 3: Vorheriger Filter (a)
elif code == ord('a'):
visible_filter_idx -= 1
if visible_filter_idx < 0:
visible_filter_idx = 0
+ # Option 4: Programm beenden (q)
elif code & 0xFF == ord('q'):
break
-
else:
break
+# Aufräumen
capture.release()
-
cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/filter_images.py b/filter_images.py
deleted file mode 100644
index 348940186e882418603ed7dc5aabcc6c1b9522fd..0000000000000000000000000000000000000000
--- a/filter_images.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import cv2
-import numpy as np
-from datetime import datetime
-
-VIDEO = 'Videomaterial/WIN_20230414_13_41_55_Pro.mp4'
-capture = cv2.VideoCapture(VIDEO)
-
-last = None
-frame = None
-diff = None
-window = 'Filter'
-
-# Einstellungen
-min_threshold = 30
-max_threshold = 110
-img_threshold = 100
-line_threshold = 30
-
-def overlay_imgs(base, top):
- topgray = cv2.cvtColor(top, cv2.COLOR_RGB2GRAY)
-
- _, mask = cv2.threshold(topgray, 10, 255, cv2.THRESH_BINARY)
- mask_inv = cv2.bitwise_not(mask)
- # Now black-out the area of overlay
- img1_bg = cv2.bitwise_and(base,base,mask = mask_inv)
- # Take only region of from logo image.
- img2_fg = cv2.bitwise_and(top,top,mask = mask)
-
- return cv2.add(img1_bg, img2_fg)
-
-def filter_image(image):
- global min_threshold, max_threshold, line_threshold, result, window
- image = image.copy()
- # Use canny edge detection
- image = cv2.Canny(image, min_threshold, max_threshold)
-
- # kernel = np.ones((5,5),np.uint8)
- kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
- image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)
-
- lines = cv2.HoughLinesP(image, cv2.HOUGH_PROBABILISTIC, np.pi/360, line_threshold, minLineLength=10, maxLineGap=20)
-
- overlay = np.zeros((image.shape[0], image.shape[1], 3), np.uint8)
-
- if lines is not None:
- for line in lines:
- x1,y1,x2,y2 = line[0]
- pts = np.array([[x1, y1], [x2, y2]], np.int32)
- cv2.polylines(overlay, [pts], False, (255, 0, 0), thickness=3)
-
- return overlay
-
-# def img_callback(val):
-# global img_threshold, result
-
-# img_threshold = val
-# if result is not None:
-# filter_image(result)
-
-# def max_callback(val):
-# global max_threshold, result
-
-# max_threshold = val
-# if result is not None:
-# filter_image(result)
-
-def line_callback(val):
- global line_threshold, frame, diff
-
- line_threshold = val
- if diff is not None:
- overlay = filter_image(diff)
-
- result = overlay_imgs(frame, overlay)
- cv2.imshow(window, result)
-
-
-cv2.namedWindow(window, cv2.WINDOW_AUTOSIZE)
-cv2.createTrackbar('Line Threshold: ', window, line_threshold, 100, line_callback)
-
-while capture.isOpened():
- ret, frame = capture.read()
- if ret == True:
- frame = cv2.resize(frame, (1280, 720))
-
- filter = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
-
- #frame = frame[:,:,2] # Blue Channel
- filter = cv2.medianBlur(filter, 3)
- if last is not None:
- diff = cv2.absdiff(filter, last) # Difference
- overlay = filter_image(diff)
-
- result = overlay_imgs(frame, overlay)
- cv2.imshow(window, result)
-
- last = filter
-
- code = cv2.waitKey(33)
- if code & 0xFF == ord('s'):
- now = datetime.now()
- str = now.strftime("%d_%m_%Y_%H_%M_%S")
- cv2.imwrite(f'Filter/IMG_{str}.png', result)
-
- if code & 0xFF == ord('q'):
- break
-
- else:
- break
-
-capture.release()
-
-cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/filter_images_V1.py b/filter_images_V1.py
deleted file mode 100644
index a296968693c6190ff56b51b0e62835ceebb95ef9..0000000000000000000000000000000000000000
--- a/filter_images_V1.py
+++ /dev/null
@@ -1,189 +0,0 @@
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-from datetime import datetime
-
-VIDEO = 'Videomaterial/WIN_20230414_13_41_55_Pro.mp4'
-capture = cv2.VideoCapture(VIDEO)
-
-
-last = None
-frame = None
-diff = None
-window = 'Filter'
-
-# Einstellungen
-min_threshold = 30
-max_threshold = 110
-img_threshold = 100
-line_threshold = 30
-
-def overlay_imgs(base, top):
- topgray = cv2.cvtColor(top, cv2.COLOR_RGB2GRAY)
-
- _, mask = cv2.threshold(topgray, 10, 255, cv2.THRESH_BINARY)
- mask_inv = cv2.bitwise_not(mask)
- # Now black-out the area of overlay
- img1_bg = cv2.bitwise_and(base,base,mask = mask_inv)
- # Take only region of from logo image.
- img2_fg = cv2.bitwise_and(top,top,mask = mask)
-
- return cv2.add(img1_bg, img2_fg)
-
-
-
-
-def plot_points(x, y, px, py):
- fig = plt.figure()
- ax = fig.add_subplot()
- ax.scatter(px, py, c='r')
- ax.plot(x, y)
- ax.set_xlim([0, 1280])
- ax.set_ylim([720, 0])
- plt.show()
-
-def find_points(image):
- indices = np.where(image > 0)
- if (indices[1].size > 0):
- x_sort = np.sort(indices[1])
- p = np.polyfit(indices[1], indices[0], 4)
- x = np.arange(x_sort[0], x_sort[-1], 1)
- y = np.polyval(p, x)
-
- points = np.column_stack((x, y)).astype(np.int32)
- return points
-
- return None
-
-
-def filter_image(image):
- global min_threshold, max_threshold, line_threshold, result, window
- image = image.copy()
- # Use canny edge detection
- image = cv2.Canny(image, min_threshold, max_threshold)
-
- # Closing operation elliptical shaped kernels
- kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5)) # use --> dilation = cv2.dilate(img,kernel,iterations = 1)
- # Closing operation / closing small holes
- image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)
-
- # Minimum filter: Rauschen entfernen
-
- # Closing operation elliptical shaped kernels
- kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3)) # use another kernal shapes
- # perform erosion on the image
- image = cv2.erode(image, kernel)
-
- kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
- #image = cv2.erode(image, kernel)
-
-
- points = find_points(image)
- overlay = np.zeros((image.shape[0], image.shape[1], 3), np.uint8)
- # draw a polygon on the image
- cv2.polylines(overlay, [points], False, (255, 0, 0), thickness=3)
-
- #lines = cv2.HoughLinesP(image, cv2.HOUGH_PROBABILISTIC, np.pi/360, line_threshold, minLineLength=10, maxLineGap=20)
-
- #if lines is not None:
- # for line in lines:
- # x1,y1,x2,y2 = line[0]
- # pts = np.array([[x1, y1], [x2, y2]], np.int32)
- # #cv2.polylines(overlay, [pts], False, (255, 0, 0), thickness=1)
-
- return overlay, image
-
-def sharpenImg(image):
- # median of all the pixels under the kernel area
- blur = cv2.medianBlur(image, 5)
- # adding tow images
- sharp = cv2.addWeighted(image, 1.5, blur, -0.5, 0.0)
- return sharp
-
-
-# def img_callback(val):
-# global img_threshold, result
-
-# img_threshold = val
-# if result is not None:
-# filter_image(result)
-
-# def max_callback(val):
-# global max_threshold, result
-
-# max_threshold = val
-# if result is not None:
-# filter_image(result)
-
-def line_callback(val):
- global line_threshold, frame, diff
-
- line_threshold = val
- if diff is not None:
- overlay = filter_image(diff)
-
- result = overlay_imgs(frame, overlay)
- cv2.imshow(window, result)
-
-def calculate_function(overlay,image):
- indices = np.where(overlay > [0])
- if((indices[0].size>0)):
- p=np.polyfit(indices[0],indices[1], 4)
- x = np.arange(0, overlay.shape[1], 1)
- y=np.polyval(p,x)
- points=np.column_stack((x,y)).astype(np.int32)
- indices = np.where(points < 0)
- points = np.delete(points,indices,axis=0)
- print(points)
- cv2.polylines(image, [points], False, (255, 0, 0), thickness=3)
- # plt.figure()
- # plt.plot(x,y)
- # plt.savefig("test.png")
- # plt.show()
- # time.sleep(20)
- # print (p)
- return overlay
-
-# To control the Size of the Disply
-cv2.namedWindow(window, cv2.WINDOW_AUTOSIZE)
-cv2.createTrackbar('Line Threshold: ', window, line_threshold, 100, line_callback)
-
-result = None
-while capture.isOpened():
- # ret is the stat of the reading
- ret, frame = capture.read()
- if ret == True:
- frame = cv2.resize(frame, (1280, 720))
-
- filter = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
- filter = sharpenImg(filter)
-
- if last is not None:
- diff = cv2.absdiff(filter, last) # Difference
- overlay, image = filter_image(diff)
-
- result = overlay_imgs(frame, overlay)
-
- #function_img=calculate_function(overlay,result)
-
- cv2.imshow(window, image)
- cv2.imshow('Rsult', result)
-
- last = filter
-
- code = cv2.waitKey(33)
- if code & 0xFF == ord('s'):
- now = datetime.now()
- str = now.strftime("%d_%m_%Y_%H_%M_%S")
- cv2.imwrite(f'Filter/IMG_{str}.png', result)
-
- if code & 0xFF == ord('q'):
- break
-
- else:
- break
-
-capture.release()
-
-cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/filter_images_V2.py b/filter_images_V2.py
deleted file mode 100644
index 978004f518147a67f595080146ca45d26df90d0f..0000000000000000000000000000000000000000
--- a/filter_images_V2.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import cv2
-import numpy as np
-from datetime import datetime
-from collections import deque
-
-VIDEO = 'Videomaterial/WIN_20230414_13_41_55_Pro.mp4'
-capture = cv2.VideoCapture(VIDEO)
-
-last = None
-frame = None
-diff = None
-window = 'Filter'
-
-# Einstellungen
-min_threshold = 30
-max_threshold = 110
-img_threshold = 100
-line_threshold = 30
-
-def overlay_imgs(base, top):
- topgray = cv2.cvtColor(top, cv2.COLOR_RGB2GRAY)
-
- _, mask = cv2.threshold(topgray, 10, 255, cv2.THRESH_BINARY)
- mask_inv = cv2.bitwise_not(mask)
- # Now black-out the area of overlay
- img1_bg = cv2.bitwise_and(base,base,mask = mask_inv)
- # Take only region of from logo image.
- img2_fg = cv2.bitwise_and(top,top,mask = mask)
-
- return cv2.add(img1_bg, img2_fg)
-def find_points(image):
- indices = np.where(image > 0)
- if (indices[1].size > 0):
- x_sort = np.sort(indices[1])
- p = np.polyfit(indices[1], indices[0], 4)
- x = np.arange(x_sort[0], x_sort[-1], 1)
- y = np.polyval(p, x)
-
- points = np.column_stack((x, y)).astype(np.int32)
- return points
-
- return None
-def filter_image(image):
- global min_threshold, max_threshold, line_threshold, result, window
- image = image.copy()
- # Use canny edge detection
- image = cv2.Canny(image, min_threshold, max_threshold)
-
- # kernel = np.ones((5,5),np.uint8)
- kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
- image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)
-
- #lines = cv2.HoughLinesP(image, cv2.HOUGH_PROBABILISTIC, np.pi/360, line_threshold, minLineLength=10, maxLineGap=20)
- points = find_points(image)
- overlay = np.zeros((image.shape[0], image.shape[1], 3), np.uint8)
- cv2.polylines(overlay, [points], False, (255, 0, 0), thickness=2)
- #if lines is not None:
- # for line in lines:
- # x1,y1,x2,y2 = line[0]
- # pts = np.array([[x1, y1], [x2, y2]], np.int32)
- # cv2.polylines(overlay, [pts], False, (255, 0, 0), thickness=3)
-
- return overlay, image
-
-# def img_callback(val):
-# global img_threshold, result
-
-# img_threshold = val
-# if result is not None:
-# filter_image(result)
-
-# def max_callback(val):
-# global max_threshold, result
-
-# max_threshold = val
-# if result is not None:
-# filter_image(result)
-
-def line_callback(val):
- global line_threshold, frame, diff
-
- line_threshold = val
- if diff is not None:
- overlay = filter_image(diff)
-
- result = overlay_imgs(frame, overlay)
- cv2.imshow(window, result)
-
-
-cv2.namedWindow(window, cv2.WINDOW_AUTOSIZE)
-cv2.createTrackbar('Line Threshold: ', window, line_threshold, 100, line_callback)
-
-buffer = deque(maxlen=4)
-mix = 0.8
-
-while capture.isOpened():
-
- ret, frame = capture.read()
- if ret == True:
- frame = cv2.resize(frame, (1280, 720))
-
- filter = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
- filter = cv2.medianBlur(filter, 3)
- buffer.append(filter)
- if len(buffer) >= 1:
- diff = np.zeros((filter.shape[0], filter.shape[1], 1), np.uint8)
- for i in range(0, len(buffer) - 1):
- diff_frame = cv2.absdiff(buffer[i], buffer[i+1]) # Difference
- diff = cv2.addWeighted(diff, 1.0, diff_frame, 1.0, 0.0)
- #diff = cv2.add(diff, diff_frame)
-
- overlay, image = filter_image(diff)
- result = overlay_imgs(frame, overlay)
-
- cv2.imshow(window, image)
- cv2.imshow('overlay', overlay)
- cv2.imshow('result', result)
-
- code = cv2.waitKey(33)
- if code & 0xFF == ord('s'):
- now = datetime.now()
- str = now.strftime("%d_%m_%Y_%H_%M_%S")
- cv2.imwrite(f'Filter/IMG_{str}.png', result)
-
- if code & 0xFF == ord('q'):
- break
-
- else:
- break
-
-capture.release()
-
-cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/filter_images_V3.py b/filter_images_V3.py
deleted file mode 100644
index 02f1164cb7f75c2ca52d00c3edd7f23eba6849fa..0000000000000000000000000000000000000000
--- a/filter_images_V3.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-from datetime import datetime
-
-VIDEO = 'Videomaterial/WIN_20230414_13_41_55_Pro.mp4'
-capture = cv2.VideoCapture(VIDEO)
-
-
-last = None
-frame = None
-diff = None
-window = 'Filter'
-
-# Einstellungen
-min_threshold = 30
-max_threshold = 110
-img_threshold = 100
-line_threshold = 30
-
-def overlay_imgs(base, top):
- topgray = cv2.cvtColor(top, cv2.COLOR_RGB2GRAY)
-
- _, mask = cv2.threshold(topgray, 10, 255, cv2.THRESH_BINARY)
- mask_inv = cv2.bitwise_not(mask)
- # Now black-out the area of overlay
- img1_bg = cv2.bitwise_and(base,base,mask = mask_inv)
- # Take only region of from logo image.
- img2_fg = cv2.bitwise_and(top,top,mask = mask)
-
- return cv2.add(img1_bg, img2_fg)
-
-
-
-
-def plot_points(x, y, px, py):
- fig = plt.figure()
- ax = fig.add_subplot()
- ax.scatter(px, py, c='r')
- ax.plot(x, y)
- ax.set_xlim([0, 1280])
- ax.set_ylim([720, 0])
- plt.show()
-
-def find_points(image):
- indices = np.where(image > 0)
- if (indices[1].size > 0):
- x_sort = np.sort(indices[1])
- p = np.polyfit(indices[1], indices[0], 4)
- x = np.arange(x_sort[0], x_sort[-1], 1)
- y = np.polyval(p, x)
-
- points = np.column_stack((x, y)).astype(np.int32)
- return points
-
- return None
-
-
-def filter_image(image):
- global min_threshold, max_threshold, line_threshold, result, window
- image = image.copy()
-
-
- # construct a rectangular kernel from the current size / rect shaped kernel
- kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
-
-
- # Opening operation
- image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
-
-
- # Closing operation / closing small holes
- image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)
-
-
- # dilation
- kernel2 = np.ones((3,3),np.uint8)
- image = cv2.dilate(image,kernel2,iterations = 3)
-
-
- # perform erosion on the image
- image = cv2.erode(image, kernel)
-
- # Use canny edge detection
- image = cv2.Canny(image, min_threshold, max_threshold)
-
- points = find_points(image)
- overlay = np.zeros((image.shape[0], image.shape[1], 3), np.uint8)
- # draw a polygon on the image
- cv2.polylines(overlay, [points], False, (255, 0, 0), thickness=7)
-
-
- return overlay, image
-
-def sharpenImg(image):
- # median of all the pixels under the kernel area
- blur = cv2.medianBlur(image, 5)
- # adding tow images
- sharp = cv2.addWeighted(image, 1.5, blur, -0.5, 0.0)
- return sharp
-
-
-
-def line_callback(val):
- global line_threshold, frame, diff
-
- line_threshold = val
- if diff is not None:
- overlay = filter_image(diff)
-
- result = overlay_imgs(frame, overlay)
- cv2.imshow(window, result)
-
-def calculate_function(overlay,image):
- indices = np.where(overlay > [0])
- if((indices[0].size>0)):
- p=np.polyfit(indices[0],indices[1], 4)
- x = np.arange(0, overlay.shape[1], 1)
- y=np.polyval(p,x)
- points=np.column_stack((x,y)).astype(np.int32)
- indices = np.where(points < 0)
- points = np.delete(points,indices,axis=0)
- print(points)
- cv2.polylines(image, [points], False, (255, 0, 0), thickness=3)
- # plt.figure()
- # plt.plot(x,y)
- # plt.savefig("test.png")
- # plt.show()
- # time.sleep(20)
- # print (p)
- return overlay
-
-# To control the Size of the Disply
-cv2.namedWindow(window, cv2.WINDOW_AUTOSIZE)
-cv2.createTrackbar('Line Threshold: ', window, line_threshold, 100, line_callback)
-
-result = None
-while capture.isOpened():
- # ret is the stat of the reading
- ret, frame = capture.read()
- if ret == True:
- frame = cv2.resize(frame, (1280, 720))
-
- filter = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
- filter = sharpenImg(filter)
-
- if last is not None:
- diff = cv2.absdiff(filter, last) # Difference
- overlay, image = filter_image(diff)
-
- result = overlay_imgs(frame, overlay)
-
- #function_img=calculate_function(overlay,result)
- cv2.imshow('image', image)
- cv2.imshow(window, result)
-
- last = filter
-
- code = cv2.waitKey(33)
- if code & 0xFF == ord('s'):
- now = datetime.now()
- str = now.strftime("%d_%m_%Y_%H_%M_%S")
- cv2.imwrite(f'Filter/IMG_{str}.png', result)
-
- if code & 0xFF == ord('q'):
- break
-
- else:
- break
-
-capture.release()
-
-cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/filter_images_V4.py b/filter_images_V4.py
deleted file mode 100644
index f6974310a66788b6fd17d2081b9e610cded6a312..0000000000000000000000000000000000000000
--- a/filter_images_V4.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-from datetime import datetime
-import pandas as pd
-
-VIDEO = 'Videomaterial/WIN_20230414_13_41_55_Pro.mp4'
-capture = cv2.VideoCapture(VIDEO)
-
-
-last = None
-frame = None
-diff = None
-window = 'Filter'
-
-# Einstellungen
-min_threshold = 30
-max_threshold = 110
-img_threshold = 100
-line_threshold = 30
-
-spanne = 2
-
-def overlay_imgs(base, top):
- topgray = cv2.cvtColor(top, cv2.COLOR_RGB2GRAY)
-
- _, mask = cv2.threshold(topgray, 10, 255, cv2.THRESH_BINARY)
- mask_inv = cv2.bitwise_not(mask)
- # Now black-out the area of overlay
- img1_bg = cv2.bitwise_and(base,base,mask = mask_inv)
- # Take only region of from logo image.
- img2_fg = cv2.bitwise_and(top,top,mask = mask)
-
- return cv2.add(img1_bg, img2_fg)
-
-
-def plot_points(x, y, px, py):
- fig = plt.figure()
- ax = fig.add_subplot()
- ax.scatter(px, py, c='r')
- ax.plot(x, y)
- ax.set_xlim([0, 1280])
- ax.set_ylim([720, 0])
- plt.show()
-
-def find_points(image):
- indices = np.where(image > 0)
- if (indices[1].size > 0):
- x_so = indices[1]
- y_so = indices[0]
-
- list_xy = np.column_stack((x_so, y_so)).astype(np.int32)
- # list_xy = np.sort(list_xy, axis=0)
- # print( list_xy)
-
- df = pd.DataFrame(list_xy,columns=['x','y'])
-
- df = df.sort_values(by=['x'], ascending=True)
-
- n_list = []
- for el in df.x.unique():
- med = (df.y.where(df.x >= el-spanne).where(el+spanne >= df.x)).median()
- n_list.append([el,med])
-
-
- n_list = np.array(n_list)
-
-
- p = np.polyfit(n_list[:,0], n_list[:,1], 6)
- x = np.arange(n_list[:,0][0], n_list[:,0][-1], 1)
- y = np.polyval(p,x)
- points = np.column_stack((x, y)).astype(np.int32)
-
- return points
-
- return None
-
-
-def filter_image(image):
- global min_threshold, max_threshold, line_threshold, result, window
- image = image.copy()
-
-
- # construct a rectangular kernel from the current size / rect shaped kernel
- kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1,1))
-
- # Opening operation
- image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
-
- # perform erosion on the image
- image = cv2.erode(image, (3,3))
-
- # Closing operation / closing small holes
- # image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, (1,1))
- image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, (5,5))
- # # image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, (4,4))
-
-
- # dilation
- kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2,2))
- image = cv2.dilate(image,kernel2,iterations = 1)
-
-
-
-
- # Use canny edge detection
- image = cv2.Canny(image, min_threshold, max_threshold)
-
- points = find_points(image)
- overlay = np.zeros((image.shape[0], image.shape[1], 3), np.uint8)
- # draw a polygon on the image
- cv2.polylines(overlay, [points], False, (255, 0, 0), thickness=6)
-
- return overlay, image
-
-def sharpenImg(image):
- # median of all the pixels under the kernel area
- blur = cv2.medianBlur(image, 7)
- # adding tow images
- sharp = cv2.addWeighted(image, 1.5, blur, -0.5, 0.0)
- return sharp
-
-
-
-def line_callback(val):
- global line_threshold, frame, diff
-
- line_threshold = val
- if diff is not None:
- overlay = filter_image(diff)
-
- result = overlay_imgs(frame, overlay)
- cv2.imshow(window, result)
-
-
-# To control the Size of the Disply
-cv2.namedWindow(window, cv2.WINDOW_AUTOSIZE)
-cv2.createTrackbar('Line Threshold: ', window, line_threshold, 100, line_callback)
-
-result = None
-while capture.isOpened():
- # ret is the stat of the reading
- ret, frame = capture.read()
- if ret == True:
- frame = cv2.resize(frame, (1280, 720))
-
- # filter = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
- filter = sharpenImg(frame)
-
- if last is not None:
- diff = cv2.absdiff(filter, last) # Difference
- cv2.imshow("diff", diff)
- overlay, image = filter_image(diff)
-
- result = overlay_imgs(frame, overlay)
-
-
- cv2.imshow("image", image)
- cv2.imshow("result", result)
- # time.sleep(0.1)
- last = filter
-
- code = cv2.waitKey(10)
- if code & 0xFF == ord('s'):
- now = datetime.now()
- str = now.strftime("%d_%m_%Y_%H_%M_%S")
- cv2.imwrite(f'Filter/IMG_{str}.png', result)
-
- if code & 0xFF == ord('q'):
- break
-
- else:
- break
-
-capture.release()
-
-cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/filter_images_V5.py b/filter_images_V5.py
deleted file mode 100644
index b8f850f811a24d7a5787c7b33f65a47aa7e85d7c..0000000000000000000000000000000000000000
--- a/filter_images_V5.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-from datetime import datetime
-import pandas as pd
-
-VIDEO = 'Videomaterial/WIN_20230602_14_42_19_Pro.mp4'
-capture = cv2.VideoCapture(VIDEO)
-
-
-last = None
-frame = None
-diff = None
-window = 'Filter'
-
-# Einstellungen
-min_threshold = 30
-max_threshold = 110
-img_threshold = 100
-line_threshold = 30
-
-spanne = 2
-
-def overlay_imgs(base, top):
- topgray = cv2.cvtColor(top, cv2.COLOR_RGB2GRAY)
-
- _, mask = cv2.threshold(topgray, 10, 255, cv2.THRESH_BINARY)
- mask_inv = cv2.bitwise_not(mask)
- # Now black-out the area of overlay
- img1_bg = cv2.bitwise_and(base,base,mask = mask_inv)
- # Take only region of from logo image.
- img2_fg = cv2.bitwise_and(top,top,mask = mask)
-
- return cv2.add(img1_bg, img2_fg)
-
-
-def plot_points(x, y, px, py):
- fig = plt.figure()
- ax = fig.add_subplot()
- ax.scatter(px, py, c='r')
- ax.plot(x, y)
- ax.set_xlim([0, 1280])
- ax.set_ylim([720, 0])
- plt.show()
-
-def find_points(image):
- indices = np.where(image > 0)
- if (indices[1].size > 0):
- x_so = indices[1]
- y_so = indices[0]
-
- list_xy = np.column_stack((x_so, y_so)).astype(np.int32)
- # list_xy = np.sort(list_xy, axis=0)
- # print( list_xy)
-
- df = pd.DataFrame(list_xy,columns=['x','y'])
-
- df = df.sort_values(by=['x'], ascending=True)
-
- n_list = []
-
- df_un = df.x.unique()
- for el in df_un[::2]:
-
- med = (df.y.where(df.x >= el-spanne).where(el+spanne >= df.x)).median()
- n_list.append([el,med])
-
-
- n_list = np.array(n_list)
-
-
- p = np.polyfit(n_list[:,0], n_list[:,1], 6)
- x = np.arange(n_list[:,0][0], n_list[:,0][-1], 1)
- y = np.polyval(p,x)
- points = np.column_stack((x, y)).astype(np.int32)
-
- return points
-
- return None
-
-
-def filter_image(image):
- global min_threshold, max_threshold, line_threshold, result, window
- image = image.copy()
-
-
- # construct a rectangular kernel from the current size / rect shaped kernel
- kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1,1))
-
- # Opening operation
- image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
-
- # perform erosion on the image
- image = cv2.erode(image, (3,3))
-
- # Closing operation / closing small holes
- # image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, (1,1))
- image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, (5,5))
- # # image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, (4,4))
-
-
- # dilation
- kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2,2))
- image = cv2.dilate(image,kernel2,iterations = 1)
-
-
-
-
- # Use canny edge detection
- image = cv2.Canny(image, min_threshold, max_threshold)
-
- points = find_points(image)
- overlay = np.zeros((image.shape[0], image.shape[1], 3), np.uint8)
- # draw a polygon on the image
- cv2.polylines(overlay, [points], False, (255, 0, 0), thickness=6)
-
- return overlay, image
-
-def sharpenImg(image):
- # median of all the pixels under the kernel area
- blur = cv2.medianBlur(image, 7)
- # adding tow images
- sharp = cv2.addWeighted(image, 1.5, blur, -0.5, 0.0)
- return sharp
-
-
-
-def line_callback(val):
- global line_threshold, frame, diff
-
- line_threshold = val
- if diff is not None:
- overlay = filter_image(diff)
-
- result = overlay_imgs(frame, overlay)
- cv2.imshow(window, result)
-
-
-# To control the Size of the Disply
-cv2.namedWindow(window, cv2.WINDOW_AUTOSIZE)
-cv2.createTrackbar('Line Threshold: ', window, line_threshold, 100, line_callback)
-
-result = None
-while capture.isOpened():
- # ret is the stat of the reading
- ret, frame = capture.read()
- if ret == True:
- frame = cv2.resize(frame, (1280, 720))
-
- # filter = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
- filter = sharpenImg(frame)
-
- if last is not None:
- diff = cv2.absdiff(filter, last) # Difference
- cv2.imshow("diff", diff)
- overlay, image = filter_image(diff)
-
- result = overlay_imgs(frame, overlay)
-
-
- cv2.imshow("image", image)
- cv2.imshow("result", result)
- # time.sleep(0.1)
- last = filter
-
- code = cv2.waitKey(10)
- if code & 0xFF == ord('s'):
- now = datetime.now()
- str = now.strftime("%d_%m_%Y_%H_%M_%S")
- cv2.imwrite(f'Filter/IMG_{str}.png', result)
-
- if code & 0xFF == ord('q'):
- break
-
- else:
- break
-
-capture.release()
-
-cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/filters.py b/filters.py
index fdd1487b06959f85b20582e05cbd69641e762bf4..80b747193bee1d38ada18ec145af0c7238134291 100755
--- a/filters.py
+++ b/filters.py
@@ -15,6 +15,7 @@ def none(info, image, state):
bufferOrginal.append(image)
return image, False
+
def plot_points(x, y, px, py):
# currentfig
# if currentfig is not None:
@@ -185,7 +186,7 @@ def grayscale(info, image, state):
res = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
return res, False
-def medianBlur(info, image, state):
+def median_blur(info, image, state):
# median of all the pixels under the kernel area
blur = cv.medianBlur(image, 7)
# adding tow images
@@ -302,8 +303,8 @@ def polyfit(n_list,n=4):
return None
-min_threshold = 30
-max_threshold = 110
+#min_threshold = 30
+#max_threshold = 110
def filter_canny(info, image, state):
min_threshold = info['params']['canny_min']
@@ -397,7 +398,7 @@ def video_absdiff2(info, image, state):
if len(buffer) >= 2:
diff = np.zeros((image.shape[0], image.shape[1], 1), np.uint8)
for i in range(0, len(buffer) - 1):
- diff_frame = cv.absdiff(buffer[i], buffer[i+1]) # Difference
+ diff_frame = cv.absdiff(buffer[i], buffer[i+1])
diff = cv.addWeighted(diff, 1.0, diff_frame, 1.0 - mix, 0.0)
return diff, False