unchanged from main
This commit is contained in:
Loic Delattre
parent
f95730b96d
commit
0bdea59850
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@ -1,71 +1,24 @@
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import cv2
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from PIL import Image
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import csv
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import os
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import os.path
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<<<<<<< Updated upstream
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#create a directory to store the RGB datas ouptut
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path_to_script = os.path.dirname(os.path.abspath(__file__))
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if os.path.exists(path_to_script + r"\RGB database") == False:
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os.mkdir(path_to_script + r"\RGB database")
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=======
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############################
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# Task: Initating local files
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#
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# Input:
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# - None
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#
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# Output:
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# - Empty directories to store the RGB data, number of frames stored
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#
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# author: Delattre Loic, Dey Maryne (loic.delattre@ecam.fr, maryne.dey@ecam.fr)
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# date: 06/02/2023
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########################
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def rep_files_init():
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#create a directory to store the RGB datas ouptut
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path_to_script = os.path.dirname(os.path.abspath(__file__))
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if os.path.exists(path_to_script + r"\RGB_database") == False:
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os.mkdir(path_to_script + r"\RGB_database")
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if os.path.exists(path_to_script + r"\RGB database") == False:
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os.mkdir(path_to_script + r"\RGB database")
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onlyfiles = next(os.walk(path_to_script + r"\frames"))[2] #get the amount of frames previously extracted
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frames_num = len(onlyfiles)-1
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>>>>>>> Stashed changes
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onlyfiles = next(os.walk(path_to_script + r"\frames"))[2] #get the amount of frames previously extracted
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frames_num = len(onlyfiles)-1
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return frames_num
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face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
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eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
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image = "frames/frame1.jpg"
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read_img = cv2.imread(image)
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#cv2.imshow('img',imRGB)
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#cv2.waitKey(0)
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############################
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# Task: Extracting the RGB values from every pixel in a given frame
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#
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# Input:
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# - A frame of the face video, the frame number
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#
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# Output:
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# - Three lists containing all of the RGB values of the specific frame
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#
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# author: Delattre Loic, Dey Maryne (loic.delattre@ecam.fr, maryne.dey@ecam.fr)
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# date: 06/02/2023
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########################
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def RGB_dataframe(img, itr):
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<<<<<<< Updated upstream
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=======
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img = cv2.imread(image)
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face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
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eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
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>>>>>>> Stashed changes
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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faces = face_cascade.detectMultiScale(gray, 1.3, 5)
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@ -96,7 +49,7 @@ def RGB_dataframe(img, itr):
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#extra values in x and y in the parameters are adjustements made after manual testing
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cv2.rectangle(roi_color, (fx,fy-150),(fx+100,fy-20),(0,127,255),2)
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'''imS = cv2.resize(img, (960, 540))
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imS = cv2.resize(img, (960, 540))
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#cv2.imshow('img',imS)
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#cv2.waitKey(0)
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x1 = x + fx
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@ -108,35 +61,19 @@ def RGB_dataframe(img, itr):
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#cv2.imshow('img',imRGB)
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#cv2.waitKey(0)
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R = []
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G = []
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B = []
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size_list = list(imRGB.shape[:2])
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pixel_values = []
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for i in range(0, size_list[0]):
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for j in range(0, size_list[1]):
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k = imRGB[i,j]
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<<<<<<< Updated upstream
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pixel_values.append(k)
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fields = ['R', 'G', 'B']
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with open('RGB database/RGB' + str(itr) + '_data.csv', 'w', newline="") as f:
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=======
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R.append(k[0])
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G.append(k[1])
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B.append(k[2])'''
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B.append(k[2])
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return R, G, B
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############################
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# Task: Exporting to a csv file the RGB traces averages of all the frame of a given video
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#
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# Input:
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# - a folder with all the frames of one video
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#
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# Output:
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# - One CSV with all RGB average dats
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#
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# author: Delattre Loic, Dey Maryne (loic.delattre@ecam.fr, maryne.dey@ecam.fr)
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# date: 06/02/2023
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########################
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def average_and_export():
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i = 0
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count = 0.1
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@ -145,9 +82,10 @@ def average_and_export():
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while i < frames_num:
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i += 1
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image = "frames/frame" + str(i) + ".jpg"
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read_img = cv2.imread(image)
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#Bug here inside of func with min funcs of empty data
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try:
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out_colors = RGB_dataframe(image, i)
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out_colors = RGB_dataframe(read_img, i)
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except:
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out_colors = out_colors
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Ravg = sum(out_colors[0])/len(out_colors[0])
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@ -160,20 +98,9 @@ def average_and_export():
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with open('RGB database/RGB_data.csv', 'w', newline="") as f:
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fields = ['R', 'G', 'B']
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>>>>>>> Stashed changes
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write = csv.writer(f)
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write.writerow(fields)
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write.writerows(pixel_values)
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return
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<<<<<<< Updated upstream
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i = 0
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count = 0.1
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while i < frames_num:
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i += 1
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RGB_dataframe(read_img, i)
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if i/frames_num >= count:
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print(str(round(count*100))+ "% of the database exported to csv")
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count += 0.1
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=======
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>>>>>>> Stashed changes
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average_and_export()
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