unchanged from main

RGB_channels.py

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