85 lines
2.3 KiB
Python
85 lines
2.3 KiB
Python
import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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# Load video using OpenCV
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video = cv2.VideoCapture("Solen.mov")
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# Get video information
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fps = video.get(cv2.CAP_PROP_FPS)
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num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
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# Load face detection model
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face_detector = cv2.CascadeClassifier("Haar_Cascade.xml")
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# Lists to store information for each frame
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frame_matrices = []
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average_rgb = []
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# Loop through frames
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for i in range(num_frames):
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ret, frame = video.read()
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# Check if frame was successfully retrieved
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if not ret:
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break
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# Skip frames to get 15 frames per second
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if i % int(fps/15) != 0:
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continue
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# Convert to grayscale
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gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# Detect faces
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face = face_detector.detectMultiScale(
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gray_frame, scaleFactor=1.1, minNeighbors=5)
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# print(face)
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# Draw a rectangle on each detected face and if face is detected store information
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if len(face) > 0:
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x, y, w, h = face[0]
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face = frame[y:y+h, x:x+w]
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frame = cv2.rectangle(frame, (int(x*2), int(y*1)),
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(x + int(w*0.7), y + int(h*0.2)), (0, 255, 0), 3)
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# Show image to check if the rectangle is well positioned on the forehead
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# cv2.imshow("Faces", frame)
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# cv2.waitKey(0)
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# Split into RGB channels
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b, g, r = cv2.split(face)
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# Calculate average on each channel
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avg_b = np.mean(b) / 255
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avg_g = np.mean(g) / 255
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avg_r = np.mean(r) / 255
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# Add to list
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average_rgb.append([avg_b, avg_g, avg_r])
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# frame_matrices.append(face)
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print(average_rgb)
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# print(frame_matrices)
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# Convert to numpy array
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average_rgb = np.array(average_rgb)
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print(average_rgb)
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np.savetxt("dataRGB", average_rgb)
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# Get number of frames
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num_frames = average_rgb.shape[0]
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# Create an array of frame numbers
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frame_numbers = np.arange(num_frames)
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# Plot the line graph
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plt.plot(frame_numbers, average_rgb[:, 0], 'b', label='Blue')
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plt.plot(frame_numbers, average_rgb[:, 1], 'g', label='Green')
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plt.plot(frame_numbers, average_rgb[:, 2], 'r', label='Red')
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# Add labels and legend
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plt.xlabel('Frame Number')
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plt.ylabel('Normalized Average RGB Value')
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plt.legend(loc='upper right')
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# Show the plot
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plt.show()
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