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Checking in before a major refactor
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143
main.py
143
main.py
@ -5,6 +5,8 @@ from collections.abc import Iterable
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import matplotlib.pyplot as plt
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from pathlib import Path
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OUTPUT_GRAPH = False
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OUTPUT_FRAMES = False
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def brightest_average(pixel_values: np.ndarray):
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brightest_pixels = np.argsort(pixel_values)[-3:]
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@ -14,14 +16,15 @@ def brightest_average(pixel_values: np.ndarray):
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def weighted_average(pixel_values: np.ndarray):
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normalized_values = pixel_values / 255
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adjusted_values = normalized_values
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x_values = np.arange(pixel_values.size)
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return np.average(x_values, weights=pixel_values)
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adjusted_values = normalized_values ** 200
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x_values = np.arange(adjusted_values.size)
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return np.average(x_values, weights=adjusted_values)
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def first_non_zero(pixel_values: np.ndarray):
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return np.nonzero(pixel_values)[0][0]
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def count_non_zero(pixel_values: np.ndarray):
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return np.count_nonzero(pixel_values)
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@ -33,35 +36,119 @@ def compute_x_value(pixel_values: np.ndarray):
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"first_non_zero": first_non_zero,
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"count_non_zero": count_non_zero,
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}
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return algorithms["brightest_avg"](pixel_values)
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# return algorithms["brightest_avg"](pixel_values)
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# return algorithms["count_non_zero"](pixel_values)
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# return algorithms["first_non_zero"](pixel_values)
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# return algorithms["weighted_avg"](pixel_values)
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return algorithms["count_non_zero"](pixel_values)
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fig = plt.figure()
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from matplotlib.animation import FFMpegWriter
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writer = FFMpegWriter(fps=30)
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plt.ylim([0, 200])
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l = None
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def graph_frame(pixel_values: np.ndarray, output_file: str):
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# fig.
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return
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plt.figure()
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plt.plot(pixel_values)
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plt.ylim([0, 200])
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plt.savefig(output_file)
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plt.close()
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# plt.figure()
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global l
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if l is None:
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l, = plt.plot(pixel_values)
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else:
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x = np.arange(len(pixel_values))
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l.set_data(x, pixel_values)
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# writer.grab_frame()
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# plt.savefig(output_file)
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# plt.close()
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return
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def crop_frame(frame):
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mid_y = 720//2 + 15
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mid_x = 1280//2 + 30
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frame = frame[mid_y-50:mid_y+50, mid_x+100:mid_x+300]
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return frame
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def preprocess_frame(frame):
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lowerb = np.array([0, 0, 120])
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upperb = np.array([255, 255, 255])
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red_line = cv2.inRange(frame, lowerb, upperb)
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masked_video = cv2.bitwise_and(frame,frame,mask = red_line)
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gray = cv2.cvtColor(masked_video, cv2.COLOR_BGR2GRAY)
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return gray
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def apply_gaussian_blur(frame):
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frame = cv2.GaussianBlur(frame, (3, 3), 0)
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frame = cv2.GaussianBlur(frame, (3, 3), 0)
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frame = cv2.GaussianBlur(frame, (11, 11), 0)
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frame = cv2.GaussianBlur(frame, (11, 11), 0)
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return frame
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def compute_score_for_frame(x_values: Iterable):
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return np.std(x_values)
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def compute_height_map(video_file):
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video_data = cv2.VideoCapture(video_file)
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frames = []
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while video_data.isOpened():
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ret, frame = video_data.read()
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if not ret:
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break
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frame = crop_frame(frame)
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frame = preprocess_frame(frame)
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# frame = apply_gaussian_blur(frame)
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laser_x_values = []
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for line in frame:
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if line.max() > 0:
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laser_x_val = compute_x_value(line)
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laser_x_values.append(laser_x_val)
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frames.append(laser_x_values)
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return frames
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def graph_height_map(frames):
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fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
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points = []
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for y, line_data in enumerate(frames):
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for x, z in enumerate(line_data):
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points.append(
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(x, y, z)
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)
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x, y, z = zip(*points)
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x, y, z = np.array(x), np.array(y), np.array(z)
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ax.scatter(x, y, z)
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fig.savefig("surface_map.png")
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def main():
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ranking = []
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# i = 0
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for video_file in sorted(glob("sample_data2/*")):
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# if i < 6:
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# i += 1
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# continue
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# height_data = compute_height_map(video_file)
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# graph_height_map(height_data)
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# return
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fig.suptitle(video_file)
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video_data = cv2.VideoCapture(video_file)
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out = cv2.VideoWriter("out.avi", cv2.VideoWriter_fourcc('M','J','P','G'), 30, (400,400))
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mid_y = 720//2 + 15
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mid_x = 1280//2 + 30
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# out = cv2.VideoWriter("out.avi", cv2.VideoWriter_fourcc('M','J','P','G'), 30, (400,400))
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frame_index = 0
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@ -70,36 +157,25 @@ def main():
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ret, frame = video_data.read()
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if not ret:
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break
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frame = frame[mid_y-50:mid_y+50, mid_x+100:mid_x+300]
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lowerb = np.array([0, 0, 120])
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upperb = np.array([255, 255, 255])
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red_line = cv2.inRange(frame, lowerb, upperb)
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frame = crop_frame(frame)
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frame = preprocess_frame(frame)
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# frame = apply_gaussian_blur(frame)
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masked_video = cv2.bitwise_and(frame,frame,mask = red_line)
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# cv2.imwrite("test.png", masked_video)
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# exit()
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# out.write(masked_video)
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gray = cv2.cvtColor(masked_video, cv2.COLOR_BGR2GRAY)
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gray = cv2.GaussianBlur(gray, (3, 3), 0)
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gray = cv2.GaussianBlur(gray, (3, 3), 0)
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gray = cv2.GaussianBlur(gray, (11, 11), 0)
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gray = cv2.GaussianBlur(gray, (11, 11), 0)
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laser_x_values = []
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for line in gray:
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# find the 4 brightest pixels
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for line in frame:
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if line.max() > 0:
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laser_x_val = compute_x_value(line)
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laser_x_values.append(laser_x_val)
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graph_frame(laser_x_values, f"graphs/{Path(video_file).stem}-{frame_index}.png")
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if OUTPUT_GRAPH:
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graph_frame(laser_x_values, f"graphs/{Path(video_file).stem}-{frame_index}.png")
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# gray = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
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# out.write(gray)
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cv2.imwrite(f"frame_data/{Path(video_file).stem}-{frame_index}.png", gray)
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if OUTPUT_FRAMES:
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cv2.imwrite(f"frame_data/{Path(video_file).stem}-{frame_index}.png", frame)
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frame_score = compute_score_for_frame(laser_x_values)
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# print(frame_index, frame_std)
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@ -112,6 +188,7 @@ def main():
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print(np.std(video_std))
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ranking.append((video_file, np.std(video_std)))
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# return
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print('\nSCORES\n')
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