diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..54e4222 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +video/* +!video/counting_video.mp4 +__pycache__ diff --git a/create_detector_submit_images.py b/create_detector_submit_images.py index 88edfb3..ffcaf2e 100644 --- a/create_detector_submit_images.py +++ b/create_detector_submit_images.py @@ -1,24 +1,48 @@ -from groundlight import Groundlight, Detector, ImageQuery +from groundlight import ExperimentalApi, Detector, ImageQuery from fetch_frames import FRAMES_DIR from glob import glob import cv2 +import argparse SAMPLE_INTERVAL = 10 # sample every 10th frame -gl = Groundlight() -detector: Detector = gl.get_or_create_detector( - name="employees_with_groundlight_tshirt", - query="Label each employee with Groundlight T-shirt in the image", -) +def create_detector_submit_images(detector_name, detector_query, class_name): + gl = ExperimentalApi() -frame_files = sorted(glob(f"{FRAMES_DIR}/*.jpg")) + # TODO: update to use get_or_create_detector once mode can be specified + try: + detector: Detector = gl.get_detector_by_name(detector_name) + except: + detector: Detector = gl.create_counting_detector( + name=detector_name, + query=detector_query, + class_name=class_name, + ) -# submit every SAMPLE_INTERVAL frame to the detector in the first half of the video -for frame_file in frame_files[:len(frame_files)//2:SAMPLE_INTERVAL]: - # load frame - frame = cv2.imread(frame_file) - # resize frame to 640x480 for faster processing - frame = cv2.resize(frame, (640, 480)) - iq: ImageQuery = gl.submit_image_query(detector=detector, image=frame) - print("count: ", iq.result.count) - print("confidence: ", iq.result.confidence) \ No newline at end of file + frame_files = sorted(glob(f"{FRAMES_DIR}/*.jpg")) + + # submit every SAMPLE_INTERVAL frame to the detector in the first half of the video + for frame_file in frame_files[:len(frame_files)//2:SAMPLE_INTERVAL]: + # load frame + frame = cv2.imread(frame_file) + # resize frame to 640x480 for faster processing + frame = cv2.resize(frame, (640, 480)) + iq: ImageQuery = gl.submit_image_query(detector=detector, image=frame) + print("count: ", iq.result.count) + print("confidence: ", iq.result.confidence) + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Create detector and submit images for analysis") + + parser.add_argument("--name", "-n", + default="employees_with_groundlight_tshirt", + help="Name of the detector (default: employees_with_groundlight_tshirt)") + parser.add_argument("--query", "-q", + default="Label each employee with Groundlight T-shirt in the image", + help="Query for the detector (default: Label each employee with Groundlight T-shirt in the image)") + parser.add_argument("--class-name", "-c", + default="employee", + help="Name of the class to count (default: employee)") + + args = parser.parse_args() + create_detector_submit_images(args.name, args.query, args.class_name) \ No newline at end of file diff --git a/enter_exit_counts.py b/enter_exit_counts.py index 8669198..94b04fa 100644 --- a/enter_exit_counts.py +++ b/enter_exit_counts.py @@ -1,55 +1,76 @@ import cv2 +import argparse + from tracker import IOUTracker from fetch_frames import VIDEO_PATH, FRAMES_DIR from glob import glob from groundlight import Groundlight, Detector -# get the detector -gl = Groundlight() -detector: Detector = gl.get_or_create_detector( - name="employees_with_groundlight_tshirt", - query="Label each employee with Groundlight T-shirt in the image", -) -# load frames and submit to detector to get rois -frame_files = sorted(glob(f"{FRAMES_DIR}/*.jpg"))[::3] # every 3rd frame to reduce load -frames_rois = [] -for frame_file in frame_files: - frame = cv2.imread(frame_file) - # Resize frame to 640x480 for faster processing - frame = cv2.resize(frame, (640, 480)) - # Submit image to detector - iq = gl.submit_image_query(detector=detector, image=frame) - frames_rois.append(iq.rois) +def process_frames(detector_name, output_video): + # get the detector + gl = Groundlight() + detector: Detector = gl.get_detector_by_name( + name=detector_name, + ) + # load frames and submit to detector to get rois + frame_files = sorted(glob(f"{FRAMES_DIR}/*.jpg"))[::3] # every 3rd frame to reduce load + frames_rois = [] + for frame_file in frame_files: + frame = cv2.imread(frame_file) + # Resize frame to 640x480 for faster processing + frame = cv2.resize(frame, (640, 480)) + # Submit image to detector + iq = gl.ask_ml(detector=detector, image=frame) + frames_rois.append(iq.rois) + + # initialize the tracker + tracker = IOUTracker() + # specify a virtual line in the video in normalized coordinates + line_start = (0.54, 0.99) + line_end = (0.35, 0.70) -# initialize the tracker -tracker = IOUTracker() -# specify a virtual line in the video in normalized coordinates -line_start = (0.54, 0.99) -line_end = (0.35, 0.70) + # write a video with the detections + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + video_out = cv2.VideoWriter(f"{VIDEO_PATH}/{output_video}", fourcc, 10, (640, 480)) + for frame_file, frame_rois in zip(frame_files, frames_rois): + detections = [(roi.geometry.left, roi.geometry.top, roi.geometry.right, roi.geometry.bottom) for roi in frame_rois] + tracker.update(detections) + tracker.count_crossing(line_start, line_end) + + frame = cv2.imread(frame_file) + frame = cv2.resize(frame, (640, 480)) + h, w = frame.shape[:2] + for roi in frame_rois: + left, top, right, bottom = roi.geometry.left, roi.geometry.top, roi.geometry.right, roi.geometry.bottom + left = int(left * w) + right = int(right * w) + top = int(top * h) + bottom = int(bottom * h) + cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2) + cv2.putText(frame, f"{roi.score:.2f}", (left, top+15), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 128), 2) + # draw virtual line + cv2.line(frame, (int(line_start[0]*w), int(line_start[1]*h)), (int(line_end[0]*w), int(line_end[1]*h)), (0, 0, 255), 2) + # draw counts + cv2.putText(frame, f"Total: {len(frame_rois)}", (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 128), 2) + cv2.putText(frame, f"Entered: {tracker.enter_count}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 128), 2) + cv2.putText(frame, f"Exited: {tracker.exit_count}", (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) + video_out.write(frame) + video_out.release() -# write a video with the detections -fourcc = cv2.VideoWriter_fourcc(*'mp4v') -video_out = cv2.VideoWriter(f"{VIDEO_PATH}/output.mp4", fourcc, 10, (640, 480)) -for frame_file, frame_rois in zip(frame_files, frames_rois): - detections = [(roi.geometry.left, roi.geometry.top, roi.geometry.right, roi.geometry.bottom) for roi in frame_rois] - tracker.update(detections) - tracker.count_crossing(line_start, line_end) +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Process frames with Groundlight detector and create output video") + parser.add_argument( + "--name", "-n", + type=str, + default="employees_with_groundlight_tshirt", + help="Name of the Groundlight detector to use (default: employees_with_groundlight_tshirt)", + ) + parser.add_argument( + "--output-video", "-o", + type=str, + default="output.mp4", + help="Output video name (default: output.mp4)", + ) - frame = cv2.imread(frame_file) - frame = cv2.resize(frame, (640, 480)) - h, w = frame.shape[:2] - for roi in frame_rois: - left, top, right, bottom = roi.geometry.left, roi.geometry.top, roi.geometry.right, roi.geometry.bottom - left = int(left * w) - right = int(right * w) - top = int(top * h) - bottom = int(bottom * h) - cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2) - cv2.putText(frame, f"{roi.score:.2f}", (left, top+15), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 128), 2) - # draw virtual line - cv2.line(frame, (int(line_start[0]*w), int(line_start[1]*h)), (int(line_end[0]*w), int(line_end[1]*h)), (0, 0, 255), 2) - # draw counts - cv2.putText(frame, f"Entered: {tracker.enter_count}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 128), 2) - cv2.putText(frame, f"Exited: {tracker.exit_count}", (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) - video_out.write(frame) -video_out.release() + args = parser.parse_args() + process_frames(args.name, args.output_video) \ No newline at end of file diff --git a/fetch_frames.py b/fetch_frames.py index 552f8c0..6346792 100644 --- a/fetch_frames.py +++ b/fetch_frames.py @@ -1,18 +1,29 @@ import os import cv2 +import argparse VIDEO_PATH = "./video/" FRAMES_DIR = f"{VIDEO_PATH}/frames" -# create directory to save frames -os.makedirs(FRAMES_DIR, exist_ok=True) -# open mp4 file and read frames -cap = cv2.VideoCapture(f"{VIDEO_PATH}/counting_video.mp4") +def fetch_frames(video_name): + # create directory to save frames + os.makedirs(FRAMES_DIR, exist_ok=True) + # open mp4 file and read frames + cap = cv2.VideoCapture(f"{VIDEO_PATH}/{video_name}") -frame_num = 0 -while cap.isOpened(): - ret, frame = cap.read() - if not ret: - break - cv2.imwrite(f"{FRAMES_DIR}/{frame_num:04d}.jpg", frame) - frame_num += 1 \ No newline at end of file + frame_num = 0 + while cap.isOpened(): + ret, frame = cap.read() + if not ret: + break + cv2.imwrite(f"{FRAMES_DIR}/{frame_num:04d}.jpg", frame) + frame_num += 1 + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Extract frames from a video file") + parser.add_argument("--video", "-v", + default="counting_video.mp4", + help="Name of the video file to get frames from (default: our sample video, counting_video.mp4)") + + args = parser.parse_args() + fetch_frames(args.video) diff --git a/poetry.lock b/poetry.lock index b2d7470..28a4923 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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