diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..8c3b2e4 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "visualhaar"] + path = visualhaar + url = git@git.rubenvandeven.com:r/visualhaar.git diff --git a/face_recognition/comparison.py b/face_recognition/comparison.py index 57eb499..e1039a1 100644 --- a/face_recognition/comparison.py +++ b/face_recognition/comparison.py @@ -5,13 +5,17 @@ import logging import argparse import numpy as np import time +from PIL import ImageFont, ImageDraw, Image +import os draw_colors = { - 'dnn': (255,0,0), + 'hog': (255,0,0), 'haar': (0,255,0), - 'hog': (0,0,255), + 'dnn': (0,0,255), } +font = ImageFont.truetype("/home/ruben/Documents/Projecten/2018/PATH/presentation/lib/font/source-sans-pro/source-sans-pro-regular.ttf", 30) + class Result(): def __init__(self, algorithm, image, confidence_threshold = 0.5): self.algorithm = algorithm @@ -28,13 +32,54 @@ class Result(): 'confidence': confidence }) return self - + def draw_detections(self): + color = draw_colors[self.algorithm] + cv2_im_rgb = cv2.cvtColor(self.visualisation,cv2.COLOR_BGR2RGB) + # Pass the image to PIL + pil_im = Image.fromarray(cv2_im_rgb) + draw = ImageDraw.Draw(pil_im, 'RGBA') + + for detection in self.detections: + self.draw_detection(draw, detection, color) + + self.visualisation = cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR) + + def draw_detection(self, draw: ImageDraw, detection: dict, color: tuple): + width = 2 + + if detection['confidence'] > self.confidence_threshold: + # draw the bounding box of the face along with the associated + # probability + text = "{:.2f}%".format(detection['confidence'] * 100) + y = detection['startY'] - 40 if detection['startY'] - 40 > 10 else detection['startY'] + 10 + + draw.text((detection['startX'], y), text, font=font, fill=color) + # cv2.putText(self.visualisation, text, (detection['startX'], y), + # cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2, lineType = cv2.LINE_AA) + + alpha = 1 + else: + # At least 10% opacity + alpha = max(.3, detection['confidence']) + + color = list(color) + color.append(int(alpha*255)) + color = tuple(color) + + draw.rectangle((detection['startX'], detection['startY'], detection['endX'], detection['endY']), outline=color, width=width) + # cv2.rectangle(rect_img, (0, 0), + # (sub_img.shape[1]-int(width/2), sub_img.shape[0]-int(width/2)), + # color, width) + + + + def draw_detections_cv2(self): color = draw_colors[self.algorithm] for detection in self.detections: self.draw_detection(detection, color) - def draw_detection(self, detection, color=(0,0,255)): + def draw_detection_cv2(self, detection, color=(0,0,255)): # First we crop the sub-rect from the image sub_img = self.visualisation[detection['startY']:detection['endY'], detection['startX']:detection['endX']] @@ -88,11 +133,28 @@ class Result(): -def record(device_id, q1,q2, q3, q4): +def record(device_id, q1,q2, q3, q4, resolution, rotate): capture = cv2.VideoCapture(device_id) + + is_rotated_90 = rotate in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE] + + capture.set(cv2.CAP_PROP_FRAME_WIDTH, resolution[1] if is_rotated_90 else resolution[0]) + capture.set(cv2.CAP_PROP_FRAME_HEIGHT, resolution[0] if is_rotated_90 else resolution[1]) + while True: ret, image = capture.read() - logging.debug('r') + if image is None: + logging.critical("Error with camera?") + exit() + + + if rotate is not None: + image = cv2.rotate(image, rotate) + + # print(image.shape[:2], image.shape[1::-1]) + if image.shape[1::-1] != resolution: + logging.warning(f"Camera resultion seems wrong: {image.shape[:2]} instead of {resolution}") + try: q1.put_nowait(image) except Full as e: @@ -155,6 +217,10 @@ def process1_hog(in_q, out_q): from skimage import data, exposure import matplotlib.pyplot as plt import dlib + import matplotlib.pyplot as plt + + # Get the color map by name: + cm = plt.get_cmap('plasma') face_detector = dlib.get_frontal_face_detector() @@ -187,9 +253,17 @@ def process1_hog(in_q, out_q): # print(dets, scores, idxs) hog_image_rescaled = (hog_image_rescaled.astype('float32') * 255).astype('uint8') - hog_image_rescaled = cv2.cvtColor(hog_image_rescaled, cv2.COLOR_GRAY2BGR) + # hog_image_rescaled = cv2.cvtColor(hog_image_rescaled, cv2.COLOR_GRAY2BGR) + # blue background: + # hog_image_rescaled[:,:,0] = 200 - result = Result('hog', hog_image_rescaled, 0) + + # Apply the colormap like a function to any array: + colored_image = (cm(hog_image_rescaled) * 255).astype('uint8') + colored_image = cv2.cvtColor(colored_image, cv2.COLOR_RGB2BGR) + + # result = Result('hog', hog_image_rescaled, 0) + result = Result('hog', colored_image, 0) # Display the results for i, rectangle in enumerate(dets): @@ -269,10 +343,11 @@ def process2_dnn(in_q, out_q): out_q.put(result) -def process3_haar(in_q, out_q): +def process3_haar(in_q, out_q, cascade_file): from cffi import FFI from PIL import Image import cv2 + import os logger = logging.getLogger('haar') @@ -281,11 +356,12 @@ def process3_haar(in_q, out_q): int test(int); typedef void* haarclassifier; - haarclassifier classifier_new(); + haarclassifier classifier_new(char *filename); void scan_image(haarclassifier, size_t width,size_t height, char *input, char *buffer, size_t length, bool debug); """) - C = ffi.dlopen("/home/ruben/Documents/Projecten/2020/rust/testproject/target/debug/libvisual_haarcascades_lib.so") + dir_path = os.path.dirname(os.path.realpath(__file__)) + C = ffi.dlopen(os.path.join(dir_path,"../visualhaar/target/debug/libvisual_haarcascades_lib.so")) # print(C.test(9)) # i = Image.open("Marjo.jpg") @@ -293,11 +369,14 @@ def process3_haar(in_q, out_q): # height = i.size[0] # use the rust lib to draw the visualisation - haar = C.classifier_new() + + filename = cascade_file.encode('ascii') + fn = ffi.new("char[]", filename) + haar = C.classifier_new(fn) logger.info("Initialised haar classifier") # opencv for the actual detections - faceCascade = cv2.CascadeClassifier('./haarcascade_frontalface_alt2.xml') + faceCascade = cv2.CascadeClassifier(cascade_file) while True: frame = in_q.get() @@ -358,12 +437,16 @@ def process3_haar(in_q, out_q): # print(img) out_q.put(result) -def display(image_res, q1, q2, q3, q4): +def display(image_res, q1, q2, q3, q4, fullscreen = False): prev_image1 = np.zeros((image_res[1],image_res[0],3), np.uint8) prev_image2 = np.zeros((image_res[1],image_res[0],3), np.uint8) prev_image3 = np.zeros((image_res[1],image_res[0],3), np.uint8) prev_image4 = np.zeros((image_res[1],image_res[0],3), np.uint8) + if fullscreen: + cv2.namedWindow("output", cv2.WND_PROP_FULLSCREEN) + cv2.setWindowProperty("output",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN) + while True: logging.debug('r') try: @@ -403,14 +486,27 @@ def display(image_res, q1, q2, q3, q4): img_concate_Verti1 = np.concatenate((image1,image2),axis=0) img_concate_Verti2 = np.concatenate((image3,image4),axis=0) grid_img = np.concatenate((img_concate_Verti1,img_concate_Verti2),axis=1) - cv2.imshow("Output", grid_img) + cv2.imshow("output", grid_img) # Hit 'q' on the keyboard to quit! - if cv2.waitKey(1) & 0xFF == ord('q'): + key = cv2.waitKey(1) & 0xFF + if key == ord('q'): break + if key == ord(' '): + # TODO save frame + pass -def main(camera_id): +def main(camera_id, rotate, fullscreen, cascade_file): image_size = (int(1920/2), int(1080/2)) + + if not os.path.exists(cascade_file): + raise RuntimeError(f"Cannot load OpenCV haar-cascade file '{cascade_file}'") + + is_rotated_90 = rotate in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE] + + if is_rotated_90: + image_size = (image_size[1], image_size[0]) + # TODO should we use queues here at all? # https://docs.python.org/3/library/multiprocessing.html#programming-guidelines # TODO: queue maxsize, or prefrabily some sort of throttled queue (like zmq hight water mark) @@ -422,11 +518,11 @@ def main(camera_id): q_process2 = Queue(maxsize=1) q_process3 = Queue(maxsize=1) - p1 = Process(target=record, args=(camera_id, q_webcam1, q_webcam2,q_webcam3,q_webcam4)) - p2 = Process(target=display, args=(image_size, q_webcam1, q_process1, q_process2, q_process3 )) + p1 = Process(target=record, args=(camera_id, q_webcam1, q_webcam2,q_webcam3,q_webcam4, image_size, rotate)) + p2 = Process(target=display, args=(image_size, q_webcam1, q_process1, q_process2, q_process3, fullscreen )) p3 = Process(target=process1_hog, args=(q_webcam2, q_process1,)) p4 = Process(target=process2_dnn, args=(q_webcam3, q_process2,)) - p5 = Process(target=process3_haar, args=(q_webcam4, q_process3,)) + p5 = Process(target=process3_haar, args=(q_webcam4, q_process3,cascade_file)) p1.start() p2.start() diff --git a/mirror.py b/mirror.py index 30a3828..769d3c3 100644 --- a/mirror.py +++ b/mirror.py @@ -1,10 +1,27 @@ import argparse import face_recognition.comparison +import cv2 + if __name__ == '__main__': parser = argparse.ArgumentParser(description='Visualise face recognition algorithms.') parser.add_argument('--camera', '-c', type=int, default=0, help='Numeric id of the camera') + parser.add_argument('--fullscreen', '-f', action='store_true', + help='Display output full screen') + parser.add_argument('--clockwise', action='store_true', + help='Rotate clockwise') + parser.add_argument('--counter-clockwise', action='store_true', + help='Rotate counter clockwise') + parser.add_argument('--cascade', default='haarcascade_frontalface_alt2.xml', + help='Cascade XML file to use (opencv format)') args = parser.parse_args() - face_recognition.comparison.main(args.camera) + + rotate = None + if args.clockwise: + rotate = cv2.ROTATE_90_CLOCKWISE + if args.counter_clockwise: + rotate = cv2.ROTATE_90_COUNTERCLOCKWISE + + face_recognition.comparison.main(args.camera, rotate, args.fullscreen, args.cascade) diff --git a/test_rust.py b/test_rust.py index 0c1a5c5..7416fb5 100644 --- a/test_rust.py +++ b/test_rust.py @@ -8,11 +8,12 @@ ffi.cdef(""" int test(int); typedef void* haarclassifier; - haarclassifier classifier_new(); + haarclassifier classifier_new(char *filename); void scan_image(haarclassifier, size_t width,size_t height, char *input, char *buffer, size_t length, bool debug); """) -C = ffi.dlopen("/home/ruben/Documents/Projecten/2020/rust/testproject/target/debug/libvisual_haarcascades_lib.so") +# C = ffi.dlopen("/home/ruben/Documents/Projecten/2020/rust/testproject/target/debug/libvisual_haarcascades_lib.so") +C = ffi.dlopen("visualhaar/target/debug/libvisual_haarcascades_lib.so") print(C.test(9)) # i = Image.open("/home/ruben/Documents/Projecten/2020/rust/lena_orig.png") @@ -41,7 +42,13 @@ while True: # buffer2 = ffi.from_buffer("char[]", (i.tobytes("raw","RGB"))) buffer2 = ffi.from_buffer("char[]", image.tobytes()) - haar = C.classifier_new() + + filename = "/home/ruben/Documents/Projecten/2020/rust/testproject/haarcascade_frontalface_alt2.xml".encode('ascii') + fn = ffi.new("char[]", filename) + # fn = ffi.string(filename) + + print("Initialise...") + haar = C.classifier_new(fn) # i = Image.open("/home/ruben/Documents/Projecten/2020/rust/lena_orig.png") # data = i.tobytes("raw", "RGB") diff --git a/visualhaar b/visualhaar new file mode 160000 index 0000000..9621bdc --- /dev/null +++ b/visualhaar @@ -0,0 +1 @@ +Subproject commit 9621bdc934b9a16e883d763e05de9d84d424c639