Crisper output and many configuration to cli arguments

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "visualhaar"]
+	path = visualhaar
+	url = git@git.rubenvandeven.com:r/visualhaar.git

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
+
+
+            # 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', 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()

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)

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")
 

visualhaar

@@ -0,0 +1 @@
+Subproject commit 9621bdc934b9a16e883d763e05de9d84d424c639