Windows pyinstaller compatibilities

face_recognition/comparison.py

@@ -229,7 +229,7 @@ def process1_hog(in_q, out_q):
     face_detector = dlib.get_frontal_face_detector()
 
     visualisation_factor = 1
-    detection_factor = .4
+    detection_factor = .3
 
 
     process_this_frame = True
@@ -239,7 +239,7 @@ def process1_hog(in_q, out_q):
             frame = in_q.get()
 
             frame = cv2.cvtColor(src=frame, code=cv2.COLOR_BGR2GRAY)
-            viz_frame = cv2.resize(frame, (0, 0), fx=visualisation_factor, fy=visualisation_factor)
+            # viz_frame = cv2.resize(frame, (0, 0), fx=visualisation_factor, fy=visualisation_factor)
             det_frame = cv2.resize(frame, (0, 0), fx=detection_factor, fy=detection_factor)
 
             start = time.time()
@@ -371,16 +371,20 @@ def process3_haar(in_q, out_q, cascade_file, library_filename = None):
         dir_path = os.path.dirname(os.path.realpath(__file__))
         
         lib_path = os.path.join(dir_path, "..", "visualhaar", "target", "release")
-        so_path = os.path.join(lib_path, "libvisual_haarcascades_lib.so")
+        possible_paths = [
-        dll_path = os.path.join(lib_path, "visual_haarcascades_lib.dll")
+            os.path.join(lib_path, "libvisual_haarcascades_lib.so"),
+            os.path.join(lib_path, "visual_haarcascades_lib.dll"),
+            os.path.join(dir_path, "..", "visual_haarcascades_lib.dll"),
+        ]
 
-        if os.path.exists(so_path):
+        existing_paths = [p for p in possible_paths if os.path.exists(p)]
-            C = ffi.dlopen(so_path)
+
-        elif os.path.exists(dll_path):
+        if not len(existing_paths):
-            C = ffi.dlopen(dll_path)
-        else:
             raise RuntimeError("Visual haarcascades library is not found")
-
+        
+        logger.debug(f"Using library: {existing_paths[0]}")
+        C = ffi.dlopen(existing_paths[0])
+        
     # print(C.test(9))
     # i = Image.open("Marjo.jpg")
     # width = i.size[0]

face_recognition/tools.py

@@ -0,0 +1,32 @@
+ from PIL import ImageFont, ImageDraw, Image  
+ import cv2  
+ import numpy as np  
+   
+ text_to_show = "The quick brown fox jumps over the lazy dog"  
+   
+ # Load image in OpenCV  
+ image = cv2.imread("Me.jpg")  
+   
+ # Convert the image to RGB (OpenCV uses BGR)  
+ cv2_im_rgb = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)  
+   
+ # Pass the image to PIL  
+ pil_im = Image.fromarray(cv2_im_rgb)  
+   
+ draw = ImageDraw.Draw(pil_im)  
+   
+ # Draw the text  
+ draw.text((10, 700), text_to_show, font=font)  
+   
+ # Get back the image to OpenCV  
+ cv2_im_processed = cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)  
+   
+ cv2.imshow('Fonts', cv2_im_processed)  
+ cv2.waitKey(0)  
+   
+ cv2.destroyAllWindows()
+
+def get_font(filename, size):
+    return ImageFont.truetype(filename, size)
+
+ def draw_text(img, ):

mirror.py

@@ -1,9 +1,11 @@
 import argparse
 import face_recognition.comparison
 import cv2
-
+from multiprocessing import freeze_support
 
 if __name__ == '__main__':
+    freeze_support() # support pyinstaller on Windows
+    
     parser = argparse.ArgumentParser(description='Visualise face recognition algorithms.')
     parser.add_argument('--camera', '-c', type=int, default=0,
                         help='Numeric id of the camera')