upgraded to opencv-contrib-python>=4.12 and added EDColor, EDLines and EDEllipses

.gitignore

@@ -1,3 +1,4 @@
+.venv
 venv
 flagged
 

app.py

@@ -1,52 +1,220 @@
-import gradio as gr
-import cv2
+import glob
 import os
 
-ed     = cv2.ximgproc.createEdgeDrawing()
-params = cv2.ximgproc.EdgeDrawing.Params()
-params.PFmode = True
-ed.setParams(params)
+import cv2
+import gradio as gr
+
 
 def center_crop_mod64(img):
-    h, w = img.shape[:2]
-    shortside, longside = min(w, h), max(w, h)
-    longside_crop = (longside // 64) * 64
-    left   = (w - longside_crop) // 2 if w > h else 0
-    top    = (h - longside_crop) // 2 if h > w else 0
-    right  = left + longside_crop if w > h else shortside
-    bottom = top  + longside_crop if h > w else shortside
-    return img[top:bottom, left:right]
-    
-def edpf(image_rgb):
-    img_gray = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2GRAY)
-    img_crop = center_crop_mod64(img_gray)
-    edges    = ed.detectEdges(img_crop)
-    edge_map = ed.getEdgeImage(edges)
-    return edge_map
-
-examples=[
-        os.path.join(os.path.dirname(__file__), "images/bag.png"),
-        os.path.join(os.path.dirname(__file__), "images/beard.png"),
-        os.path.join(os.path.dirname(__file__), "images/bird.png"),
-        os.path.join(os.path.dirname(__file__), "images/cat.png"),
-        os.path.join(os.path.dirname(__file__), "images/dog2.png"),
-        os.path.join(os.path.dirname(__file__), "images/house.png"),
-        os.path.join(os.path.dirname(__file__), "images/house2.png"),
-        os.path.join(os.path.dirname(__file__), "images/human.png"),
-        os.path.join(os.path.dirname(__file__), "images/kitten.png"),
-        os.path.join(os.path.dirname(__file__), "images/lion.png"),
-        os.path.join(os.path.dirname(__file__), "images/man.png"),
-        os.path.join(os.path.dirname(__file__), "images/robot.png"),
-        os.path.join(os.path.dirname(__file__), "images/robotics.png"),
-        os.path.join(os.path.dirname(__file__), "images/room.png"),
-        os.path.join(os.path.dirname(__file__), "images/room2.png"),
-        os.path.join(os.path.dirname(__file__), "images/suit.png"),
-        os.path.join(os.path.dirname(__file__), "images/tree.png"),
-        os.path.join(os.path.dirname(__file__), "images/vermeer.png"),
-    ]
-    
-app = gr.Interface(fn=edpf, inputs=gr.Image(value="images/dog2.png"), outputs="image", title="Edge Drawing Parameter Free", description="A modern edge detection algorithm which requires no parameter tuning. Generate edge maps for the edpf ControlNet model at https://huggingface.co/GeroldMeisinger/control-edgedrawing", examples=examples)
+	"""Crop image to largest area that's divisible by 64"""
+	h, w	= img.shape[:2]
+	shortside, longside	= min(w, h), max(w, h)
+	longside_crop	= (longside // 64) * 64
 
-if __name__ == "__main__":
-    app.launch()
+	left	= (w - longside_crop) // 2	if w > h else 0
+	top	= (h - longside_crop) // 2	if h > w else 0
+	right	= left + longside_crop	if w > h else shortside
+	bottom	= top  + longside_crop	if h > w else shortside
+
+	return img[top:bottom, left:right]
+
+def load_example_images():
+	"""Load all images from images/ directory"""
+	image_dir = os.path.join(os.path.dirname(__file__), "images")
+	if not os.path.exists(image_dir):
+		return []
+
+	extensions = ['*.png', '*.jpg', '*.jpeg', '*.webp']
+	examples = []
+	for ext in extensions:
+		examples.extend(glob.glob(os.path.join(image_dir, ext)))
+		examples.extend(glob.glob(os.path.join(image_dir, ext.upper())))
+
+	return sorted(examples)
+
+def apply_edge_detection(image_rgb, algorithm, apply_crop, convert2grayscale, params):
+	"""Apply selected edge detection algorithm with specified parameters"""
+
+	if image_rgb is None:
+		return None
+
+	# Apply cropping if requested
+	if apply_crop:
+		img_processed = center_crop_mod64(image_rgb)
+	else:
+		img_processed = image_rgb.copy()
+
+	# Convert to grayscale if requested
+	if convert2grayscale and len(img_processed.shape) == 3:
+		img_input = cv2.cvtColor(cv2.cvtColor(img_processed, cv2.COLOR_RGB2GRAY), cv2.COLOR_GRAY2RGB)
+	else:
+		img_input = img_processed
+	ret = img_input.copy()
+
+	# set parameters
+
+
+	# Detect edges
+	ed = cv2.ximgproc.createEdgeDrawing()
+	ed.setParams(params)
+	ed.detectEdges(img_input)
+
+	if algorithm == "Edges":
+		ret = ed.getEdgeImage()
+		return ret
+
+	elif algorithm == "Lines":
+		lines = ed.detectLines()
+
+		# draw lines
+		if lines is not None:
+			for line in lines:
+				x1, y1, x2, y2 = map(int, line[0])
+				cv2.line(ret, (x1, y1), (x2, y2), (0, 255, 0), 1)
+
+		return ret
+
+	elif algorithm == "Ellipses":
+		ellipsess = ed.detectEllipses()
+
+		# draw ellipses
+		if ellipsess is not None:
+			for ellipses in ellipsess:
+				ellipse = ellipses[0]
+				center = (int(ellipse[0]), int(ellipse[1]))
+				if ellipse[2] == 0: # Ellipse
+					axes = (int(ellipse[3]), int(ellipse[4]))
+					angle = ellipse[5]
+					cv2.ellipse(ret, center, axes, angle, 0, 360, (0, 255, 0), 1)
+				else: # Circle
+					radius = int(ellipse[2])
+					cv2.circle(ret, center, radius, (0, 255, 0), 1)
 
+		return ret
+
+	return img_input
+
+# Create Gradio interface
+with gr.Blocks(title="Edge Drawing") as app:
+	examples = load_example_images()
+	gr.Markdown("# Enhanced Edge Detection with [Edge Drawing](https://github.com/CihanTopal/ED_Lib) using [opencv-contrib-python](https://docs.opencv.org/4.x/d1/d1c/classcv_1_1ximgproc_1_1EdgeDrawing.html)")
+
+	# Image row - Input and Output side by side
+	with gr.Row():
+		input_image = gr.Image(value=examples[0] if examples else None , label="Input Image" , type="numpy")
+		output_image = gr.Image(value=None , label="Detection Result" , type="numpy")
+
+	# Controls row - Processing Options on left, Parameters on right
+	with gr.Row():
+		# Processing Options
+		with gr.Column():
+			with gr.Group():
+				gr.Markdown("### Processing Options")
+				algorithm_radio	= gr.Radio(label="Detection mode", value="Edges", choices=["Edges", "Lines", "Ellipses"])
+
+				paramterfree_checkbox	= gr.Checkbox(label	="Parameter Free Mode"	, value=True	, info="Auto-determine optimal parameters")
+				convert2grayscale_checkbox	= gr.Checkbox(label	="Convert to Grayscale"	, value=False	, info="Force conversion to grayscale prior to edge detection (note that Edge Drawing has native support for color images)")
+				crop_checkbox	= gr.Checkbox(label	="Apply center crop mod-64"	, value=False	, info="Crop to largest area divisible by 64 (used for Stable Diffusion)")
+
+		# EdgeDrawing Parameters
+		with gr.Column():
+			with gr.Group():
+				gr.Markdown("### Edge Drawing Parameters")
+
+				# Parameter controls group (disabled by default)
+				with gr.Group(visible=False) as param_group:
+					with gr.Column():
+						with gr.Row():
+							gradient_operator = gr.Radio(choices=["PREWITT", "SOBEL", "SCHARR", "LSD"], value="PREWITT", label="Gradient Operator", info="indicates the operator used for gradient calculation. Default value is PREWITT")
+							gradient_threshold	= gr.Slider(minimum=	1	, maximum=	100	, value=	20	, step=	1	, label="Gradient Threshold"	, scale=  1	, info="Threshold value of gradiential difference between pixels. Used to create gradient image. Default value is 20"	)
+
+						with gr.Row():
+							anchor_threshold	= gr.Slider(minimum=	0	, maximum=	255	, value=	0	, step=	1	, label="Anchor Threshold"	, scale=  1	, info="Threshold value used to select anchor points. Default value is 0"	)
+							min_path_length	= gr.Slider(minimum=	1	, maximum=	50	, value=	10	, step=	1	, label="Min Path Length"	, scale=  1	, info="Minimum connected pixels length processed to create an edge segment. Default value is 10"	)
+
+						with gr.Row():
+							min_line_length	= gr.Slider(minimum=	-1	, maximum=	100	, value=	-1	, step=	1	, label="Min Line Length"	, scale=  1	, info="Minimum line length to detect. Default value is -1"	)
+							line_fit_error_threshold	= gr.Slider(minimum=	0.1	, maximum=	10.0	, value=	1.0	, step=	0.1	, label="Line Fit Error Threshold"	, scale=  1	, info="Default value is 1.0"	)
+							max_distance_between_two_lines	= gr.Slider(minimum=	1.0	, maximum=	20.0	, value=	6.0	, step=	0.1	, label="Max Distance Between Two Lines"	, scale=  1	, info="Default value is 6.0"	)
+
+						with gr.Row():
+							max_error_threshold	= gr.Slider(minimum=	0.1	, maximum=	5.0	, value=	1.3	, step=	0.1	, label="Max Error Threshold"	, scale=  1	, info="Default value is 1.3"	)
+							scan_interval	= gr.Slider(minimum=	1	, maximum=	10	, value=	1	, step=	1	, label="Scan Interval"	, scale=  1	, info="Default value is 1"	)
+
+						with gr.Row():
+							sigma	= gr.Slider(minimum=	0.1	, maximum=	5.0	, value=	1.0	, step=	0.1	, label="Sigma"	, scale=  1	, info="Sigma value for internal GaussianBlur() function. Default value is 1.0"	)
+							nfa_validation	= gr.Checkbox(label="NFA Validation"	, value=True	, info="Indicates if NFA (Number of False Alarms) algorithm will be used for line and ellipse validation. Default value is true")
+							sum_flag	= gr.Checkbox(label="Sum Flag"	, value=True	, info="Default value is true")
+
+	# Update function
+	def update_output(
+			image_rgb, algorithm, apply_crop, conv2grayscale, pf_mode,
+			gradient_operator, anchor_threshold, gradient_threshold, min_path_length, min_line_length, line_fit_error_threshold, max_distance_between_two_lines, max_error_threshold, nfa_validation, scan_interval, sigma, sum_flag
+		):
+		operator_map = {
+			"PREWITT"	: cv2.ximgproc.EdgeDrawing_PREWITT,
+			"SOBEL"	: cv2.ximgproc.EdgeDrawing_SOBEL,
+			"SCHARR"	: cv2.ximgproc.EdgeDrawing_SCHARR,
+			"LSD"	: cv2.ximgproc.EdgeDrawing_LSD,
+		}
+
+		params = cv2.ximgproc.EdgeDrawing.Params()
+		params.PFmode = pf_mode
+		params.EdgeDetectionOperator = operator_map.get(gradient_operator, cv2.ximgproc.EdgeDrawing_PREWITT)
+		if not pf_mode:
+			params.AnchorThresholdValue	= anchor_threshold
+			params.GradientThresholdValue	= gradient_threshold
+			params.MinPathLength	= min_path_length
+			params.MinLineLength	= min_line_length
+			params.LineFitErrorThreshold	= line_fit_error_threshold
+			params.MaxDistanceBetweenTwoLines	= max_distance_between_two_lines
+			params.MaxErrorThreshold	= max_error_threshold
+			params.NFAValidation	= nfa_validation
+			params.ScanInterval	= scan_interval
+			params.Sigma	= sigma
+			params.SumFlag	= sum_flag
+
+		ret = apply_edge_detection(image_rgb, algorithm, apply_crop, conv2grayscale, params)
+		return ret
+
+	# Function to toggle parameter group visibility
+	def toggle_params(pf_mode):
+		return gr.Group(visible=not pf_mode)
+
+	# Set up event handlers
+	inputs = [
+		input_image,
+		algorithm_radio,
+		crop_checkbox,
+		convert2grayscale_checkbox,
+		paramterfree_checkbox,
+		gradient_operator,
+		anchor_threshold,
+		gradient_threshold,
+		min_path_length,
+		min_line_length,
+		line_fit_error_threshold,
+		max_distance_between_two_lines,
+		max_error_threshold,
+		nfa_validation,
+		scan_interval,
+		sigma,
+		sum_flag,
+	]
+
+	# Update output when any input changes
+	for inp in inputs:
+		inp.change(fn=update_output, inputs=inputs, outputs=output_image)
+
+	# Toggle parameter group when parameter free mode changes
+	paramterfree_checkbox.change(fn=toggle_params, inputs=paramterfree_checkbox, outputs=param_group)
+
+	# Apply edge detection immediately on startup
+	app.load(fn=update_output, inputs=inputs, outputs=output_image)
+
+	# Examples
+	if examples:
+		gr.Examples(examples=examples, inputs=input_image, label="Example Images" )
+
+if __name__ == "__main__":
+	app.launch()

requirements.txt

@@ -1,2 +1,2 @@
-gradio==3.44.4
-opencv-contrib-python==4.8.0.76
+gradio==5.39.0
+opencv-contrib-python>=4.12